From 3672335bed5afe0d5e4e1d9bb86afdb675018893 Mon Sep 17 00:00:00 2001 From: mprammer Date: Tue, 12 May 2026 16:41:07 -0400 Subject: [PATCH 01/38] release 0.1.4: catalog discoverability bundle MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Faceted TUI side panel, view presets, per-column profiles, domain tags + showcase tiers, and matching CLI flags so newcomers can find "interesting data" without scrolling docs/datasets.md. * discovery.py: closed vocabs (12 tags, 4 tiers, 5 size buckets, 6 trait flags), FilterState dataclass + apply_preset, view-preset registry, bucket_for_size, _is_variant_field, sparkline, format_column_line. Shared by browse, list_datasets, validate_manifest, docs, profile. * sources.schema.json: optional tags + showcase on DatasetSpec (default [], backwards-compatible). validate_manifest cross-checks the closed vocabs and warns on empty tiers only when at least one tier is populated (so --strict stays clean on the uncurated manifest). * profile.schema.json + scripts/pipeline/profile.py: new opt-in stage `python -m scripts.pipeline.profile ` writes outputs/v1//profile.json with per-dtype stats — numeric histogram + NDV; string NDV + top-5 (NDV <= 256, skipped on binary); bool T/F/null; date/timestamp 10-bucket histogram; list/map length stats; struct/variant emit null. Idempotent against parquet sha256. * docs.py: _snapshot_for_slug now writes size_bucket + shape_traits per slug; high_cardinality_present backfilled from profile.json when present. _render_curated_picks prepends a tier-grouped block to datasets.md. * list_datasets.py: --tag / --showcase / --size / --trait (with ! negation) / --view / --inspect / --tags-help / --showcase-help. Reuses FilterState. * browse.py: left-docked facet panel (7 SelectionList groups + tri-state RadioSet for shape traits); view-preset bar with keys 1-4; new sortable Tags / Showcase / Size columns; "N of M" counts header; right detail-pane Columns section with sparklines (lazy-loaded profile.json, cached). C clears facets; f focuses panel. * text_whitespace_parse: per-column int/float type inference so uci-seeds emits numerics for the profile stage to histogram against. * README: replaces "Pick any other dataset" with a Discover subsection. * Skills: raincloud-profile, raincloud-discover (new); raincloud-list- datasets, raincloud-build (updated). Curation (~249 tags + showcase entries) is a separate follow-up; the scaffolding degrades gracefully empty (preset views show 0 hits, curated-picks blocks read "No picks yet"). Co-Authored-By: Claude Signed-off-by: mprammer --- .agents/skills/raincloud-build/SKILL.md | 6 + .agents/skills/raincloud-discover/SKILL.md | 37 ++ .../skills/raincloud-list-datasets/SKILL.md | 4 + .agents/skills/raincloud-profile/SKILL.md | 36 ++ CHANGELOG.md | 39 ++ README.md | 16 +- profile.schema.json | 120 +++++ pyproject.toml | 2 +- scripts/pipeline/browse.py | 440 ++++++++++++++- scripts/pipeline/discovery.py | 239 +++++++++ scripts/pipeline/docs.py | 174 +++++- .../handlers/text_whitespace_parse.py | 39 +- scripts/pipeline/list_datasets.py | 179 ++++++- scripts/pipeline/profile.py | 505 ++++++++++++++++++ scripts/pipeline/validate_manifest.py | 49 ++ sources.schema.json | 25 + sources.schema.md | 20 +- tests/fixtures/profile_uci_seeds.json | 290 ++++++++++ tests/test_browse.py | 126 ++++- tests/test_discovery.py | 164 ++++++ tests/test_docs.py | 169 ++++++ tests/test_list_datasets.py | 224 ++++++++ tests/test_manifest.py | 85 +++ tests/test_profile.py | 284 ++++++++++ 24 files changed, 3239 insertions(+), 33 deletions(-) create mode 100644 .agents/skills/raincloud-discover/SKILL.md create mode 100644 .agents/skills/raincloud-profile/SKILL.md create mode 100644 profile.schema.json create mode 100644 scripts/pipeline/discovery.py create mode 100644 scripts/pipeline/profile.py create mode 100644 tests/fixtures/profile_uci_seeds.json create mode 100644 tests/test_discovery.py create mode 100644 tests/test_profile.py diff --git a/.agents/skills/raincloud-build/SKILL.md b/.agents/skills/raincloud-build/SKILL.md index 4cd2006..c368d2f 100644 --- a/.agents/skills/raincloud-build/SKILL.md +++ b/.agents/skills/raincloud-build/SKILL.md @@ -29,3 +29,9 @@ Before running: After a successful build, suggest running `/raincloud-docs` to regenerate derived docs. Context: [SKILLS.md](../../context/SKILLS.md), [AGENTS.md](../../context/AGENTS.md). + +## Note (0.1.4) + +Per-column profiles are a separate opt-in stage: +`python -m scripts.pipeline.profile ` after a build. Not part of the +default build pipeline. See `raincloud-profile` skill. diff --git a/.agents/skills/raincloud-discover/SKILL.md b/.agents/skills/raincloud-discover/SKILL.md new file mode 100644 index 0000000..a1e880a --- /dev/null +++ b/.agents/skills/raincloud-discover/SKILL.md @@ -0,0 +1,37 @@ +--- +name: raincloud-discover +description: Use when the user wants to find "interesting" datasets — exposes the new tag / showcase / size / trait / view filters on list_datasets. +--- + +# raincloud-discover + +Wraps `python -m scripts.pipeline.list_datasets` with the discoverability flags. The TUI (`python -m scripts.pipeline.browse`) is the interactive sibling. + +## Closed vocab + +- **Showcase tiers** (4): `start-here`, `encoding-research`, `vortex-wins`, `stress-test`. Run `--showcase-help` for live counts. +- **Domain tags** (12): `geospatial`, `nlp-text`, `web-analytics`, `e-commerce`, `finance`, `social`, `scientific`, `healthcare`, `sports`, `transportation`, `government`, `benchmark`. Run `--tags-help` for live counts. +- **Size buckets** (5): `xs / s / m / l / xl` (file-size on disk). +- **Trait flags** (6): `has_nested`, `has_timestamp`, `has_variant`, `string_heavy`, `wide_row`, `high_cardinality_present`. Prefix with `!` to negate. + +## Patterns + +```bash +python -m scripts.pipeline.list_datasets --view start-here --long +python -m scripts.pipeline.list_datasets --tag geospatial --tag scientific +python -m scripts.pipeline.list_datasets --trait has_nested --size m +python -m scripts.pipeline.list_datasets --trait '!has_nested' --vortex +python -m scripts.pipeline.list_datasets --inspect clickbench-hits +python -m scripts.pipeline.list_datasets --tags-help +python -m scripts.pipeline.list_datasets --showcase-help +``` + +Filters AND across axes, OR within an axis. `--view` replaces other facet flags (preset is the entire facet spec). + +## When to invoke + +- "Find me datasets with X property" → use traits + tags + size filters +- "What's good for testing nested-type encoding?" → `--trait has_nested --vortex` +- "Show me a small dataset to iterate on" → `--size xs --size s` +- "Inspect this slug" → `--inspect ` +- "What tags / tiers exist?" → `--tags-help` / `--showcase-help` diff --git a/.agents/skills/raincloud-list-datasets/SKILL.md b/.agents/skills/raincloud-list-datasets/SKILL.md index da67a0d..0394860 100644 --- a/.agents/skills/raincloud-list-datasets/SKILL.md +++ b/.agents/skills/raincloud-list-datasets/SKILL.md @@ -51,3 +51,7 @@ python -m scripts.pipeline.list_datasets --grep '\bgeo' --long Pair with `/raincloud-status ` to check filesystem state of any returned slug, and `/raincloud-validate-manifest` after editing the manifest based on findings. Context: [SKILLS.md](../../context/SKILLS.md), [sources.schema.md](../../context/sources.schema.md), [`docs/v1/datasets.md`](../../../docs/v1/datasets.md) for full-row metadata. + +## New discovery axes (0.1.4) + +`--showcase`, `--tag`, `--size`, `--trait`, `--view`, `--inspect`, `--tags-help`, `--showcase-help`. See the `raincloud-discover` skill for the full vocabulary and patterns. diff --git a/.agents/skills/raincloud-profile/SKILL.md b/.agents/skills/raincloud-profile/SKILL.md new file mode 100644 index 0000000..143b339 --- /dev/null +++ b/.agents/skills/raincloud-profile/SKILL.md @@ -0,0 +1,36 @@ +--- +name: raincloud-profile +description: Use when the user asks to compute or refresh per-column statistics for a raincloud dataset — produces `outputs/v1//profile.json` for the TUI's detail pane and `list_datasets --inspect`. +--- + +# raincloud-profile + +Wraps `python -m scripts.pipeline.profile`. Opt-in stage; off the default +build path. Idempotent against parquet sha256. + +## When to invoke + +- "Generate profiles for X / for everything that's built" +- "Refresh the profile after I rebuilt slug X" +- "Show me per-column statistics" → run this first, then `list_datasets --inspect ` + +## Patterns + +```bash +python -m scripts.pipeline.profile # one +python -m scripts.pipeline.profile --all # every built parquet +python -m scripts.pipeline.profile --sample-rows 1000000 # cap for huge slugs +``` + +Per-dtype stats: +- **Numeric**: histogram + NDV + min/max/mean +- **String / binary**: NDV; top-5 when NDV ≤ 256 (binary skips top-5 — bytes don't render usefully) +- **Bool**: T/F/null counts +- **Date/Timestamp**: range + 10-bucket histogram +- **List/Map**: length min/max/mean +- **Struct / variant**: skipped (emits null at column-map level) + +Profiles live at `outputs/v1//profile.json` and are read by: +- The TUI's right-pane Columns section (`python -m scripts.pipeline.browse`) +- The CLI's `--inspect ` rendering +- `docs.py` for backfilling `shape_traits.high_cardinality_present` into `snapshot.json` diff --git a/CHANGELOG.md b/CHANGELOG.md index a733dd0..da30ad3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,45 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.1.4] - 2026-05-12 + +### Added + +- **Catalog discoverability** — newcomers can now find "interesting" + datasets without scrolling the 158 KB `datasets.md`. +- **TUI faceted side panel** (`browse.py`) — filter groups for showcase, + domain tags, size, shape traits, license, family, fetch type, vortex. + View-preset bar (`Start Here`, `Encoding Research`, `Vortex Wins`, + `Stress Test`) on top, accessible via keys `1`–`4`. Counts header shows + `N of 249`. +- **Per-column profiles** — new opt-in stage + `python -m scripts.pipeline.profile []` produces + `outputs/v1//profile.json` with per-dtype stats (numeric + histograms, string NDV + top-K, bool T/F/null, date/timestamp ranges, + list/map length stats). Surfaced in the TUI's detail pane and via + `list_datasets --inspect `. +- **Editorial metadata** in `sources.json` — optional `tags` (closed + vocab, 12 entries) and `showcase` (closed vocab, 4 tiers) per + `DatasetSpec`. Empty by default; curation pass lands separately. +- **Derived signals** in `docs/snapshot.json` — per-slug `shape_traits` + (has_nested, has_timestamp, has_variant, string_heavy, wide_row, + high_cardinality_present) and `size_bucket` (xs/s/m/l/xl), derived by + `docs.py` from on-disk parquets. +- **CLI parity** — `list_datasets` gains `--tag`, `--showcase`, `--size`, + `--trait` (with `!` negation), `--view`, `--inspect`, `--tags-help`, + `--showcase-help`. +- **Curated-picks header** in `docs/v1/datasets.md` — one block per + showcase tier, regenerated from `sources.json`. +- **README "Discover" subsection** — directs newcomers at the TUI first. +- **Skills**: new `raincloud-profile`, new `raincloud-discover`; updated + `raincloud-list-datasets`, `raincloud-build`. + +### Schema + +- `sources.schema.json` adds optional `tags` and `showcase` to + `DatasetSpec` (defaults to `[]`; backwards-compatible read). +- New `profile.schema.json` (Draft 2020-12) for per-slug profile output. + ## [0.1.3] - 2026-05-10 ### Changed diff --git a/README.md b/README.md index 0c2d595..79f87ef 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,21 @@ outputs/v1/countries-of-the-world/vortex/countries-of-the-world.vortex The command runs every pipeline stage — fetch, extract, parse, transform, write, validate, convert — and leaves both a Parquet file and its converted Vortex sibling under per-format subdirectories of `outputs/v1//`. -**Pick any other dataset from [`docs/v1/datasets.md`](docs/v1/datasets.md)** (249 curated) and pass its slug the same way. Examples spanning the size range: +### Discover + +Run `python -m scripts.pipeline.browse` and start with **Start Here**. The left panel filters by domain, size, shape traits, license, or family; click a slug to see its description, on-disk state, and per-column profile (run `python -m scripts.pipeline.profile ` first to populate it). + +Headless? The same axes are flags on `list_datasets`: + +```bash +python -m scripts.pipeline.list_datasets --view start-here --long +python -m scripts.pipeline.list_datasets --tag geospatial +python -m scripts.pipeline.list_datasets --trait has_nested --size m +python -m scripts.pipeline.list_datasets --inspect clickbench-hits +python -m scripts.pipeline.list_datasets --tags-help +``` + +The curated-picks header at the top of [`docs/v1/datasets.md`](docs/v1/datasets.md) groups slugs into four editorial tiers (Start Here, Encoding Research, Vortex Wins, Stress Test) drawn from each spec's `showcase` field — pick any slug and pass it to `build` the same way. Examples spanning the size range, all members of the **Start Here** tier: ```bash python -m scripts.pipeline.build uci-seeds # 210 rows, ~200 ms diff --git a/profile.schema.json b/profile.schema.json new file mode 100644 index 0000000..6c9cc3c --- /dev/null +++ b/profile.schema.json @@ -0,0 +1,120 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://raincloud.dev/profile.schema.json", + "title": "Raincloud per-column profile", + "description": "Schema for outputs/v{n}//profile.json, produced by `python -m scripts.pipeline.profile`. Source of truth for the dtype tags and stat menus lives in scripts/pipeline/profile.py.", + "type": "object", + "required": ["schema_version", "slug", "row_count", "parquet_sha256", "computed_at", "columns"], + "additionalProperties": false, + "properties": { + "schema_version": { "type": "integer", "const": 1 }, + "slug": { "type": "string" }, + "row_count": { "type": "integer", "minimum": 0 }, + "parquet_sha256": { "type": "string", "pattern": "^[0-9a-f]{64}$" }, + "computed_at": { "type": "string", "format": "date-time" }, + "sample_rows": { "type": ["integer", "null"], "minimum": 1 }, + "columns": { + "type": "object", + "description": "Map of column name → per-dtype profile object. struct/variant entries are `null`.", + "additionalProperties": { + "oneOf": [ + { "type": "null" }, + { "$ref": "#/$defs/NumericProfile" }, + { "$ref": "#/$defs/StringProfile" }, + { "$ref": "#/$defs/BoolProfile" }, + { "$ref": "#/$defs/TemporalProfile" }, + { "$ref": "#/$defs/ListMapProfile" } + ] + } + } + }, + "$defs": { + "Histogram": { + "type": "object", + "required": ["buckets", "counts"], + "additionalProperties": false, + "properties": { + "buckets": { "type": "array", "minItems": 2 }, + "counts": { "type": "array", "items": { "type": "integer", "minimum": 0 } } + } + }, + "NumericProfile": { + "type": "object", + "required": ["dtype", "null_count", "min", "max", "mean", "ndv_approx", "histogram"], + "additionalProperties": false, + "properties": { + "dtype": { "type": "string", "pattern": "^(int|uint|float|decimal)" }, + "null_count": { "type": "integer", "minimum": 0 }, + "min": {}, + "max": {}, + "mean": { "type": "number" }, + "ndv_approx": { "type": "integer", "minimum": 0 }, + "histogram": { "$ref": "#/$defs/Histogram" } + } + }, + "StringProfile": { + "type": "object", + "required": ["dtype", "null_count", "ndv_approx", "mean_length", "top_values"], + "additionalProperties": false, + "properties": { + "dtype": { "type": "string", "enum": ["string", "binary", "large_string", "large_binary"] }, + "null_count": { "type": "integer", "minimum": 0 }, + "ndv_approx": { "type": "integer", "minimum": 0 }, + "mean_length": { "type": ["number", "null"] }, + "top_values": { + "oneOf": [ + { "type": "null" }, + { + "type": "array", + "maxItems": 5, + "items": { + "type": "object", + "required": ["value", "count"], + "additionalProperties": false, + "properties": { + "value": { "type": ["string", "null"] }, + "count": { "type": "integer", "minimum": 0 } + } + } + } + ] + } + } + }, + "BoolProfile": { + "type": "object", + "required": ["dtype", "true_count", "false_count", "null_count"], + "additionalProperties": false, + "properties": { + "dtype": { "type": "string", "const": "bool" }, + "true_count": { "type": "integer", "minimum": 0 }, + "false_count": { "type": "integer", "minimum": 0 }, + "null_count": { "type": "integer", "minimum": 0 } + } + }, + "TemporalProfile": { + "type": "object", + "required": ["dtype", "null_count", "min", "max", "histogram"], + "additionalProperties": false, + "properties": { + "dtype": { "type": "string", "pattern": "^(date|time|timestamp)" }, + "null_count": { "type": "integer", "minimum": 0 }, + "min": { "type": "string" }, + "max": { "type": "string" }, + "histogram": { "$ref": "#/$defs/Histogram" } + } + }, + "ListMapProfile": { + "type": "object", + "required": ["dtype", "null_count", "length_min", "length_max", "length_mean"], + "additionalProperties": false, + "properties": { + "dtype": { "type": "string", "pattern": "^(list|map)" }, + "null_count": { "type": "integer", "minimum": 0 }, + "length_min": { "type": "integer", "minimum": 0 }, + "length_max": { "type": "integer", "minimum": 0 }, + "length_mean": { "type": "number" } + } + } + } +} diff --git a/pyproject.toml b/pyproject.toml index f280c3a..dd7e25a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "raincloud" -version = "0.1.3" +version = "0.1.4" description = "Client-reproducible pipeline for building a curated catalog of public datasets as Parquet + Vortex files." readme = "README.md" requires-python = ">=3.11" diff --git a/scripts/pipeline/browse.py b/scripts/pipeline/browse.py index 6e389dc..534a790 100644 --- a/scripts/pipeline/browse.py +++ b/scripts/pipeline/browse.py @@ -32,9 +32,22 @@ try: from textual.app import App, ComposeResult from textual.binding import Binding - from textual.containers import Container, Horizontal, VerticalScroll + from textual.containers import Container, Horizontal, Vertical, VerticalScroll from textual.screen import ModalScreen - from textual.widgets import DataTable, Footer, Header, RichLog, Static + from textual.widgets import ( + Button, + Collapsible, + DataTable, + Footer, + Header, + Label, + RadioButton, + RadioSet, + RichLog, + SelectionList, + Static, + ) + from textual.widgets.selection_list import Selection except ImportError: print( "textual is not installed. Install with: uv sync --extra tui --inexact", @@ -42,17 +55,104 @@ ) raise SystemExit(2) +from .discovery import ( + SHOWCASE_TIERS, + SIZE_BUCKETS, + TAG_VOCAB, + TRAIT_FLAGS, + VIEW_PRESETS, + FilterState, + apply_preset, + format_column_line, +) from .list_datasets import _canonicalize_type from .spec import ( REPO_ROOT, iter_datasets, load_manifest, + outputs_root, prepared_parquet, prepared_parquet_hydrated, prepared_vortex, spec_field, ) + +def _filter_state_from_selections(selections: dict) -> FilterState: + """Build a FilterState from raw {axis_name: set-of-values} selections. + + Each axis name corresponds to a SelectionList in the facet panel. The + vortex axis is special: it's a single Optional[bool] rather than a set. + """ + state = FilterState() + for axis in ("showcase", "tag", "size", "license", "family", "fetch_type"): + getattr(state, axis).update(selections.get(axis) or set()) + v = selections.get("vortex") + if v is True or v is False: + state.vortex = v + return state + + +def _trait_state_to_filter(trait_states: dict[str, str]) -> FilterState: + """Convert {flag: "yes"|"no"|"unknown"} → FilterState fragment. + + Used by the Shape-traits facet group. "unknown" leaves the flag out of + both `trait` and `trait_negated`, i.e. don't filter on this trait. + """ + state = FilterState() + for flag, val in trait_states.items(): + if val == "yes": + state.trait.add(flag) + elif val == "no": + state.trait_negated.add(flag) + return state + + +def _combine_filters(a: FilterState, b: FilterState) -> FilterState: + """Union of two FilterStates across every axis. b takes precedence for vortex.""" + out = FilterState() + out.showcase = a.showcase | b.showcase + out.tag = a.tag | b.tag + out.size = a.size | b.size + out.trait = a.trait | b.trait + out.trait_negated = a.trait_negated | b.trait_negated + out.license = a.license | b.license + out.family = a.family | b.family + out.fetch_type = a.fetch_type | b.fetch_type + out.vortex = b.vortex if b.vortex is not None else a.vortex + return out + + +def _live_vocabs(manifest: dict) -> dict[str, list[str]]: + """Discover the values present in the live manifest for facets whose + vocab isn't closed (license, family, fetch_type).""" + licenses: set[str] = set() + families: set[str] = set() + fetch_types: set[str] = set() + for spec in manifest.get("datasets", []): + lic = (spec.get("license") or {}).get("spdx") + if lic: + licenses.add(lic) + fam = spec.get("family") + if fam: + families.add(fam) + ft = (spec.get("fetch") or {}).get("type") + if ft: + fetch_types.add(ft) + return { + "license": sorted(licenses), + "family": sorted(families), + "fetch_type": sorted(fetch_types), + } + + +def _selection_list(group_id: str, options: list[str]) -> SelectionList: + """Build a SelectionList for a facet group; nothing pre-selected.""" + return SelectionList[str]( + *[Selection(o, o) for o in options], + id=f"facet-{group_id}", + ) + COLUMNS: tuple[tuple[str, str], ...] = ( ("slug", "slug"), ("family", "family"), @@ -62,6 +162,9 @@ ("vortex", "vortex"), ("scrape", "scrape"), ("hydrate", "hydrate"), + ("Tags", "tags"), + ("Showcase", "showcase"), + ("Size", "size_bucket"), ) @@ -330,6 +433,26 @@ def _references_block(spec: dict) -> str: return f"[b]references[/b]\n{lines}\n" +def _render_profile_section(profile: dict | None) -> str: + """Render the right-pane Columns section from a per-slug profile.json blob. + + None / missing profile → suggestion to run the profile stage. + """ + if profile is None: + return ( + "No profile yet — run " + "`python -m scripts.pipeline.profile ` " + "to populate per-column statistics." + ) + lines = [ + f"rows: {profile.get('row_count', '?')}", + f"columns ({len(profile.get('columns', {}))}):", + ] + for name, col in profile.get("columns", {}).items(): + lines.append(format_column_line(name, col)) + return "\n".join(lines) + + def _columns_block(spec: dict, columns: list[tuple[str, str]] | None) -> str: """Render the `[b]columns[/b]` section for the detail pane. @@ -348,8 +471,18 @@ def _columns_block(spec: dict, columns: list[tuple[str, str]] | None) -> str: def _row(spec: dict, parquet_cell: str, vortex_cell: str, - hydrate_cell: str) -> tuple[str, ...]: + hydrate_cell: str, *, snapshot: dict | None = None) -> tuple[str, ...]: + """Build one DataTable row from a spec + its snapshot record. + + Returns cell values matching the order of COLUMNS. The `snapshot` kwarg + carries per-slug snapshot data (e.g. `size_bucket`) for fields that aren't + on the spec itself; callers outside `_rebuild_table` can omit it. + """ + snapshot = snapshot or {} scrape_cell = "⚠" if spec_field(spec, "license.scrape_advisory") else "·" + tags_cell = ", ".join(spec.get("tags") or []) or "·" + showcase_cell = ", ".join(spec.get("showcase") or []) or "·" + size_bucket_cell = snapshot.get("size_bucket") or "·" return ( spec["slug"], spec.get("family") or "", @@ -359,6 +492,9 @@ def _row(spec: dict, parquet_cell: str, vortex_cell: str, vortex_cell, scrape_cell, hydrate_cell, + tags_cell, + showcase_cell, + size_bucket_cell, ) @@ -787,6 +923,34 @@ class DatasetBrowser(App): CSS = """ Screen { layout: vertical; } + #root-row { height: 1fr; } + #facets { + width: 28; + height: 1fr; + border-right: solid $accent; + padding: 0 1; + } + #facets .facets-title { + text-style: bold; + padding: 0 0 1 0; + } + #counts-label { + padding: 0 0 1 0; + color: $text-muted; + } + #main-col { width: 1fr; height: 1fr; } + #preset-bar { + height: 3; + padding: 0 1; + align: left middle; + } + .preset-btn { + min-width: 14; + margin: 0 1; + } + .clear-btn { + margin-left: 2; + } #body { height: 1fr; } DataTable { width: 60%; height: 1fr; } #detail { @@ -795,6 +959,16 @@ class DatasetBrowser(App): padding: 1 2; border-left: solid $accent; } + SelectionList { height: auto; max-height: 12; } + .trait-row { + height: auto; + align: left middle; + margin: 0 1; + } + .trait-name { + width: 22; + content-align: left middle; + } """ BINDINGS = [ @@ -802,6 +976,12 @@ class DatasetBrowser(App): Binding("c", "show_columns", "Columns"), Binding("t", "show_types", "Types"), Binding("b", "show_build_confirm", "Build"), + Binding("f", "focus_facets", "Focus facets"), + Binding("C", "clear_facets", "Clear facets"), + Binding("1", "preset('start-here')", "Start Here"), + Binding("2", "preset('encoding-research')", "Encoding Research"), + Binding("3", "preset('vortex-wins')", "Vortex Wins"), + Binding("4", "preset('stress-test')", "Stress Test"), ] def __init__( @@ -840,24 +1020,169 @@ def __init__( def compose(self) -> ComposeResult: yield Header() - with Horizontal(id="body"): - yield DataTable(id="table", cursor_type="row", zebra_stripes=True) - with VerticalScroll(id="detail"): - yield Static("", id="detail-content") + live_vocabs = _live_vocabs(self._manifest) + with Horizontal(id="root-row"): + with VerticalScroll(id="facets"): + yield Label("Filters", classes="facets-title") + yield Label( + f"{len(self._specs)} of {len(self._specs)}", + id="counts-label", + ) + with Collapsible(title="Showcase", id="facet-group-showcase"): + yield _selection_list("showcase", list(SHOWCASE_TIERS)) + with Collapsible(title="Domain", id="facet-group-tag"): + yield _selection_list("tag", list(TAG_VOCAB)) + with Collapsible(title="Size", id="facet-group-size"): + yield _selection_list("size", list(SIZE_BUCKETS)) + with Collapsible(title="Shape traits", id="facet-group-traits"): + for flag in TRAIT_FLAGS: + with Horizontal(classes="trait-row"): + yield Label(flag, classes="trait-name") + yield RadioSet( + RadioButton("Any", id=f"trait-{flag}-any", value=True), + RadioButton("Yes", id=f"trait-{flag}-yes"), + RadioButton("No", id=f"trait-{flag}-no"), + id=f"trait-radioset-{flag}", + ) + with Collapsible(title="License", id="facet-group-license"): + yield _selection_list("license", live_vocabs["license"]) + with Collapsible(title="Family", id="facet-group-family"): + yield _selection_list("family", live_vocabs["family"]) + with Collapsible(title="Fetch type", id="facet-group-fetch_type"): + yield _selection_list("fetch_type", live_vocabs["fetch_type"]) + with Collapsible(title="Vortex", id="facet-group-vortex"): + yield _selection_list("vortex", ["available", "skipped"]) + with Vertical(id="main-col"): + with Horizontal(id="preset-bar"): + for preset_name in VIEW_PRESETS: + yield Button( + preset_name.replace("-", " ").title(), + id=f"preset-{preset_name}", + classes="preset-btn", + ) + yield Button("Clear", id="preset-clear", classes="preset-btn clear-btn") + with Horizontal(id="body"): + yield DataTable(id="table", cursor_type="row", zebra_stripes=True) + with VerticalScroll(id="detail"): + yield Static("", id="detail-content") yield Footer() def on_mount(self) -> None: table = self.query_one("#table", DataTable) for label, key in COLUMNS: table.add_column(label, key=key) - for spec in self._specs: - parquet_cell, vortex_cell, hydrate_cell = self._presence[spec["slug"]] + self._rebuild_table(self._specs) + if self._specs: + self._update_detail(self._specs[0]["slug"]) + + def _rebuild_table(self, specs: list[dict]) -> None: + """Repopulate the slug table with the given (already-filtered) specs.""" + table = self.query_one("#table", DataTable) + table.clear() + snapshot_slugs: dict = {} + if isinstance(self._snapshot, dict): + snapshot_slugs = self._snapshot.get("slugs") or {} + for spec in specs: + cells = self._presence.get(spec["slug"]) + if cells is None: + # Defensive — for fixtures or specs missing from _presence + # (shouldn't happen in normal use, but the test path can + # construct specs the app didn't stat). + parquet, vortex = _output_paths(spec, self._manifest) + hydrated = _hydrated_parquet(spec, self._manifest) + cells = ( + _parquet_cell(parquet), + _vortex_cell(spec, parquet, vortex), + _hydrate_cell(spec, hydrated), + ) + self._presence[spec["slug"]] = cells + parquet_cell, vortex_cell, hydrate_cell = cells + snap = snapshot_slugs.get(spec["slug"]) or {} table.add_row( - *_row(spec, parquet_cell, vortex_cell, hydrate_cell), + *_row(spec, parquet_cell, vortex_cell, hydrate_cell, snapshot=snap), key=spec["slug"], ) - if self._specs: - self._update_detail(self._specs[0]["slug"]) + + def _current_selections(self) -> dict: + """Read all SelectionLists into a {axis_name: set | bool | None} dict.""" + sel: dict = {} + for axis in ("showcase", "tag", "size", "license", "family", "fetch_type"): + try: + widget = self.query_one(f"#facet-{axis}", SelectionList) + sel[axis] = set(widget.selected) + except Exception: + sel[axis] = set() + try: + vortex_widget = self.query_one("#facet-vortex", SelectionList) + chosen = set(vortex_widget.selected) + if chosen == {"available"}: + sel["vortex"] = True + elif chosen == {"skipped"}: + sel["vortex"] = False + elif not chosen: + sel["vortex"] = None + else: + # Both selected → don't filter (no useful intersection) + sel["vortex"] = None + except Exception: + sel["vortex"] = None + + # Tri-state shape-trait radios. Each trait's RadioSet has three + # buttons (Any / Yes / No); "Any" maps to "unknown" (no filter). + trait_states: dict[str, str] = {} + for flag in TRAIT_FLAGS: + try: + rs = self.query_one(f"#trait-radioset-{flag}", RadioSet) + pressed = rs.pressed_button + if pressed is None: + trait_states[flag] = "unknown" + elif pressed.id and pressed.id.endswith("-yes"): + trait_states[flag] = "yes" + elif pressed.id and pressed.id.endswith("-no"): + trait_states[flag] = "no" + else: + trait_states[flag] = "unknown" + except Exception: + trait_states[flag] = "unknown" + sel["_traits"] = trait_states + return sel + + def _refresh_filter(self) -> None: + """Apply current FilterState to the slug table; update counts label.""" + selections = self._current_selections() + trait_states = selections.pop("_traits", {}) + primary = _filter_state_from_selections(selections) + traits_fs = _trait_state_to_filter(trait_states) + state = _combine_filters(primary, traits_fs) + snapshot_slugs: dict = {} + if isinstance(self._snapshot, dict): + snapshot_slugs = self._snapshot.get("slugs") or {} + visible: list[dict] = [] + for spec in self._specs: + snap = snapshot_slugs.get(spec["slug"]) or {} + if state.matches(spec=spec, snapshot=snap): + visible.append(spec) + self._rebuild_table(visible) + try: + label = self.query_one("#counts-label", Label) + label.update(f"{len(visible)} of {len(self._specs)}") + except Exception: + pass + + def on_selection_list_selected_changed( + self, event: SelectionList.SelectedChanged + ) -> None: + """A checkbox in any facet group changed — re-filter the slug table.""" + self._refresh_filter() + + def on_radio_set_changed(self, event: RadioSet.Changed) -> None: + """A shape-trait tri-state radio toggled — re-filter the slug table. + + Ignores RadioSets outside the trait group (currently none exist, but + guards against future additions).""" + rs_id = event.radio_set.id or "" + if rs_id.startswith("trait-radioset-"): + self._refresh_filter() def on_data_table_row_highlighted(self, event: DataTable.RowHighlighted) -> None: if event.row_key is not None and event.row_key.value: @@ -875,6 +1200,23 @@ def on_data_table_header_selected(self, event: DataTable.HeaderSelected) -> None self._sort_reverse = False self.query_one("#table", DataTable).sort(key, reverse=self._sort_reverse) + def _profile_for(self, slug: str) -> dict | None: + """Load and cache the per-slug profile.json (None when missing/malformed).""" + if not hasattr(self, "_profile_cache"): + self._profile_cache: dict[str, dict | None] = {} + if slug in self._profile_cache: + return self._profile_cache[slug] + path = outputs_root(self._manifest) / slug / "profile.json" + if not path.exists(): + self._profile_cache[slug] = None + return None + try: + import json as _json + self._profile_cache[slug] = _json.loads(path.read_text()) + except Exception: + self._profile_cache[slug] = None + return self._profile_cache[slug] + def _update_detail(self, slug: str) -> None: spec = self._by_slug.get(slug) if spec is None: @@ -885,9 +1227,11 @@ def _update_detail(self, slug: str) -> None: slug, self._manifest, self._snapshot ) columns, _src = self._columns_cache[slug] + body = _detail(spec, parquet_cell, vortex_cell, hydrate_cell, columns, + snapshot=self._snapshot) + profile_section = _render_profile_section(self._profile_for(slug)) self.query_one("#detail-content", Static).update( - _detail(spec, parquet_cell, vortex_cell, hydrate_cell, columns, - snapshot=self._snapshot) + f"{body}\n--- Columns ---\n{profile_section}" ) def _ensure_stats(self, slug: str) -> tuple[list[dict] | None, str | None]: @@ -919,6 +1263,74 @@ def action_show_types(self) -> None: stats, source = self._ensure_stats(slug) self.push_screen(TypesModal(slug, spec, stats, source)) + def _apply_state_to_facets(self, state: FilterState) -> None: + """Update facet widgets so their selections match `state`.""" + axis_to_values = { + "showcase": state.showcase, + "tag": state.tag, + "size": state.size, + "license": state.license, + "family": state.family, + "fetch_type": state.fetch_type, + } + for axis, values in axis_to_values.items(): + try: + widget = self.query_one(f"#facet-{axis}", SelectionList) + widget.deselect_all() + for v in values: + widget.select(v) + except Exception: + pass + + # Vortex: SelectionList with two options. + try: + widget = self.query_one("#facet-vortex", SelectionList) + widget.deselect_all() + if state.vortex is True: + widget.select("available") + elif state.vortex is False: + widget.select("skipped") + except Exception: + pass + + # Trait RadioSets: reset all to "Any", then set Yes/No for active traits. + for flag in TRAIT_FLAGS: + try: + rs = self.query_one(f"#trait-radioset-{flag}", RadioSet) + rs.query_one(f"#trait-{flag}-any", RadioButton).value = True + if flag in state.trait: + rs.query_one(f"#trait-{flag}-yes", RadioButton).value = True + elif flag in state.trait_negated: + rs.query_one(f"#trait-{flag}-no", RadioButton).value = True + except Exception: + pass + + def action_preset(self, name: str) -> None: + new_state = apply_preset(name) + self._apply_state_to_facets(new_state) + self._refresh_filter() + + def action_clear_facets(self) -> None: + self._apply_state_to_facets(FilterState()) + self._refresh_filter() + + def action_focus_facets(self) -> None: + try: + self.query_one("#facets").focus() + except Exception: + pass + + def on_button_pressed(self, event) -> None: + bid = event.button.id or "" + if bid == "preset-clear": + self.action_clear_facets() + return + if bid.startswith("preset-"): + preset_name = bid[len("preset-"):] + if preset_name in VIEW_PRESETS: + self.action_preset(preset_name) + return + def action_show_build_confirm(self) -> None: slug = self._highlighted_slug if slug is None: diff --git a/scripts/pipeline/discovery.py b/scripts/pipeline/discovery.py new file mode 100644 index 0000000..33f46ba --- /dev/null +++ b/scripts/pipeline/discovery.py @@ -0,0 +1,239 @@ +# SPDX-FileCopyrightText: 2026 Raincloud Maintainers +# SPDX-License-Identifier: Apache-2.0 +"""Shared catalog-discovery vocab + filter engine. + +Imported by browse.py (TUI), list_datasets.py (CLI), and validate_manifest.py. +Single source of truth for the closed vocabs and the view-preset registry. + +Two of the four discovery axes are *editorial* — authored in sources.json: + * `tags` — domain vocab; 0–3 per spec; values must come from TAG_VOCAB. + * `showcase` — editorial tier membership; values must come from SHOWCASE_TIERS. + +Two are *derived* — computed by docs.py and stored in snapshot.json per slug: + * `size_bucket` — file-size bucket, one of SIZE_BUCKETS. + * `shape_traits` — booleans (or null) for each member of TRAIT_FLAGS. +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Optional + + +TAG_VOCAB: tuple[str, ...] = ( + "geospatial", + "nlp-text", + "web-analytics", + "e-commerce", + "finance", + "social", + "scientific", + "healthcare", + "sports", + "transportation", + "government", + "benchmark", +) + +SHOWCASE_TIERS: tuple[str, ...] = ( + "start-here", + "encoding-research", + "vortex-wins", + "stress-test", +) + +SIZE_BUCKETS: tuple[str, ...] = ("xs", "s", "m", "l", "xl") + +_KB = 1024 +_MB = 1024 * _KB +_GB = 1024 * _MB + +SIZE_BUCKET_BOUNDS: dict[str, tuple[float, float]] = { + "xs": (0, 10 * _MB), + "s": (10 * _MB, 100 * _MB), + "m": (100 * _MB, 1 * _GB), + "l": (1 * _GB, 10 * _GB), + "xl": (10 * _GB, float("inf")), +} + +TRAIT_FLAGS: tuple[str, ...] = ( + "has_nested", + "has_timestamp", + "has_variant", + "string_heavy", + "wide_row", + "high_cardinality_present", +) + +# Saved facet selections used by the TUI "View" bar and the CLI --view flag. +# Each preset names axes → values; clicking a preset replaces the current +# selection in those axes only (axes not mentioned are cleared). +VIEW_PRESETS: dict[str, dict[str, set[str]]] = { + "start-here": {"showcase": {"start-here"}}, + "encoding-research": {"showcase": {"encoding-research"}}, + "vortex-wins": {"showcase": {"vortex-wins"}}, + "stress-test": {"showcase": {"stress-test"}, "size": {"l", "xl"}}, +} + + +def bucket_for_size(num_bytes: int | float) -> str: + """Return the size-bucket label for an on-disk byte count. + + Negative input is treated as zero (defensive — shouldn't happen). + """ + if num_bytes < 0: + num_bytes = 0 + for label in SIZE_BUCKETS: + lo, hi = SIZE_BUCKET_BOUNDS[label] + if lo <= num_bytes < hi: + return label + return "xl" + + +@dataclass +class FilterState: + """Multi-axis catalog filter. + + Selections within an axis OR-combine; across axes they AND-combine. + `dataclasses.replace(state, family=new)` produces a fully independent + copy — unchanged set fields are cloned in `__post_init__` so callers + can freely mutate either instance. + + `vortex` is tri-valued: None means "don't filter on vortex", True means + "only available", False means "only skipped". + """ + + showcase: set[str] = field(default_factory=set) + tag: set[str] = field(default_factory=set) + size: set[str] = field(default_factory=set) + trait: set[str] = field(default_factory=set) + trait_negated: set[str] = field(default_factory=set) + license: set[str] = field(default_factory=set) + family: set[str] = field(default_factory=set) + fetch_type: set[str] = field(default_factory=set) + vortex: Optional[bool] = None + + def __post_init__(self): + """Defensively clone mutable set fields so `dataclasses.replace` + produces fully independent instances (otherwise unchanged set fields + are shared by reference between old and new).""" + self.showcase = set(self.showcase) + self.tag = set(self.tag) + self.size = set(self.size) + self.trait = set(self.trait) + self.trait_negated = set(self.trait_negated) + self.license = set(self.license) + self.family = set(self.family) + self.fetch_type = set(self.fetch_type) + + def is_empty(self) -> bool: + return ( + not self.showcase and not self.tag and not self.size + and not self.trait and not self.trait_negated + and not self.license and not self.family + and not self.fetch_type and self.vortex is None + ) + + def matches(self, *, spec: dict, snapshot: dict) -> bool: + """Return True if the joined (spec, snapshot) record passes all filters.""" + if self.showcase and not (set(spec.get("showcase") or []) & self.showcase): + return False + if self.tag and not (set(spec.get("tags") or []) & self.tag): + return False + if self.size: + bucket = snapshot.get("size_bucket") + if bucket not in self.size: + return False + traits = snapshot.get("shape_traits") or {} + for flag in self.trait: + if traits.get(flag) is not True: + return False + for flag in self.trait_negated: + if traits.get(flag) is True: + return False + if self.license: + lic = ((spec.get("license") or {}).get("spdx")) + if lic not in self.license: + return False + if self.family and spec.get("family") not in self.family: + return False + if self.fetch_type: + ft = (spec.get("fetch") or {}).get("type") + if ft not in self.fetch_type: + return False + if self.vortex is not None: + available = bool((spec.get("convert") or {}).get("vortex", False)) + if available != self.vortex: + return False + return True + + +def apply_preset(name: str) -> FilterState: + """Return a new FilterState carrying only the named preset's selections. + + Axes mentioned by the preset are populated; other axes are empty. + Raises KeyError if `name` is not a registered preset. + """ + preset = VIEW_PRESETS[name] + new = FilterState() + for axis, values in preset.items(): + if not hasattr(new, axis): + raise ValueError( + f"VIEW_PRESETS['{name}'] mentions unknown axis {axis!r}" + ) + setattr(new, axis, set(values)) + return new + + +def _is_variant_field(field) -> bool: + """Detect raincloud's persistent-VARIANT marker in pyarrow field metadata. + + Pyarrow Field is duck-typed via its `.metadata` attribute; this helper + avoids importing pyarrow into discovery.py (keeping the module + dependency-light for validate_manifest.py). + """ + md = getattr(field, "metadata", None) or {} + return b"__variant_type" in md + + +SPARK_CHARS = " ▁▂▃▄▅▆▇█" + + +def sparkline(counts: list[int]) -> str: + """Render a list of non-negative counts as a Unicode-block sparkline.""" + if not counts: + return "" + hi = max(counts) + if hi == 0: + return SPARK_CHARS[0] * len(counts) + return "".join( + SPARK_CHARS[int(round((c / hi) * (len(SPARK_CHARS) - 1)))] + for c in counts + ) + + +def format_column_line(name: str, profile: dict | None) -> str: + """One-line text rendering of a per-column profile. + + Used by `list_datasets --inspect` (this task) and the TUI detail pane + (Task 17). For struct/variant or empty profiles, emits a "skipped" note. + """ + if profile is None: + return f" {name:<20} struct/variant (skipped)" + dtype = profile.get("dtype", "?") + if "histogram" in profile: + spark = sparkline(profile["histogram"]["counts"]) + lo = profile.get("min") + hi = profile.get("max") + return f" {name:<20} {dtype:<14} {lo} … {hi} {spark}" + if "ndv_approx" in profile: + ndv = profile["ndv_approx"] + top = profile.get("top_values") + if top: + preview = ", ".join(f"{t['value']!r}({t['count']})" for t in top[:3]) + return f" {name:<20} {dtype:<14} NDV≈{ndv} top: {preview}" + return f" {name:<20} {dtype:<14} NDV≈{ndv}, mean_len={profile.get('mean_length')}" + if "true_count" in profile: + return f" {name:<20} {dtype:<14} T:{profile['true_count']} F:{profile['false_count']} N:{profile['null_count']}" + if "length_min" in profile: + return f" {name:<20} {dtype:<14} len {profile['length_min']}…{profile['length_max']} (avg {profile['length_mean']:.1f})" + return f" {name:<20} {dtype:<14} (no stats)" diff --git a/scripts/pipeline/docs.py b/scripts/pipeline/docs.py index 2fea9b2..14e2fe1 100644 --- a/scripts/pipeline/docs.py +++ b/scripts/pipeline/docs.py @@ -43,9 +43,11 @@ import re import sys from datetime import datetime, timezone +from pathlib import Path import pyarrow.parquet as pq +from .discovery import SHOWCASE_TIERS, TRAIT_FLAGS, _is_variant_field, bucket_for_size from .spec import ( REPO_ROOT, load_manifest, @@ -146,6 +148,40 @@ def _size_label(bytes_: int | None) -> str: # ---------- datasets.md ---------- +_TIER_TITLES = { + "start-here": "Start Here", + "encoding-research": "Encoding Research", + "vortex-wins": "Vortex Wins", + "stress-test": "Stress Test", +} + + +def _render_curated_picks(manifest: dict) -> str: + """Render a curated-picks Markdown block keyed by SHOWCASE_TIERS. + + Member slugs come from sources.json `showcase` arrays; up to 8 per tier + (deterministic by manifest order). Empty tiers get a one-line + placeholder rather than disappearing. + """ + lines: list[str] = ["## Curated picks", ""] + for tier in SHOWCASE_TIERS: + members = [s for s in manifest.get("datasets", []) + if tier in (s.get("showcase") or [])] + title = _TIER_TITLES.get(tier, tier) + lines.append(f"### {title}") + if not members: + lines.append("_No picks yet — curation pass pending._") + lines.append("") + continue + for spec in members[:8]: + desc = (spec.get("description") or "").splitlines()[0][:140] + lines.append(f"- **[{spec['slug']}](#{spec['slug']})** — {desc}") + lines.append("") + lines.append("---") + lines.append("") + return "\n".join(lines) + + def generate_datasets_md(): manifest = load_manifest() snapshot_slugs = _load_snapshot_slugs(manifest.get("schema_version")) @@ -208,7 +244,7 @@ def generate_datasets_md(): sep = ("|--------------------|-------------------|---------------------" "|----------------------|-----------|---------|-----------" "|----------------------|---------------------|--------------------|") - out = [_generation_header("datasets"), header, sep] + out = [_generation_header("datasets"), "", _render_curated_picks(manifest), header, sep] for r in rows: out.append("| " + " | ".join(r) + " |") @@ -345,6 +381,128 @@ def generate_handlers_md(): # ---------- snapshot.json ---------- +def _is_nested_arrow_type(arrow_type) -> bool: + import pyarrow as pa + return ( + pa.types.is_list(arrow_type) + or pa.types.is_large_list(arrow_type) + or pa.types.is_fixed_size_list(arrow_type) + or pa.types.is_struct(arrow_type) + or pa.types.is_map(arrow_type) + ) + + +_HIGH_CARDINALITY_RATIO = 0.5 # NDV / row_count threshold to flag a string column + + +def _high_cardinality_from_profile(profile_path: "Path") -> bool | None: + """True if any string-typed column has ndv_approx / row_count >= ratio. + + Returns False when no string column meets the bar, None when no profile + exists or the profile is malformed. Reads profile.json only — does not + open the parquet. + """ + import json as _json + if not profile_path.exists(): + return None + try: + profile = _json.loads(profile_path.read_text()) + except Exception: + return None + rows = profile.get("row_count") or 0 + if rows <= 0: + return None + for col in (profile.get("columns") or {}).values(): + if not col: + continue + if col.get("dtype") not in {"string", "binary", "large_string", "large_binary"}: + continue + ndv = col.get("ndv_approx") or 0 + if ndv / rows >= _HIGH_CARDINALITY_RATIO: + return True + return False + + +def _shape_traits_from_schema(schema) -> dict[str, bool | None]: + """Compute the schema-derivable subset of TRAIT_FLAGS. + + `high_cardinality_present` is left null here — Task 10 populates it + from profile.json when available. + """ + import pyarrow as pa + n_cols = len(schema) + n_string = 0 + has_nested = False + has_timestamp = False + has_variant = False + for field in schema: + t = field.type + if pa.types.is_string(t) or pa.types.is_large_string(t): + n_string += 1 + if _is_nested_arrow_type(t): + has_nested = True + if pa.types.is_timestamp(t) or pa.types.is_date(t): + has_timestamp = True + if _is_variant_field(field): + has_variant = True + # variant is a struct payload, so also nested + has_nested = True + + return { + "has_nested": has_nested, + "has_timestamp": has_timestamp, + "has_variant": has_variant, + "string_heavy": (n_cols > 0) and (n_string / n_cols > 0.5), + "wide_row": n_cols > 50, + "high_cardinality_present": None, + } + + +def _snapshot_for_slug(*, slug: str, parquet_path: Path, prior_snapshot: dict | None) -> dict: + """Build the discovery-specific subset of a per-slug snapshot record. + + Owned fields: + - `size_bucket` — from parquet bytes via discovery.bucket_for_size. + - `shape_traits` — schema-derived; Task 10 backfills high_cardinality_present. + + Tasks 10 will further refine `high_cardinality_present` from profile.json. + + Falls back to prior snapshot values per the "load-bearing snapshot" + invariant: partial regens must not dash-out tracked ground truth. + + The `slug` param is currently informational only. + """ + _OWNED_KEYS = ("size_bucket", "shape_traits") + record: dict = {} + + if parquet_path.exists(): + record["size_bucket"] = bucket_for_size(parquet_path.stat().st_size) + # Read schema metadata only — no full file scan. Fall back to prior + # (or null) on a malformed/empty parquet so this stays robust. + try: + schema = pq.read_schema(parquet_path) + record["shape_traits"] = _shape_traits_from_schema(schema) + # If a per-slug profile exists, backfill high_cardinality_present. + profile_path = parquet_path.parent.parent / "profile.json" + record["shape_traits"]["high_cardinality_present"] = ( + _high_cardinality_from_profile(profile_path) + ) + except Exception: + if prior_snapshot is not None and "shape_traits" in prior_snapshot: + record["shape_traits"] = prior_snapshot["shape_traits"] + else: + record["shape_traits"] = None + elif prior_snapshot is not None: + for k in _OWNED_KEYS: + if k in prior_snapshot: + record[k] = prior_snapshot[k] + else: + record["size_bucket"] = None + record["shape_traits"] = None + + return record + + def generate_snapshot(*, overwrite_missing: bool = False): """Per-slug record of canonical-build state for the TUI / agents. @@ -389,6 +547,7 @@ def generate_snapshot(*, overwrite_missing: bool = False): parquet = prepared_parquet(slug) vortex = prepared_vortex(slug) expected_rows = spec_field(spec, "expect.rows") + prior_for_slug = existing_slugs.get(slug) fresh: dict = { "expected_rows": expected_rows, "last_built_rows": None, # populated below from parquet metadata @@ -412,6 +571,19 @@ def generate_snapshot(*, overwrite_missing: bool = False): n_with_schema += 1 except Exception as e: fresh["columns_error"] = f"{type(e).__name__}: {str(e)[:120]}" + # Discovery-axis fields: size_bucket + shape_traits. Adds to `fresh` + # without disturbing existing fields. When the parquet is missing this + # run, the helper preserves the prior snapshot's values so partial + # regens don't dash out tracked ground truth. The merge tuple below + # mirrors `_OWNED_KEYS` inside the helper. + snap_fragment = _snapshot_for_slug( + slug=slug, + parquet_path=parquet, + prior_snapshot=prior_for_slug, + ) + for k in ("size_bucket", "shape_traits"): + if k in snap_fragment: + fresh[k] = snap_fragment[k] fresh_has_data = (fresh["parquet_bytes"] is not None or fresh["vortex_bytes"] is not None or fresh["columns"] is not None) diff --git a/scripts/pipeline/handlers/text_whitespace_parse.py b/scripts/pipeline/handlers/text_whitespace_parse.py index c0bd066..612332f 100644 --- a/scripts/pipeline/handlers/text_whitespace_parse.py +++ b/scripts/pipeline/handlers/text_whitespace_parse.py @@ -5,9 +5,11 @@ Some UCI datasets (e.g. seeds) use a mix of tabs and multi-tab runs as their field separator, which pyarrow's CSV reader can't collapse. We read each non-blank line, split on `\\s+`, and construct a pyarrow Table with -auto-generated column names (`col_0`, `col_1`, ...). Downstream consumers -typically follow up with `uci_default`-style column renaming or manual -schema wiring. +auto-generated column names (`col_0`, `col_1`, ...). Per-column type +inference promotes columns where every non-null token parses as int64 to +int64, then to float64; otherwise the column stays as string. Downstream +consumers typically follow up with `uci_default`-style column renaming or +manual schema wiring. """ from __future__ import annotations @@ -17,6 +19,34 @@ import pyarrow as pa +def _infer_column(values: list[str | None]) -> pa.Array: + """Promote a column of string tokens to int64 / float64 when every non-null + token parses cleanly. Falls back to string for any ambiguity. + """ + non_null = [v for v in values if v is not None] + if non_null: + # int64 first — strict: must parse via int() AND have no decimal point. + try: + if all("." not in v and "e" not in v.lower() for v in non_null): + _ = [int(v) for v in non_null] + return pa.array( + [int(v) if v is not None else None for v in values], + type=pa.int64(), + ) + except (ValueError, TypeError): + pass + # float64 fallback for decimals / scientific notation. + try: + _ = [float(v) for v in non_null] + return pa.array( + [float(v) if v is not None else None for v in values], + type=pa.float64(), + ) + except (ValueError, TypeError): + pass + return pa.array(values, type=pa.string()) + + def text_whitespace_parse(spec: dict, parsed: list[tuple[Path, pa.Table | None]], **kwargs ) -> list[tuple[str, pa.Table]]: @@ -40,7 +70,6 @@ def text_whitespace_parse(spec: dict, parsed: list[tuple[Path, pa.Table | None]] for i in range(max_cols): cols[i].append(r[i] if i < len(r) else None) - table = pa.table({f"col_{i}": pa.array(cols[i], type=pa.string()) - for i in range(max_cols)}) + table = pa.table({f"col_{i}": _infer_column(cols[i]) for i in range(max_cols)}) print(f" {path.name}: {table.num_rows:,} rows × {table.num_columns} cols") return [(spec["slug"], table)] diff --git a/scripts/pipeline/list_datasets.py b/scripts/pipeline/list_datasets.py index f25b7eb..a245488 100644 --- a/scripts/pipeline/list_datasets.py +++ b/scripts/pipeline/list_datasets.py @@ -18,6 +18,11 @@ python -m scripts.pipeline.list_datasets --kaggle-tos # requires_interactive_accept python -m scripts.pipeline.list_datasets --scrape # license.scrape_advisory non-null python -m scripts.pipeline.list_datasets --hydrate # hydrate config non-null + python -m scripts.pipeline.list_datasets --showcase start-here # editorial tier (repeatable) + python -m scripts.pipeline.list_datasets --tag geospatial # domain tag (repeatable) + python -m scripts.pipeline.list_datasets --size s --size m # size bucket (repeatable) + python -m scripts.pipeline.list_datasets --trait has_nested # shape trait; ! to negate + python -m scripts.pipeline.list_datasets --view start-here # named preset (clears other axes) python -m scripts.pipeline.list_datasets --long # slug + key fields python -m scripts.pipeline.list_datasets --json # one JSON object per row python -m scripts.pipeline.list_datasets --count # just the count @@ -41,26 +46,101 @@ import sys from typing import Any +from .discovery import ( + FilterState, + SHOWCASE_TIERS, + SIZE_BUCKETS, + TAG_VOCAB, + TRAIT_FLAGS, + VIEW_PRESETS, + apply_preset, + format_column_line, +) from .spec import ( + REPO_ROOT, iter_datasets, load_manifest, + outputs_root, prepared_parquet, prepared_vortex, spec_field, ) -def _matches(spec: dict, args) -> bool: - if args.family and spec.get("family") != args.family: return False +def _load_snapshot() -> dict: + """Load docs/snapshot.json (or v1 fallback) keyed by slug. + + Returns {} when neither file exists or both are malformed. + """ + import json as _json + for p in (REPO_ROOT / "docs" / "snapshot.json", + REPO_ROOT / "docs" / "v1" / "snapshot.json"): + if p.exists(): + try: + blob = _json.loads(p.read_text()) + except Exception: + continue + if isinstance(blob, dict) and "slugs" in blob: + return blob["slugs"] + if isinstance(blob, dict) and "datasets" in blob: + return {d["slug"]: d for d in blob["datasets"]} + if isinstance(blob, dict): + return blob + return {} + + +def _filter_state_from_args(args) -> FilterState: + """Build FilterState from the parsed argparse Namespace. + + --view replaces ALL other facet selections (preset is the complete spec). + Mixing a preset with individual facet flags is incoherent UX, so when + --view is set we short-circuit and return the preset state unmodified. + Other inline filters (handler, reader, kaggle_tos, scrape, hydrate, grep) + still apply since they're outside FilterState's domain. + """ + if getattr(args, "view", None): + return apply_preset(args.view) + state = FilterState() + # Additive: each repeated flag adds to the corresponding axis. + for axis_name, src in ( + ("showcase", args.showcase), + ("tag", args.tag), + ("size", args.size), + ): + if src: + getattr(state, axis_name).update(src) + # The existing --license, --family, --fetch-type flags are single-value. + if getattr(args, "license", None): + state.license.add(args.license) + if getattr(args, "family", None): + state.family.add(args.family) + if getattr(args, "fetch_type", None): + state.fetch_type.add(args.fetch_type) + # Trait flags: prefix '!' negates. + for flag in args.trait or []: + if flag.startswith("!"): + state.trait_negated.add(flag[1:]) + else: + state.trait.add(flag) + if args.vortex: + state.vortex = True + elif args.no_vortex: + state.vortex = False + return state + + +def _matches(spec: dict, args, state: FilterState, snapshot: dict) -> bool: + """Apply the inline filters not covered by FilterState, then defer the + closed-vocab axes (family / license / fetch_type / vortex / showcase / + tag / size / trait) to FilterState.matches(). + """ if args.handler and spec_field(spec, "transform.handler") != args.handler: return False - if args.license and spec_field(spec, "license.spdx") != args.license: return False - if args.fetch_type and spec_field(spec, "fetch.type") != args.fetch_type: return False if args.reader and spec_field(spec, "parse.reader") != args.reader: return False - if args.vortex and not spec_field(spec, "convert.vortex", False): return False - if args.no_vortex and spec_field(spec, "convert.vortex", False): return False if args.kaggle_tos and not spec_field(spec, "fetch.requires_interactive_accept", False): return False if args.scrape and not spec_field(spec, "license.scrape_advisory"): return False if args.hydrate and not spec.get("hydrate"): return False + if not state.matches(spec=spec, snapshot=snapshot.get(spec["slug"], {})): + return False if args.grep: haystack = " ".join(( spec.get("slug", ""), @@ -254,6 +334,65 @@ def _render_coverage_table(rows: list[dict]) -> str: return "\n".join(out) +def _inspect(slug: str) -> int: + """Render the TUI detail-pane equivalent as plain text.""" + import json as _json + manifest = load_manifest() + spec = next((s for s in manifest["datasets"] if s["slug"] == slug), None) + if spec is None: + print(f"unknown slug: {slug}", file=sys.stderr) + return 2 + + print(f"# {slug} — {spec.get('short_name', '')}") + if spec.get("showcase"): + print(f"showcase: {', '.join(spec['showcase'])}") + if spec.get("tags"): + print(f"tags: {', '.join(spec['tags'])}") + lic = (spec.get("license") or {}).get("spdx") + if lic: + print(f"license: {lic}") + print(f"family: {spec.get('family')}") + print() + desc = (spec.get("description") or "").strip() + if desc: + print(desc) + print() + + profile_path = outputs_root() / slug / "profile.json" + if not profile_path.exists(): + print(f"no profile yet — run `python -m scripts.pipeline.profile {slug}`") + return 0 + + try: + profile = _json.loads(profile_path.read_text()) + except Exception as e: + print(f"profile.json malformed: {e}", file=sys.stderr) + return 2 + print(f"rows: {profile['row_count']} sample_rows: {profile.get('sample_rows')}") + print(f"columns ({len(profile['columns'])}):") + for name, col in profile["columns"].items(): + print(format_column_line(name, col)) + return 0 + + +def _print_vocab_help(manifest: dict, *, vocab_name: str) -> int: + """Print closed vocab + per-value count from the live manifest.""" + if vocab_name == "tags": + vocab = TAG_VOCAB + field = "tags" + else: + vocab = SHOWCASE_TIERS + field = "showcase" + counts = {v: 0 for v in vocab} + for spec in manifest["datasets"]: + for v in spec.get(field) or []: + if v in counts: + counts[v] += 1 + for v in vocab: + print(f" {v:<20} ({counts[v]})") + return 0 + + def main(argv: list[str] | None = None) -> int: ap = argparse.ArgumentParser(description=__doc__.split("\n", 1)[0]) ap.add_argument("--family", help="filter by family (direct, kaggle-upstream, nyc-tlc, public-bi, uci)") @@ -271,6 +410,16 @@ def main(argv: list[str] | None = None) -> int: ap.add_argument("--hydrate", action="store_true", help="only specs with a non-null hydrate block " "(URL columns marked as candidates for the hydrate stage)") + ap.add_argument("--showcase", action="append", choices=list(SHOWCASE_TIERS), + help="filter by editorial showcase tier; repeatable") + ap.add_argument("--tag", action="append", choices=list(TAG_VOCAB), + help="filter by domain tag; repeatable") + ap.add_argument("--size", action="append", choices=list(SIZE_BUCKETS), + help="filter by size bucket; repeatable") + ap.add_argument("--trait", action="append", + help="filter by shape trait (e.g. has_nested); prefix with ! to negate; repeatable") + ap.add_argument("--view", choices=list(VIEW_PRESETS), + help="apply a named view preset (replaces other selections)") ap.add_argument("--grep", help="regex over slug + short_name + full_name + description (case-insensitive)") ap.add_argument("--long", action="store_true", help="emit a wide table with key fields") ap.add_argument("--json", action="store_true", help="emit one JSON object per matching dataset") @@ -285,6 +434,12 @@ def main(argv: list[str] | None = None) -> int: help="when used with --columns, only emit columns whose name matches this regex (case-insensitive)") ap.add_argument("--source", choices=("parquet", "vortex"), default="parquet", help="when used with --columns / --coverage, read from parquet (default) or vortex outputs") + ap.add_argument("--inspect", metavar="SLUG", + help="render the detail view for one slug (description + per-column profile)") + ap.add_argument("--tags-help", action="store_true", + help="list the closed tag vocab + per-tag counts in the manifest") + ap.add_argument("--showcase-help", action="store_true", + help="list the showcase tiers + per-tier counts in the manifest") args = ap.parse_args(argv) if args.vortex and args.no_vortex: @@ -294,8 +449,18 @@ def main(argv: list[str] | None = None) -> int: print("--columns and --coverage are mutually exclusive", file=sys.stderr) return 2 + if args.inspect: + return _inspect(args.inspect) + manifest = load_manifest() + if args.tags_help: + return _print_vocab_help(manifest, vocab_name="tags") + if args.showcase_help: + return _print_vocab_help(manifest, vocab_name="showcase") + m = load_manifest() - matched = [s for s in iter_datasets(m) if _matches(s, args)] + state = _filter_state_from_args(args) + snapshot = _load_snapshot() + matched = [s for s in iter_datasets(m) if _matches(s, args, state, snapshot)] if args.columns or args.coverage: col_rows = _iter_columns(matched, args.source) diff --git a/scripts/pipeline/profile.py b/scripts/pipeline/profile.py new file mode 100644 index 0000000..0e7c5bb --- /dev/null +++ b/scripts/pipeline/profile.py @@ -0,0 +1,505 @@ +# SPDX-FileCopyrightText: 2026 Raincloud Maintainers +# SPDX-License-Identifier: Apache-2.0 +"""Per-column profile stage. + +Off the default build path. Run explicitly: + + python -m scripts.pipeline.profile ... + python -m scripts.pipeline.profile --all + python -m scripts.pipeline.profile --sample-rows 1000000 + +Reads outputs/v1//parquet/.parquet via spec.duckdb_connect, +writes outputs/v1//profile.json. Idempotent against the parquet's +SHA-256: a matching profile.json is reused without recomputation. +""" +from __future__ import annotations + +import argparse +import hashlib +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + +import pyarrow.parquet as pq + +from .discovery import _is_variant_field +from .spec import ( + duckdb_connect, + iter_datasets, + load_manifest, + outputs_root, + prepared_parquet, +) + + +_PROFILE_SCHEMA_VERSION = 1 +_HISTOGRAM_BUCKETS = 10 +_TOPK = 5 +_TOPK_NDV_CEILING = 256 + + +def _quote_ident(name: str) -> str: + """Quote a SQL identifier, escaping any embedded double-quotes.""" + return '"' + name.replace('"', '""') + '"' + + +def _file_sha256(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() + + +def _now_iso() -> str: + return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") + + +def _arrow_dtype_label(arrow_type) -> str: + """Human-readable dtype label persisted into profile.json.""" + import pyarrow as pa + if pa.types.is_signed_integer(arrow_type) or pa.types.is_unsigned_integer(arrow_type): + return str(arrow_type) + if pa.types.is_floating(arrow_type): + # pyarrow repr is "half"/"float"/"double" — normalise to bit-width tag + # so downstream consumers (and the profile.schema.json dtype regex + # ^(int|uint|float|decimal)) recognise the column as numeric. + bits = arrow_type.bit_width + return f"float{bits}" + if pa.types.is_decimal(arrow_type): + return str(arrow_type) + if pa.types.is_boolean(arrow_type): + return "bool" + if pa.types.is_string(arrow_type): + return "string" + if pa.types.is_large_string(arrow_type): + return "large_string" + if pa.types.is_binary(arrow_type): + return "binary" + if pa.types.is_large_binary(arrow_type): + return "large_binary" + if pa.types.is_date(arrow_type): + return f"date{32 if pa.types.is_date32(arrow_type) else 64}" + if pa.types.is_timestamp(arrow_type): + return f"timestamp[{arrow_type.unit}]" + if pa.types.is_time(arrow_type): + return "time" + if pa.types.is_list(arrow_type) or pa.types.is_large_list(arrow_type): + return "list" + if pa.types.is_map(arrow_type): + return "map" + if pa.types.is_struct(arrow_type): + return "struct" + return str(arrow_type) + + +def _numeric_profile(con, table_name: str, col: str, row_count: int) -> dict | None: + """Numeric column profile: min/max/mean/null/NDV + equi-width histogram. + + Returns None for all-null columns (lo and hi both null) — the dispatcher + propagates that as `null` at the column-map level, matching the existing + struct/variant convention and keeping profile.schema.json happy + (NumericProfile.mean requires a number). + """ + quoted = _quote_ident(col) + stats = con.execute(f""" + SELECT + min({quoted}) AS lo, + max({quoted}) AS hi, + avg(CAST({quoted} AS DOUBLE)) AS mean, + count(*) - count({quoted}) AS nulls, + approx_count_distinct({quoted}) AS ndv + FROM {table_name} + """).fetchone() + lo, hi, mean, nulls, ndv = stats + if lo is None and hi is None: + # All-null column — emit null at the column-map level for schema validity. + return None + if lo == hi: + # Degenerate: single distinct value. Skip histogram. + return { + "dtype": None, # filled in by caller + "null_count": int(nulls), + "min": lo, + "max": hi, + "mean": float(mean) if mean is not None else None, + "ndv_approx": int(ndv), + "histogram": {"buckets": [lo, lo], "counts": [row_count - int(nulls)]}, + } + + # Equi-width histogram. DuckDB lacks `width_bucket`, so emulate via floor: + # bucket = CASE x = hi THEN N ELSE floor((x - lo) / (hi - lo) * N) + 1 END + # The CASE pins the right-edge (x == hi) into the last bucket; floor() + # alone would push it into bucket N+1. + lo_f = float(lo) + hi_f = float(hi) + bounds = con.execute(f""" + WITH bucketed AS ( + SELECT CASE + WHEN CAST({quoted} AS DOUBLE) = {hi_f} THEN {_HISTOGRAM_BUCKETS} + ELSE CAST(floor((CAST({quoted} AS DOUBLE) - {lo_f}) / + ({hi_f} - {lo_f}) * {_HISTOGRAM_BUCKETS}) AS INTEGER) + 1 + END AS b + FROM {table_name} + WHERE {quoted} IS NOT NULL + ) + SELECT b, count(*) FROM bucketed GROUP BY b ORDER BY b + """).fetchall() + counts = [0] * _HISTOGRAM_BUCKETS + for b, c in bounds: + if b is None: + continue + idx = min(max(int(b) - 1, 0), _HISTOGRAM_BUCKETS - 1) + counts[idx] += int(c) + + step = (float(hi) - float(lo)) / _HISTOGRAM_BUCKETS + buckets = [float(lo) + step * i for i in range(_HISTOGRAM_BUCKETS + 1)] + return { + "null_count": int(nulls), + "min": lo, + "max": hi, + "mean": float(mean) if mean is not None else None, + "ndv_approx": int(ndv), + "histogram": {"buckets": buckets, "counts": counts}, + } + + +def _bool_profile(con, table_name: str, col: str) -> dict: + quoted = _quote_ident(col) + t, f, n = con.execute(f""" + SELECT + sum(CASE WHEN {quoted} THEN 1 ELSE 0 END), + sum(CASE WHEN {quoted} = false THEN 1 ELSE 0 END), + sum(CASE WHEN {quoted} IS NULL THEN 1 ELSE 0 END) + FROM {table_name} + """).fetchone() + return { + "dtype": "bool", + "true_count": int(t or 0), + "false_count": int(f or 0), + "null_count": int(n or 0), + } + + +def _temporal_profile(con, table_name: str, col: str, row_count: int, dtype: str) -> dict | None: + """Temporal column profile (date / time / timestamp). + + Returns None for all-null columns (TemporalProfile.min/max in + profile.schema.json require strings — emitting null at the column-map + level is the schema-conformant signal for "not profiled / no data"). + + All temporal strings (min, max, histogram bucket edges) use the same + `datetime.fromtimestamp(ms/1000, tz=utc).isoformat()` form for a + uniform `YYYY-MM-DDTHH:MM:SS+00:00` representation. + """ + quoted = _quote_ident(col) + lo_ms, hi_ms, nulls = con.execute(f""" + SELECT epoch_ms(CAST(min({quoted}) AS TIMESTAMP)), + epoch_ms(CAST(max({quoted}) AS TIMESTAMP)), + count(*) - count({quoted}) + FROM {table_name} + """).fetchone() + if lo_ms is None and hi_ms is None: + # All-null column — emit null at the column-map level for schema validity. + return None + + def _iso(ms: int) -> str: + return datetime.fromtimestamp(ms / 1000, tz=timezone.utc).isoformat() + + if lo_ms == hi_ms: + # Single distinct timestamp value: skip histogram bucketing. + edge = _iso(lo_ms) + return { + "dtype": dtype, + "null_count": int(nulls), + "min": edge, + "max": edge, + "histogram": {"buckets": [edge, edge], "counts": [row_count - int(nulls)]}, + } + # Bucket the rows by floor-arithmetic (DuckDB lacks `width_bucket`). + rows = con.execute(f""" + WITH ts AS ( + SELECT epoch_ms(CAST({quoted} AS TIMESTAMP)) AS ms + FROM {table_name} + WHERE {quoted} IS NOT NULL + ) + SELECT CASE + WHEN ms = {hi_ms} THEN {_HISTOGRAM_BUCKETS} + ELSE CAST(floor((ms - {lo_ms}) * 1.0 / + ({hi_ms} - {lo_ms}) * {_HISTOGRAM_BUCKETS}) AS INTEGER) + 1 + END AS b, count(*) + FROM ts GROUP BY b ORDER BY b + """).fetchall() + counts = [0] * _HISTOGRAM_BUCKETS + for b, c in rows: + if b is None: + continue + idx = min(max(int(b) - 1, 0), _HISTOGRAM_BUCKETS - 1) + counts[idx] += int(c) + # Bucket edges as ISO strings (uniform with min/max above). + edges_ms = con.execute(f""" + SELECT {lo_ms} + ({hi_ms} - {lo_ms}) * gen / {_HISTOGRAM_BUCKETS} + FROM range(0, {_HISTOGRAM_BUCKETS + 1}) AS t(gen) + """).fetchall() + buckets = [_iso(ms) for (ms,) in edges_ms] + return { + "dtype": dtype, + "null_count": int(nulls), + "min": _iso(lo_ms), + "max": _iso(hi_ms), + "histogram": {"buckets": buckets, "counts": counts}, + } + + +def _string_profile(con, table_name: str, col: str, include_topk: bool = True, + length_expr: str = "length({col})") -> dict | None: + """String/binary column profile. + + `length_expr` is a SQL expression template that returns an integer length + for one row of column `{col}`. Callers pick the right form for the column + type, because DuckDB ≥ 1.5 has non-overlapping signatures: + + - VARCHAR / LARGE_STRING → `length({col})` (character count) + - BLOB / LARGE_BINARY → `octet_length({col})` (byte count) + - GEOMETRY (GeoParquet) → `octet_length(ST_AsWKB({col}))` (byte count) + + `include_topk=False` suppresses the top-K query — used for binary columns + where bytes literals don't render as useful "top values" text regardless + of NDV. + """ + quoted = _quote_ident(col) + length_sql = length_expr.format(col=quoted) + n_total, n_null, ndv, mean_len = con.execute(f""" + SELECT + count(*), + count(*) - count({quoted}), + approx_count_distinct({quoted}), + avg({length_sql}) + FROM {table_name} + """).fetchone() + # All-null → return None at column-map level (matches numeric/temporal convention). + if n_total > 0 and n_null == n_total: + return None + out = { + "null_count": int(n_null), + "ndv_approx": int(ndv), + "mean_length": float(mean_len) if mean_len is not None else None, + "top_values": None, + } + if include_topk and 0 < int(ndv) <= _TOPK_NDV_CEILING: + rows = con.execute(f""" + SELECT {quoted} AS v, count(*) AS c + FROM {table_name} + WHERE {quoted} IS NOT NULL + GROUP BY v + ORDER BY c DESC + LIMIT {_TOPK} + """).fetchall() + out["top_values"] = [ + {"value": (v if isinstance(v, (str, type(None))) else str(v)), + "count": int(c)} for v, c in rows + ] + return out + + +def _list_profile(con, table_name: str, col: str, + length_fn: str = "len") -> dict | None: + """List/map column profile. + + `length_fn` selects the DuckDB length function appropriate to the column's + type: `len` (alias `length` / `array_length`) for LIST, `cardinality` for + MAP. In DuckDB ≥ 1.5 these signatures are non-overlapping — `len(MAP)` + raises BinderException and `cardinality(LIST)` raises "Cardinality can + only operate on MAPs" — so the caller must pass the right one. + """ + quoted = _quote_ident(col) + n_total, nulls, lmin, lmax, lmean = con.execute(f""" + SELECT + count(*), + count(*) - count({quoted}), + min({length_fn}({quoted})), + max({length_fn}({quoted})), + avg({length_fn}({quoted})) + FROM {table_name} + """).fetchone() + if n_total > 0 and nulls == n_total: + return None + return { + "null_count": int(nulls), + "length_min": int(lmin if lmin is not None else 0), + "length_max": int(lmax if lmax is not None else 0), + "length_mean": float(lmean) if lmean is not None else 0.0, + } + + +def _column_profile(con, table_name: str, field, row_count: int, + duckdb_type: str | None = None) -> dict | None: + """Dispatch on pyarrow dtype. Returns None for struct/variant (caller writes null). + + `duckdb_type` is DuckDB's reported SQL type for this column. Used to detect + cases where DuckDB has reinterpreted a parquet binary column as an extension + type (today: GEOMETRY, from GeoParquet metadata + auto-loaded `spatial`), + and route the length expression accordingly. + """ + import pyarrow as pa + t = field.type + dtype_label = _arrow_dtype_label(t) + + if pa.types.is_signed_integer(t) or pa.types.is_unsigned_integer(t) \ + or pa.types.is_floating(t) or pa.types.is_decimal(t): + p = _numeric_profile(con, table_name, field.name, row_count) + if p is None: + return None + p["dtype"] = dtype_label + return p + if pa.types.is_boolean(t): + return _bool_profile(con, table_name, field.name) + if pa.types.is_date(t) or pa.types.is_timestamp(t) or pa.types.is_time(t): + return _temporal_profile(con, table_name, field.name, row_count, dtype_label) + if pa.types.is_string(t) or pa.types.is_large_string(t): + p = _string_profile(con, table_name, field.name, + include_topk=True, length_expr="length({col})") + if p is None: + return None + p["dtype"] = dtype_label + return p + if pa.types.is_binary(t) or pa.types.is_large_binary(t): + # BLOB columns: `length()` is undefined on BLOB in DuckDB ≥ 1.5, so use + # `octet_length` for byte length. Suppress top_values because bytes + # literals don't render as useful text strings. + # + # Special case: GeoParquet binary columns get reinterpreted as GEOMETRY + # by the auto-loaded `spatial` extension, and `octet_length(GEOMETRY)` + # doesn't bind. Wrap with ST_AsWKB to round-trip to BLOB. + if duckdb_type and duckdb_type.upper() == "GEOMETRY": + length_expr = "octet_length(ST_AsWKB({col}))" + else: + length_expr = "octet_length({col})" + p = _string_profile(con, table_name, field.name, + include_topk=False, length_expr=length_expr) + if p is None: + return None + p["dtype"] = dtype_label + return p + if pa.types.is_list(t) or pa.types.is_large_list(t): + p = _list_profile(con, table_name, field.name, length_fn="len") + if p is None: + return None + p["dtype"] = "list" + return p + if pa.types.is_map(t): + # MAP columns: `len()` is undefined on MAP in DuckDB ≥ 1.5, so use + # `cardinality` (entry count). + p = _list_profile(con, table_name, field.name, length_fn="cardinality") + if p is None: + return None + p["dtype"] = "map" + return p + if pa.types.is_struct(t) or _is_variant_field(field): + return None + # Fallback for unknown types: column-map None (not profiled). + return None + + +def profile_slug(*, slug: str, parquet_path: Path, sample_rows: int | None = None) -> dict: + """Compute a full profile for one slug. Pure: no filesystem writes.""" + sha = _file_sha256(parquet_path) + + schema = pq.read_schema(parquet_path) + row_count = pq.ParquetFile(parquet_path).metadata.num_rows + + src = str(parquet_path).replace("'", "''") + if sample_rows and row_count > sample_rows: + # Reservoir sampling via DuckDB. + view_sql = f"SELECT * FROM read_parquet('{src}') USING SAMPLE {sample_rows} ROWS" + else: + view_sql = f"SELECT * FROM read_parquet('{src}')" + + columns: dict[str, dict | None] = {} + with duckdb_connect() as con: + con.execute(f"CREATE OR REPLACE TEMP VIEW _profile_src AS {view_sql}") + eff_rows = con.execute("SELECT count(*) FROM _profile_src").fetchone()[0] + # DuckDB may re-interpret some columns via auto-loaded extensions + # (e.g. GeoParquet binary → GEOMETRY under `spatial`). Capture the + # SQL-level type so `_column_profile` can pick the right expression. + # PRAGMA table_info returns (cid, name, type, notnull, dflt, pk). + duckdb_types = { + row[1]: row[2] + for row in con.execute("PRAGMA table_info(_profile_src)").fetchall() + } + for field in schema: + columns[field.name] = _column_profile( + con, "_profile_src", field, eff_rows, + duckdb_type=duckdb_types.get(field.name), + ) + + return { + "schema_version": _PROFILE_SCHEMA_VERSION, + "slug": slug, + "row_count": int(row_count), + "parquet_sha256": sha, + "computed_at": _now_iso(), + "sample_rows": sample_rows, + "columns": columns, + } + + +def _profile_path(slug: str) -> Path: + return outputs_root() / slug / "profile.json" + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(prog="profile", + description=__doc__.splitlines()[0]) + ap.add_argument("slugs", nargs="*", help="slugs to profile (default: empty → see --all)") + ap.add_argument("--all", action="store_true", + help="profile every slug whose parquet exists locally") + ap.add_argument("--sample-rows", type=int, default=None, + help="cap row count via reservoir sampling (default: full pass)") + args = ap.parse_args(argv) + + manifest = load_manifest() + targets: list[str] + if args.all: + targets = [s["slug"] for s in iter_datasets(manifest) + if prepared_parquet(s["slug"], manifest).exists()] + else: + if not args.slugs: + ap.error("pass --all or one or more slugs") + targets = list(args.slugs) + + failures = 0 + for slug in targets: + parquet = prepared_parquet(slug, manifest) + if not parquet.exists(): + print(f"skip: {slug} — no parquet at {parquet}", file=sys.stderr) + continue + out = _profile_path(slug) + if out.exists(): + try: + prior = json.loads(out.read_text()) + if prior.get("parquet_sha256") == _file_sha256(parquet) and \ + prior.get("sample_rows") == args.sample_rows: + print(f"cached: {slug}") + continue + except Exception: + pass + + try: + result = profile_slug(slug=slug, parquet_path=parquet, + sample_rows=args.sample_rows) + except Exception as e: # noqa: BLE001 — surface per-slug errors + print(f"error: {slug}: {e}", file=sys.stderr) + failures += 1 + continue + + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(result, indent=2, default=str) + "\n") + print(f"wrote: {out}") + + return 1 if failures else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pipeline/validate_manifest.py b/scripts/pipeline/validate_manifest.py index 6db3558..fca248a 100644 --- a/scripts/pipeline/validate_manifest.py +++ b/scripts/pipeline/validate_manifest.py @@ -34,6 +34,7 @@ import sys from collections import Counter, defaultdict +from .discovery import SHOWCASE_TIERS, TAG_VOCAB from .spec import REPO_ROOT, load_manifest SCHEMA_PATH = REPO_ROOT / "sources.schema.json" @@ -65,6 +66,50 @@ def _registry_handlers() -> set[str]: return set(_REGISTRY) +def _check_discovery_vocab(manifest: dict) -> tuple[list[str], list[str]]: + """Per-spec validation of `tags` + `showcase`, plus an empty-tier warning. + + Schema-level enums already cover unknown values, but this cross-check pass + produces clearer error formatting AND adds the empty-tier warning, which + can't be expressed in JSON schema. + + Returns (errors, warnings). + """ + errors: list[str] = [] + warnings: list[str] = [] + tier_members: dict[str, list[str]] = {t: [] for t in SHOWCASE_TIERS} + + for spec in manifest.get("datasets", []): + slug = spec.get("slug", "") + + for tag in spec.get("tags") or []: + if tag not in TAG_VOCAB: + errors.append( + f"{slug}: tags entry {tag!r} is not in TAG_VOCAB " + f"(see scripts/pipeline/discovery.py)" + ) + + for tier in spec.get("showcase") or []: + if tier not in SHOWCASE_TIERS: + errors.append( + f"{slug}: showcase entry {tier!r} is not in SHOWCASE_TIERS" + ) + else: + tier_members[tier].append(slug) + + # Only flag empty tiers once *any* tier has at least one member. + # All-empty = "not curated yet" (scaffolding state); no warnings then. + any_populated = any(members for members in tier_members.values()) + if any_populated: + for tier, members in tier_members.items(): + if not members: + warnings.append( + f"showcase tier {tier!r} has zero members across the manifest" + ) + + return errors, warnings + + def _cross_checks(manifest: dict) -> tuple[list[str], list[str]]: """Returns (errors, warnings).""" errors: list[str] = [] @@ -155,6 +200,10 @@ def _cross_checks(manifest: dict) -> tuple[list[str], list[str]]: f"convert.vortex_skip_reason explaining the opt-out" ) + discovery_errors, discovery_warnings = _check_discovery_vocab(manifest) + errors.extend(discovery_errors) + warnings.extend(discovery_warnings) + return errors, warnings diff --git a/sources.schema.json b/sources.schema.json index 8ed01b4..758f87c 100644 --- a/sources.schema.json +++ b/sources.schema.json @@ -69,6 +69,31 @@ } }, "description": "Optional canonical references for the dataset beyond `license.source_url`. Most useful for surfacing the underlying paper or the project homepage in the TUI." + }, + "tags": { + "type": "array", + "default": [], + "uniqueItems": true, + "maxItems": 3, + "items": { + "type": "string", + "enum": [ + "geospatial", "nlp-text", "web-analytics", "e-commerce", + "finance", "social", "scientific", "healthcare", + "sports", "transportation", "government", "benchmark" + ] + }, + "description": "Closed-vocab domain tags (0–3) used by the TUI faceted side panel and `list_datasets --tag`. See `scripts/pipeline/discovery.py:TAG_VOCAB` for the authoritative list." + }, + "showcase": { + "type": "array", + "default": [], + "uniqueItems": true, + "items": { + "type": "string", + "enum": ["start-here", "encoding-research", "vortex-wins", "stress-test"] + }, + "description": "Editorial showcase tiers. Members of `start-here` are surfaced in the README + curated-picks header. See `scripts/pipeline/discovery.py:SHOWCASE_TIERS`." } } }, diff --git a/sources.schema.md b/sources.schema.md index cf3c516..62c4b91 100644 --- a/sources.schema.md +++ b/sources.schema.md @@ -110,9 +110,13 @@ This document defines the shape of `sources.json`, the manifest that drives the "advisory": "Many LAION URLs return 404 (10-30% takedown rate); ..." // per-slug pitfalls }, - /* Optional curatorial labels — list_datasets --tag filters by membership; - TUI renders as chips. Kebab-case. Free-form (no enum) but kept short. */ - "tags": ["nlp", "rlhf", "preference-data"], + /* Optional curatorial domain tags — closed vocab from + scripts/pipeline/discovery.py:TAG_VOCAB. At most 3 per spec; unique. */ + "tags": ["nlp-text", "benchmark"], + + /* Optional editorial showcase tiers — closed vocab from + scripts/pipeline/discovery.py:SHOWCASE_TIERS. Multi-tier membership allowed. */ + "showcase": ["start-here", "vortex-wins"], /* Optional canonical references beyond license.source_url. kind ∈ {paper, blog, homepage, github, dataset_card}. */ @@ -123,6 +127,16 @@ This document defines the shape of `sources.json`, the manifest that drives the } ``` +### `tags` *(array of string, optional, default `[]`)* + +Closed-vocab domain tags drawn from the discovery module's `TAG_VOCAB`. At most 3 per spec; values must be unique. Used by the TUI's "Domain" facet group and `list_datasets --tag`. Current vocab: + +`geospatial`, `nlp-text`, `web-analytics`, `e-commerce`, `finance`, `social`, `scientific`, `healthcare`, `sports`, `transportation`, `government`, `benchmark`. + +### `showcase` *(array of string, optional, default `[]`)* + +Editorial showcase tiers from `SHOWCASE_TIERS`: `start-here`, `encoding-research`, `vortex-wins`, `stress-test`. Multi-tier membership allowed. Drives the TUI view presets, the `--view` CLI flag, and the curated-picks block in `docs/v1/datasets.md`. + ## Handlers `transform.handler` names a Python callable registered in `scripts/pipeline/handlers/__init__.py`. Each handler takes diff --git a/tests/fixtures/profile_uci_seeds.json b/tests/fixtures/profile_uci_seeds.json new file mode 100644 index 0000000..7f0e41b --- /dev/null +++ b/tests/fixtures/profile_uci_seeds.json @@ -0,0 +1,290 @@ +{ + "schema_version": 1, + "slug": "uci-seeds", + "row_count": 210, + "parquet_sha256": "59639068c918f2f3f46532b3b2ee47605d1d3a59580cbb3872259f1c5becd886", + "computed_at": "2026-05-12T18:24:10Z", + "sample_rows": null, + "columns": { + "col_0": { + "null_count": 0, + "min": 10.59, + "max": 21.18, + "mean": 14.847523809523816, + "ndv_approx": 183, + "histogram": { + "buckets": [ + 10.59, + 11.649, + 12.708, + 13.767, + 14.826, + 15.885, + 16.944, + 18.003, + 19.061999999999998, + 20.121, + 21.18 + ], + "counts": [ + 32, + 34, + 22, + 28, + 22, + 16, + 10, + 26, + 13, + 7 + ] + }, + "dtype": "float64" + }, + "col_1": { + "null_count": 0, + "min": 12.41, + "max": 17.25, + "mean": 14.559285714285718, + "ndv_approx": 170, + "histogram": { + "buckets": [ + 12.41, + 12.894, + 13.378, + 13.862, + 14.346, + 14.83, + 15.314, + 15.798, + 16.282, + 16.766, + 17.25 + ], + "counts": [ + 14, + 32, + 38, + 21, + 23, + 18, + 13, + 21, + 20, + 10 + ] + }, + "dtype": "float64" + }, + "col_2": { + "null_count": 0, + "min": 0.8081, + "max": 0.9183, + "mean": 0.8709985714285714, + "ndv_approx": 184, + "histogram": { + "buckets": [ + 0.8081, + 0.8191200000000001, + 0.83014, + 0.84116, + 0.85218, + 0.8632, + 0.87422, + 0.88524, + 0.8962600000000001, + 0.90728, + 0.9183 + ], + "counts": [ + 6, + 9, + 10, + 17, + 27, + 37, + 41, + 29, + 27, + 7 + ] + }, + "dtype": "float64" + }, + "col_3": { + "null_count": 0, + "min": 4.899, + "max": 6.675, + "mean": 5.628533333333335, + "ndv_approx": 186, + "histogram": { + "buckets": [ + 4.899, + 5.0766, + 5.2542, + 5.4318, + 5.6094, + 5.787, + 5.9646, + 6.1422, + 6.3198, + 6.4974, + 6.675 + ], + "counts": [ + 11, + 41, + 40, + 25, + 21, + 16, + 19, + 20, + 9, + 8 + ] + }, + "dtype": "float64" + }, + "col_4": { + "null_count": 0, + "min": 2.63, + "max": 4.033, + "mean": 3.258604761904762, + "ndv_approx": 183, + "histogram": { + "buckets": [ + 2.63, + 2.7702999999999998, + 2.9106, + 3.0509, + 3.1912000000000003, + 3.3315, + 3.4718, + 3.6121, + 3.7524, + 3.8927000000000005, + 4.033 + ], + "counts": [ + 23, + 26, + 23, + 25, + 22, + 26, + 20, + 13, + 25, + 7 + ] + }, + "dtype": "float64" + }, + "col_5": { + "null_count": 0, + "min": 0.7651, + "max": 8.456, + "mean": 3.7002009523809516, + "ndv_approx": 211, + "histogram": { + "buckets": [ + 0.7651, + 1.53419, + 2.30328, + 3.07237, + 3.8414599999999997, + 4.61055, + 5.37964, + 6.14873, + 6.91782, + 7.68691, + 8.456 + ], + "counts": [ + 14, + 33, + 31, + 38, + 35, + 31, + 16, + 7, + 3, + 2 + ] + }, + "dtype": "float64" + }, + "col_6": { + "null_count": 0, + "min": 4.519, + "max": 6.55, + "mean": 5.408071428571429, + "ndv_approx": 163, + "histogram": { + "buckets": [ + 4.519, + 4.7221, + 4.9252, + 5.1283, + 5.3314, + 5.5344999999999995, + 5.7376000000000005, + 5.9407, + 6.1438, + 6.3469, + 6.55 + ], + "counts": [ + 6, + 19, + 48, + 51, + 17, + 3, + 25, + 17, + 17, + 7 + ] + }, + "dtype": "float64" + }, + "col_7": { + "null_count": 0, + "min": 1, + "max": 3, + "mean": 2.0, + "ndv_approx": 3, + "histogram": { + "buckets": [ + 1.0, + 1.2, + 1.4, + 1.6, + 1.8, + 2.0, + 2.2, + 2.4000000000000004, + 2.6, + 2.8, + 3.0 + ], + "counts": [ + 70, + 0, + 0, + 0, + 0, + 70, + 0, + 0, + 0, + 70 + ] + }, + "dtype": "int64" + } + } +} diff --git a/tests/test_browse.py b/tests/test_browse.py index f066fd0..262be7b 100644 --- a/tests/test_browse.py +++ b/tests/test_browse.py @@ -491,6 +491,130 @@ async def _run() -> None: table = app.query_one("#table", DataTable) assert table.row_count == 2 - assert len(table.columns) == 8 + assert len(table.columns) == 11 asyncio.run(_run()) + + +def test_collect_filter_state_from_facet_selections(): + """_filter_state_from_selections collects checkbox selections from each + group into a FilterState (multi-select within axis, AND across axes).""" + pytest.importorskip("textual") + from scripts.pipeline.browse import _filter_state_from_selections + + selections = { + "showcase": {"start-here"}, + "tag": {"geospatial", "scientific"}, + "size": {"l", "xl"}, + "license": set(), + "family": {"uci"}, + "fetch_type": {"http"}, + "vortex": True, + } + state = _filter_state_from_selections(selections) + assert state.showcase == {"start-here"} + assert state.tag == {"geospatial", "scientific"} + assert state.size == {"l", "xl"} + assert state.family == {"uci"} + assert state.fetch_type == {"http"} + assert state.vortex is True + # Empty axis stays empty. + assert state.license == set() + + +def test_filter_state_from_selections_handles_vortex_none(): + pytest.importorskip("textual") + from scripts.pipeline.browse import _filter_state_from_selections + state = _filter_state_from_selections({"vortex": None}) + assert state.vortex is None + + +def test_trait_tri_state_to_filter_state(): + """A tri-state widget maps {yes, no, unknown} → {trait, trait_negated, ignore}.""" + pytest.importorskip("textual") + from scripts.pipeline.browse import _trait_state_to_filter + + state = _trait_state_to_filter({ + "has_nested": "yes", + "has_timestamp": "no", + "string_heavy": "unknown", + }) + assert state.trait == {"has_nested"} + assert state.trait_negated == {"has_timestamp"} + # "unknown" doesn't filter on either side + assert "string_heavy" not in state.trait + assert "string_heavy" not in state.trait_negated + + +def test_combine_filters_merges_axes(): + """The combine helper preserves set fields from both sources.""" + pytest.importorskip("textual") + from scripts.pipeline.browse import _combine_filters, _filter_state_from_selections + from scripts.pipeline.discovery import FilterState + + a = _filter_state_from_selections({"showcase": {"start-here"}}) + b = FilterState(trait={"has_nested"}) + out = _combine_filters(a, b) + assert out.showcase == {"start-here"} + assert out.trait == {"has_nested"} + + +def test_apply_view_preset_matches_filter_state(): + """Applying a preset programmatically yields the expected FilterState shape.""" + pytest.importorskip("textual") + from scripts.pipeline.discovery import apply_preset, FilterState + + state = apply_preset("stress-test") + assert state.showcase == {"stress-test"} + assert state.size == {"l", "xl"} + # And presets that aren't stress-test still produce clean states. + state2 = apply_preset("start-here") + assert state2.showcase == {"start-here"} + assert state2.size == set() + + +def test_row_helper_renders_tags_and_size_cells(): + """_row now emits cells for the new sortable columns (tags, showcase, size_bucket).""" + pytest.importorskip("textual") + from scripts.pipeline.browse import _row + + spec = {"slug": "x", "tags": ["geospatial"], "showcase": ["start-here"], + "convert": {"vortex": True}, + "license": {"spdx": "MIT"}, "short_name": "X", "family": "uci"} + snapshot = {"size_bucket": "m"} + cells = _row(spec, parquet_cell="·", vortex_cell="·", hydrate_cell="—", + snapshot=snapshot) + text = " ".join(str(c) for c in cells) + # New cells should appear in the row output. + assert "geospatial" in text + assert "start-here" in text + assert "m" in cells # size bucket as a literal cell + + +def test_render_profile_section_renders_one_line_per_column(): + pytest.importorskip("textual") + from scripts.pipeline.browse import _render_profile_section + + profile = { + "row_count": 100, + "columns": { + "n": {"dtype": "int32", "null_count": 0, "min": 0, "max": 9, + "mean": 4.5, "ndv_approx": 10, + "histogram": {"buckets": list(range(11)), + "counts": [10, 5, 1, 0, 3, 9, 8, 7, 4, 2]}}, + "s": {"dtype": "string", "null_count": 0, "ndv_approx": 3, + "mean_length": 4.0, + "top_values": [{"value": "a", "count": 50}]}, + }, + } + text = _render_profile_section(profile) + assert "n" in text and "s" in text + assert "NDV" in text or "ndv" in text.lower() + + +def test_render_profile_section_missing_returns_pointer(): + pytest.importorskip("textual") + from scripts.pipeline.browse import _render_profile_section + text = _render_profile_section(None) + assert "profile" in text.lower() + assert "scripts.pipeline.profile" in text diff --git a/tests/test_discovery.py b/tests/test_discovery.py new file mode 100644 index 0000000..dbd73ff --- /dev/null +++ b/tests/test_discovery.py @@ -0,0 +1,164 @@ +# SPDX-FileCopyrightText: 2026 Raincloud Maintainers +# SPDX-License-Identifier: Apache-2.0 +"""Unit tests for the discovery vocab + filter engine. + +These tests are side-effect-free: they don't read sources.json or any parquet. +""" +from __future__ import annotations + +import pytest + +from scripts.pipeline.discovery import ( + TAG_VOCAB, + SHOWCASE_TIERS, + SIZE_BUCKETS, + SIZE_BUCKET_BOUNDS, + TRAIT_FLAGS, + VIEW_PRESETS, + bucket_for_size, +) + + +def test_vocab_shapes(): + assert len(TAG_VOCAB) == 12 + assert len(SHOWCASE_TIERS) == 4 + assert SIZE_BUCKETS == ("xs", "s", "m", "l", "xl") + assert "has_nested" in TRAIT_FLAGS + assert "high_cardinality_present" in TRAIT_FLAGS + # No duplicates anywhere. + for vocab in (TAG_VOCAB, SHOWCASE_TIERS, SIZE_BUCKETS, TRAIT_FLAGS): + assert len(set(vocab)) == len(vocab) + + +def test_view_presets_reference_valid_vocab(): + """Every preset only mentions axes and values from the closed vocabs.""" + for name, axes in VIEW_PRESETS.items(): + for axis, values in axes.items(): + if axis == "showcase": + assert values <= set(SHOWCASE_TIERS), (name, values) + elif axis == "size": + assert values <= set(SIZE_BUCKETS), (name, values) + elif axis == "tag": + assert values <= set(TAG_VOCAB), (name, values) + + +@pytest.mark.parametrize("nbytes,expected", [ + (0, "xs"), + (10 * 1024 * 1024 - 1, "xs"), + (10 * 1024 * 1024, "s"), + (100 * 1024 * 1024 - 1, "s"), + (100 * 1024 * 1024, "m"), + (1024 ** 3, "l"), + (10 * (1024 ** 3) - 1, "l"), + (10 * (1024 ** 3), "xl"), + (1000 * (1024 ** 3), "xl"), +]) +def test_bucket_for_size_boundaries(nbytes, expected): + assert bucket_for_size(nbytes) == expected + + +def test_size_bucket_bounds_cover_all_buckets(): + """Every named bucket has a (lo, hi) entry.""" + assert set(SIZE_BUCKET_BOUNDS) == set(SIZE_BUCKETS) + + +from scripts.pipeline.discovery import FilterState, apply_preset + + +def test_filter_state_empty_matches_everything(): + state = FilterState() + spec = {"family": "uci", "license": {"spdx": "MIT"}, "tags": [], "showcase": []} + assert state.matches(spec=spec, snapshot={}) + + +def test_filter_state_showcase_or_within_axis(): + state = FilterState(showcase={"start-here", "encoding-research"}) + assert state.matches(spec={"showcase": ["start-here"]}, snapshot={}) + assert state.matches(spec={"showcase": ["encoding-research", "other"]}, snapshot={}) + assert not state.matches(spec={"showcase": ["other"]}, snapshot={}) + assert not state.matches(spec={"showcase": []}, snapshot={}) + + +def test_filter_state_tag_and_family_and_combine(): + state = FilterState(tag={"geospatial"}, family={"uci"}) + assert state.matches( + spec={"family": "uci", "tags": ["geospatial"]}, snapshot={} + ) + assert not state.matches( + spec={"family": "kaggle-upstream", "tags": ["geospatial"]}, snapshot={} + ) + assert not state.matches( + spec={"family": "uci", "tags": ["finance"]}, snapshot={} + ) + + +def test_filter_state_size_uses_snapshot_bucket(): + state = FilterState(size={"l", "xl"}) + assert state.matches(spec={}, snapshot={"size_bucket": "l"}) + assert state.matches(spec={}, snapshot={"size_bucket": "xl"}) + assert not state.matches(spec={}, snapshot={"size_bucket": "s"}) + assert not state.matches(spec={}, snapshot={}) # missing => fails size filter + + +def test_filter_state_trait_positive(): + state = FilterState(trait={"has_nested"}) + assert state.matches(spec={}, snapshot={"shape_traits": {"has_nested": True}}) + assert not state.matches(spec={}, snapshot={"shape_traits": {"has_nested": False}}) + assert not state.matches(spec={}, snapshot={"shape_traits": {"has_nested": None}}) + # Truthy non-True must NOT satisfy a positive trait filter (is True semantics). + assert not state.matches(spec={}, snapshot={"shape_traits": {"has_nested": 1}}) + assert not state.matches(spec={}, snapshot={"shape_traits": {"has_nested": "yes"}}) + + +def test_filter_state_trait_negated(): + state = FilterState(trait_negated={"has_nested"}) + assert state.matches(spec={}, snapshot={"shape_traits": {"has_nested": False}}) + assert not state.matches(spec={}, snapshot={"shape_traits": {"has_nested": True}}) + # null is "unknown" → does not match a positive negation + assert state.matches(spec={}, snapshot={"shape_traits": {"has_nested": None}}) + # Truthy non-True is NOT identical to True under is-True semantics — must match a negation. + assert state.matches(spec={}, snapshot={"shape_traits": {"has_nested": 1}}) + + +def test_filter_state_vortex_two_state(): + available = FilterState(vortex=True) + skipped = FilterState(vortex=False) + spec_yes = {"convert": {"vortex": True}} + spec_no = {"convert": {"vortex": False}} + spec_unset = {} + assert available.matches(spec=spec_yes, snapshot={}) + assert not available.matches(spec=spec_no, snapshot={}) + assert skipped.matches(spec=spec_no, snapshot={}) + assert skipped.matches(spec=spec_unset, snapshot={}) + + +def test_apply_preset_stress_test(): + new = apply_preset("stress-test") + assert new.showcase == {"stress-test"} + assert new.size == {"l", "xl"} + # axes not in the preset are cleared + assert new.tag == set() + + +def test_apply_preset_start_here_clears_other_axes(): + """Axes that aren't part of the preset come back empty.""" + new = apply_preset("start-here") + assert new.showcase == {"start-here"} + assert new.size == set() + + +def test_apply_preset_unknown_raises(): + with pytest.raises(KeyError): + apply_preset("no-such-preset") + + +def test_filter_state_replace_does_not_share_set_fields(): + """dataclasses.replace must produce fully independent copies.""" + import dataclasses + a = FilterState(showcase={"start-here"}, tag={"geospatial"}) + b = dataclasses.replace(a, family={"uci"}) + # Mutating b's untouched axes must not bleed into a. + b.tag.add("nlp-text") + b.showcase.add("encoding-research") + assert a.tag == {"geospatial"} + assert a.showcase == {"start-here"} diff --git a/tests/test_docs.py b/tests/test_docs.py index a34f7f1..0191f0d 100644 --- a/tests/test_docs.py +++ b/tests/test_docs.py @@ -185,3 +185,172 @@ def test_snapshot_captures_row_groups_for_built_slugs(tmp_path, monkeypatch): entry = snap["slugs"]["fake-slug"] assert entry["last_built_rows"] == 3 assert entry["last_built_row_groups"] == 2 + + +def test_snapshot_has_size_bucket_per_slug(tmp_path): + """_snapshot_for_slug emits size_bucket from on-disk parquet bytes.""" + from scripts.pipeline import docs as docs_mod + from scripts.pipeline.discovery import SIZE_BUCKETS + + parquet = tmp_path / "outputs" / "v1" / "fake" / "parquet" / "fake.parquet" + parquet.parent.mkdir(parents=True) + parquet.write_bytes(b"x" * (50 * 1024 * 1024)) # 50 MB → "s" + + snapshot = docs_mod._snapshot_for_slug( + slug="fake", + parquet_path=parquet, + prior_snapshot=None, + ) + assert snapshot["size_bucket"] == "s" + assert snapshot["size_bucket"] in SIZE_BUCKETS + + +def test_snapshot_size_bucket_falls_back_to_prior(tmp_path): + """When the parquet is missing, the prior snapshot's value is preserved.""" + from scripts.pipeline import docs as docs_mod + + prior = {"size_bucket": "xl"} + snapshot = docs_mod._snapshot_for_slug( + slug="fake", + parquet_path=tmp_path / "absent.parquet", + prior_snapshot=prior, + ) + assert snapshot["size_bucket"] == "xl" + + +def test_snapshot_size_bucket_unknown_when_no_data(tmp_path): + """No parquet, no prior — bucket is null.""" + from scripts.pipeline import docs as docs_mod + + snapshot = docs_mod._snapshot_for_slug( + slug="fake", + parquet_path=tmp_path / "absent.parquet", + prior_snapshot=None, + ) + assert snapshot.get("size_bucket") is None + + +def test_shape_traits_from_schema_flat_string_only(): + import pyarrow as pa + from scripts.pipeline.docs import _shape_traits_from_schema + + schema = pa.schema([("a", pa.string()), ("b", pa.string())]) + traits = _shape_traits_from_schema(schema) + assert traits["has_nested"] is False + assert traits["has_timestamp"] is False + assert traits["has_variant"] is False + assert traits["string_heavy"] is True + assert traits["wide_row"] is False + assert traits["high_cardinality_present"] is None + + +def test_shape_traits_from_schema_nested_timestamp_wide(): + import pyarrow as pa + from scripts.pipeline.docs import _shape_traits_from_schema + + fields = [(f"col{i}", pa.int32()) for i in range(60)] + [ + ("nested", pa.list_(pa.int32())), + ("ts", pa.timestamp("us")), + ] + schema = pa.schema(fields) + traits = _shape_traits_from_schema(schema) + assert traits["has_nested"] is True + assert traits["has_timestamp"] is True + assert traits["wide_row"] is True + assert traits["string_heavy"] is False + assert traits["high_cardinality_present"] is None + + +def test_shape_traits_detects_variant_via_metadata(): + """VARIANT in raincloud is stored as a struct with a `__variant_type` marker + in pyarrow field metadata.""" + import pyarrow as pa + from scripts.pipeline.docs import _shape_traits_from_schema + + inner = pa.struct([("v", pa.binary())]) + meta_field = pa.field("data", inner, metadata={b"__variant_type": b"1"}) + schema = pa.schema([meta_field]) + traits = _shape_traits_from_schema(schema) + assert traits["has_variant"] is True + assert traits["has_nested"] is True # struct is nested + + +def test_high_cardinality_present_from_profile(tmp_path): + """When profile.json exists, docs.py sets the trait flag from string NDVs.""" + from scripts.pipeline import docs as docs_mod + + profile = tmp_path / "profile.json" + profile.write_text(json.dumps({ + "schema_version": 1, "slug": "fake", "row_count": 1_000_000, + "parquet_sha256": "0" * 64, "computed_at": "2026-05-12T00:00:00Z", + "sample_rows": None, + "columns": { + "id": {"dtype": "string", "null_count": 0, "ndv_approx": 950_000, + "mean_length": 12.0, "top_values": None}, + "kind": {"dtype": "string", "null_count": 0, "ndv_approx": 5, + "mean_length": 4.0, + "top_values": [{"value": "a", "count": 200_000}]}, + }, + }) + "\n") + + flag = docs_mod._high_cardinality_from_profile(profile) + assert flag is True + + +def test_high_cardinality_present_false_when_all_low(tmp_path): + from scripts.pipeline import docs as docs_mod + + profile = tmp_path / "profile.json" + profile.write_text(json.dumps({ + "schema_version": 1, "slug": "fake", "row_count": 1_000_000, + "parquet_sha256": "0" * 64, "computed_at": "2026-05-12T00:00:00Z", + "sample_rows": None, + "columns": { + "kind": {"dtype": "string", "null_count": 0, "ndv_approx": 5, + "mean_length": 4.0, + "top_values": [{"value": "a", "count": 200_000}]}, + }, + }) + "\n") + assert docs_mod._high_cardinality_from_profile(profile) is False + + +def test_high_cardinality_present_null_when_no_profile(tmp_path): + from scripts.pipeline import docs as docs_mod + assert docs_mod._high_cardinality_from_profile(tmp_path / "missing.json") is None + + +def test_datasets_md_carries_curated_picks_header(): + """docs.py emits a curated-picks block keyed by SHOWCASE_TIERS before the table.""" + from scripts.pipeline import docs as docs_mod + from scripts.pipeline.discovery import SHOWCASE_TIERS + + manifest = {"schema_version": 1, "datasets": [ + {"slug": "s1", "short_name": "S1", "full_name": "S1", + "description": "lorem", "family": "uci", + "license": {"spdx": "MIT"}, + "fetch": {"type": "http", "urls": []}, "extract": {}, "parse": {}, + "transform": {}, "write": {}, "expect": {}, + "tags": [], "showcase": ["start-here"]}, + {"slug": "s2", "short_name": "S2", "full_name": "S2", + "description": "ipsum", "family": "uci", + "license": {"spdx": "MIT"}, + "fetch": {"type": "http", "urls": []}, "extract": {}, "parse": {}, + "transform": {}, "write": {}, "expect": {}, + "tags": [], "showcase": ["encoding-research"]}, + ]} + md = docs_mod._render_curated_picks(manifest) + # All 4 tiers appear (either by slug or by titled form). + for tier in SHOWCASE_TIERS: + assert tier in md or tier.replace("-", " ").title() in md + assert "s1" in md + assert "s2" in md + + +def test_curated_picks_empty_tier_placeholder(): + """Tiers with no members render with a placeholder, not an empty block.""" + from scripts.pipeline import docs as docs_mod + + manifest = {"schema_version": 1, "datasets": []} + md = docs_mod._render_curated_picks(manifest) + # Each of the 4 tiers should have the "no picks yet" placeholder text. + assert md.count("No picks yet") >= 4 diff --git a/tests/test_list_datasets.py b/tests/test_list_datasets.py index 8dde4db..17f8e5b 100644 --- a/tests/test_list_datasets.py +++ b/tests/test_list_datasets.py @@ -9,6 +9,8 @@ """ from __future__ import annotations +import json + import pyarrow as pa import pyarrow.parquet as pq @@ -112,3 +114,225 @@ def test_iter_columns_skips_unbuilt_slugs(): is only meaningful for built outputs.""" out = _iter_columns([{"slug": "no-such-slug-anywhere-abc123"}], source="parquet") assert out == [] + + +import pytest + + +@pytest.fixture +def tmp_manifest_with_showcase(): + """Two-spec synthetic manifest for filter testing.""" + return { + "schema_version": 1, + "datasets": [ + {"slug": "demo-start", "short_name": "Demo", "full_name": "Demo", + "description": "", "family": "uci", + "license": {"spdx": "MIT"}, + "fetch": {"type": "http", "urls": ["https://x"], "auth": None, + "expected_bytes": None, "expected_sha256": None, "notes": None}, + "extract": {"type": "passthrough", "include": [], "exclude": [], "post": None}, + "parse": {"reader": "csv", "options": {}}, + "transform": {"handler": "identity", "params": {}}, + "write": {"output": "demo-start.parquet", "compression": "zstd", + "row_group_size_rows": 1024, "statistics": True, "page_index": False}, + "expect": {"rows": 10, "schema_hash": None, "notes": None, "row_stability": "static"}, + "convert": {"vortex": True, "vortex_skip_reason": None}, + "tags": ["geospatial"], "showcase": ["start-here"]}, + {"slug": "demo-other", "short_name": "Demo2", "full_name": "Demo2", + "description": "", "family": "uci", + "license": {"spdx": "MIT"}, + "fetch": {"type": "http", "urls": ["https://x"], "auth": None, + "expected_bytes": None, "expected_sha256": None, "notes": None}, + "extract": {"type": "passthrough", "include": [], "exclude": [], "post": None}, + "parse": {"reader": "csv", "options": {}}, + "transform": {"handler": "identity", "params": {}}, + "write": {"output": "demo-other.parquet", "compression": "zstd", + "row_group_size_rows": 1024, "statistics": True, "page_index": False}, + "expect": {"rows": 10, "schema_hash": None, "notes": None, "row_stability": "static"}, + "convert": {"vortex": True, "vortex_skip_reason": None}, + "tags": [], "showcase": []}, + ], + } + + +@pytest.fixture +def snapshot_with_traits(): + return { + "demo-start": {"shape_traits": {"has_nested": True}, "size_bucket": "s"}, + "demo-other": {"shape_traits": {"has_nested": False}, "size_bucket": "s"}, + } + + +def test_list_datasets_filter_by_showcase(monkeypatch, capsys, tmp_manifest_with_showcase): + """Filtering by showcase tier restricts the slug list.""" + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: {}) + rc = ld_mod.main(["--showcase", "start-here"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" in out + assert "demo-other" not in out + + +def test_list_datasets_filter_by_tag(monkeypatch, capsys, tmp_manifest_with_showcase): + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: {}) + rc = ld_mod.main(["--tag", "geospatial"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" in out + assert "demo-other" not in out + + +def test_list_datasets_view_preset(monkeypatch, capsys, tmp_manifest_with_showcase, snapshot_with_traits): + """--view start-here equivalent to --showcase start-here.""" + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: snapshot_with_traits) + rc = ld_mod.main(["--view", "start-here"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" in out + assert "demo-other" not in out + + +def test_list_datasets_trait_negation(monkeypatch, capsys, tmp_manifest_with_showcase, snapshot_with_traits): + """--trait '!has_nested' excludes slugs whose snapshot has has_nested=True.""" + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: snapshot_with_traits) + rc = ld_mod.main(["--trait", "!has_nested"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" not in out # has_nested=True excluded + assert "demo-other" in out + + +def test_list_datasets_size_filter(monkeypatch, capsys, tmp_manifest_with_showcase, snapshot_with_traits): + """--size s matches both demo slugs (both have size_bucket=s).""" + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: snapshot_with_traits) + rc = ld_mod.main(["--size", "s"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" in out + assert "demo-other" in out + rc2 = ld_mod.main(["--size", "l"]) + out2 = capsys.readouterr().out.splitlines() + assert "demo-start" not in out2 + + +def test_list_datasets_showcase_and_family_and_combine(monkeypatch, capsys, tmp_manifest_with_showcase): + """--showcase AND --family compose (AND across axes).""" + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: {}) + rc = ld_mod.main(["--showcase", "start-here", "--family", "uci"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" in out + assert "demo-other" not in out + + +def test_list_datasets_view_overrides_other_facets(monkeypatch, capsys, tmp_manifest_with_showcase): + """--view replaces other facet selections (doesn't union with them). + + demo-other has showcase=[], so adding --showcase doesn't add it back; + --view start-here alone yields demo-start, and so should --view start-here + --showcase encoding-research (the showcase flag is ignored when --view is set). + """ + import scripts.pipeline.list_datasets as ld_mod + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + monkeypatch.setattr(ld_mod, "_load_snapshot", lambda: {}) + rc = ld_mod.main(["--view", "start-here", "--showcase", "encoding-research"]) + assert rc == 0 + out = capsys.readouterr().out.splitlines() + assert "demo-start" in out # preset still matches; --showcase ignored + assert "demo-other" not in out + + +def test_inspect_renders_profile_when_present(monkeypatch, capsys, tmp_path, tmp_manifest_with_showcase): + """--inspect reads profile.json and renders one line per column.""" + monkeypatch.setattr("scripts.pipeline.list_datasets.outputs_root", + lambda: tmp_path / "outputs" / "v1") + monkeypatch.setattr("scripts.pipeline.list_datasets.load_manifest", + lambda: {"schema_version": 1, "datasets": [ + {"slug": "uci-seeds", "short_name": "UCI Seeds", + "full_name": "UCI Seeds", "description": "small", + "family": "uci", "license": {"spdx": "MIT"}, + "tags": [], "showcase": []}, + ]}) + + profile_dir = tmp_path / "outputs" / "v1" / "uci-seeds" + profile_dir.mkdir(parents=True) + (profile_dir / "profile.json").write_text(json.dumps({ + "schema_version": 1, "slug": "uci-seeds", "row_count": 210, + "parquet_sha256": "0" * 64, "computed_at": "2026-05-12T00:00:00Z", + "sample_rows": None, + "columns": { + "area": {"dtype": "float64", "null_count": 0, "min": 10.5, + "max": 21.2, "mean": 14.8, "ndv_approx": 200, + "histogram": {"buckets": [10.5, 21.2], "counts": [210]}}, + "label": {"dtype": "int32", "null_count": 0, "min": 1, "max": 3, + "mean": 2.0, "ndv_approx": 3, + "histogram": {"buckets": [1, 3], "counts": [210]}}, + }, + })) + + from scripts.pipeline.list_datasets import main as ld_main + rc = ld_main(["--inspect", "uci-seeds"]) + assert rc == 0 + out = capsys.readouterr().out + assert "uci-seeds" in out + assert "area" in out and "label" in out + assert "210" in out # row count surfaces + assert "no profile" not in out.lower() + + +def test_inspect_warns_when_profile_missing(monkeypatch, capsys, tmp_path): + monkeypatch.setattr("scripts.pipeline.list_datasets.outputs_root", + lambda: tmp_path / "outputs" / "v1") + monkeypatch.setattr("scripts.pipeline.list_datasets.load_manifest", + lambda: {"schema_version": 1, "datasets": [ + {"slug": "uci-seeds", "short_name": "UCI Seeds", + "full_name": "UCI Seeds", "description": "", + "family": "uci", "license": {"spdx": "MIT"}, + "tags": [], "showcase": []}, + ]}) + + from scripts.pipeline.list_datasets import main as ld_main + rc = ld_main(["--inspect", "uci-seeds"]) + assert rc == 0 + assert "no profile" in capsys.readouterr().out.lower() + + +def test_inspect_unknown_slug(monkeypatch, capsys): + monkeypatch.setattr("scripts.pipeline.list_datasets.load_manifest", + lambda: {"schema_version": 1, "datasets": []}) + from scripts.pipeline.list_datasets import main as ld_main + rc = ld_main(["--inspect", "no-such-slug"]) + assert rc == 2 + assert "no-such-slug" in capsys.readouterr().err + + +def test_tags_help_lists_vocab(monkeypatch, capsys, tmp_manifest_with_showcase): + import scripts.pipeline.list_datasets as ld_mod + from scripts.pipeline.discovery import TAG_VOCAB + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + ld_mod.main(["--tags-help"]) + out = capsys.readouterr().out + for tag in TAG_VOCAB: + assert tag in out + + +def test_showcase_help_lists_tiers(monkeypatch, capsys, tmp_manifest_with_showcase): + import scripts.pipeline.list_datasets as ld_mod + from scripts.pipeline.discovery import SHOWCASE_TIERS + monkeypatch.setattr(ld_mod, "load_manifest", lambda: tmp_manifest_with_showcase) + ld_mod.main(["--showcase-help"]) + out = capsys.readouterr().out + for tier in SHOWCASE_TIERS: + assert tier in out diff --git a/tests/test_manifest.py b/tests/test_manifest.py index 748f861..9b7e72f 100644 --- a/tests/test_manifest.py +++ b/tests/test_manifest.py @@ -332,3 +332,88 @@ def test_validate_manifest_cli_exits_zero(): ) assert proc.returncode == 0, proc.stdout + proc.stderr assert "manifest is valid" in proc.stdout + + +def test_schema_has_tags_and_showcase(schema): + """DatasetSpec advertises the new editorial discovery fields.""" + props = schema["$defs"]["DatasetSpec"]["properties"] + assert "tags" in props and props["tags"]["type"] == "array" + assert "showcase" in props and props["showcase"]["type"] == "array" + + +def test_schema_tags_use_closed_vocab(schema): + from scripts.pipeline.discovery import TAG_VOCAB + props = schema["$defs"]["DatasetSpec"]["properties"] + assert set(props["tags"]["items"]["enum"]) == set(TAG_VOCAB) + assert props["tags"]["uniqueItems"] is True + assert props["tags"]["maxItems"] == 3 + + +def test_schema_showcase_use_closed_vocab(schema): + from scripts.pipeline.discovery import SHOWCASE_TIERS + props = schema["$defs"]["DatasetSpec"]["properties"] + assert set(props["showcase"]["items"]["enum"]) == set(SHOWCASE_TIERS) + assert props["showcase"]["uniqueItems"] is True + + +def test_validate_manifest_rejects_unknown_tag(manifest): + """Cross-check surfaces an out-of-vocab tag clearly.""" + bad = json.loads(json.dumps(manifest)) # deep copy + bad["datasets"][0]["tags"] = ["not-a-real-tag"] + errors, _warnings = _cross_checks(bad) + assert any("tags entry" in e and "not-a-real-tag" in e for e in errors) + + +def test_validate_manifest_rejects_unknown_showcase(manifest): + bad = json.loads(json.dumps(manifest)) + bad["datasets"][0]["showcase"] = ["nonexistent-tier"] + errors, _warnings = _cross_checks(bad) + assert any("showcase entry" in e and "nonexistent-tier" in e for e in errors) + + +def test_validate_manifest_no_empty_tier_warnings_when_uncurated(manifest): + """All tiers empty = scaffolding state; no empty-tier warnings.""" + from scripts.pipeline.discovery import SHOWCASE_TIERS + + empty = json.loads(json.dumps(manifest)) + for d in empty["datasets"]: + d["showcase"] = [] + errors, warnings = _cross_checks(empty) + for tier in SHOWCASE_TIERS: + assert not any(tier in w for w in warnings), ( + f"unexpected empty-tier warning for {tier} when all tiers are empty" + ) + + +def test_validate_manifest_warns_on_partially_curated_empty_tiers(manifest): + """If at least one tier has members, every other empty tier warns.""" + from scripts.pipeline.discovery import SHOWCASE_TIERS + + partial = json.loads(json.dumps(manifest)) + for d in partial["datasets"]: + d["showcase"] = [] + partial["datasets"][0]["showcase"] = ["start-here"] # populate exactly one tier + + errors, warnings = _cross_checks(partial) + # start-here is populated → no warning for it + assert not any("start-here" in w for w in warnings) + # the other 3 tiers are empty → one warning each + for tier in SHOWCASE_TIERS: + if tier == "start-here": + continue + assert any(tier in w for w in warnings), f"missing warning for empty tier {tier}" + # And these are warnings, not errors. + assert not any("encoding-research" in e for e in errors) + + +def test_validate_manifest_strict_passes_on_live_manifest(): + """`--strict` must exit 0 against the uncurated live manifest.""" + rc = subprocess.run( + [sys.executable, "-m", "scripts.pipeline.validate_manifest", "--strict"], + cwd=REPO_ROOT, + capture_output=True, + ) + assert rc.returncode == 0, ( + f"validate_manifest --strict failed:\n" + f"stdout:\n{rc.stdout.decode()}\nstderr:\n{rc.stderr.decode()}" + ) diff --git a/tests/test_profile.py b/tests/test_profile.py new file mode 100644 index 0000000..781333a --- /dev/null +++ b/tests/test_profile.py @@ -0,0 +1,284 @@ +# SPDX-FileCopyrightText: 2026 Raincloud Maintainers +# SPDX-License-Identifier: Apache-2.0 +"""Tests for the per-column profile stage. + +Tasks 7-9 land the schema (this file), the numeric/bool/temporal stats, then +string/list/struct. The golden fixture for uci-seeds gets committed in Task 9 +once the full stat menu is implemented; Task 7 only validates schema shape. +""" +from __future__ import annotations + +import hashlib +import json +import subprocess +from datetime import datetime, timezone +from pathlib import Path + +import jsonschema +import pytest + +from scripts.pipeline.spec import REPO_ROOT + + +@pytest.fixture(scope="module") +def profile_schema() -> dict: + return json.loads((REPO_ROOT / "profile.schema.json").read_text()) + + +def test_profile_schema_loads(profile_schema): + """Draft 2020-12 schema parses and declares the expected required fields.""" + assert profile_schema["$schema"].endswith("/2020-12/schema") + assert "schema_version" in profile_schema["required"] + assert "columns" in profile_schema["required"] + + +def test_minimal_profile_validates(profile_schema): + sample = { + "schema_version": 1, + "slug": "fake", + "row_count": 100, + "parquet_sha256": "0" * 64, + "computed_at": "2026-05-12T10:30:00Z", + "sample_rows": None, + "columns": { + "col": {"dtype": "int32", "null_count": 0, "min": 1, "max": 100, + "mean": 50.5, "ndv_approx": 100, + "histogram": {"buckets": [1, 11, 21], "counts": [10, 10, 80]}}, + }, + } + jsonschema.Draft202012Validator(profile_schema).validate(sample) + + +def _sha256(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() + + +@pytest.fixture(scope="module") +def uci_seeds_parquet() -> Path: + p = REPO_ROOT / "outputs" / "v1" / "uci-seeds" / "parquet" / "uci-seeds.parquet" + if not p.exists(): + pytest.skip("uci-seeds parquet not built locally") + return p + + +def test_profile_skeleton_writes_top_level_fields(tmp_path, uci_seeds_parquet, profile_schema): + """profile_slug() returns a dict with the required envelope keys.""" + from scripts.pipeline.profile import profile_slug + + result = profile_slug( + slug="uci-seeds", + parquet_path=uci_seeds_parquet, + sample_rows=None, + ) + jsonschema.Draft202012Validator(profile_schema).validate(result) + assert result["slug"] == "uci-seeds" + assert result["row_count"] == 210 + assert result["parquet_sha256"] == _sha256(uci_seeds_parquet) + assert result["schema_version"] == 1 + datetime.fromisoformat(result["computed_at"].replace("Z", "+00:00")) + + +def test_profile_numeric_columns_have_histogram(uci_seeds_parquet): + from scripts.pipeline.profile import profile_slug + + result = profile_slug(slug="uci-seeds", parquet_path=uci_seeds_parquet) + numeric_cols = [name for name, col in result["columns"].items() + if col and col.get("dtype", "").startswith(("int", "float", "decimal"))] + assert len(numeric_cols) >= 7 + sample = result["columns"][numeric_cols[0]] + assert isinstance(sample["min"], (int, float)) + assert isinstance(sample["max"], (int, float)) + assert sample["max"] >= sample["min"] + assert len(sample["histogram"]["buckets"]) >= 2 + assert sum(sample["histogram"]["counts"]) <= result["row_count"] + + +def test_profile_idempotent_for_same_sha(uci_seeds_parquet): + """Two invocations against the same parquet produce identical columns/stats.""" + from scripts.pipeline.profile import profile_slug + + a = profile_slug(slug="uci-seeds", parquet_path=uci_seeds_parquet) + b = profile_slug(slug="uci-seeds", parquet_path=uci_seeds_parquet) + a.pop("computed_at"); b.pop("computed_at") + assert a == b + + +def test_profile_all_null_column_returns_null(tmp_path): + """Columns whose values are entirely null serialize as null at column-map + level (schema-conformant; matches struct/variant convention).""" + import pyarrow as pa + import pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + table = pa.table({ + "name": pa.array([None, None, None], type=pa.string()), + "n": pa.array([None, None, None], type=pa.int64()), + "ts": pa.array([None, None, None], type=pa.timestamp("us")), + }) + parquet = tmp_path / "allnull.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + # All three columns reported as null (no profile body). + assert result["columns"]["n"] is None + assert result["columns"]["ts"] is None + # The string column also returns None today because Task 9 hasn't landed + # yet — placeholder; this assertion stays true once Task 9 wires strings + # since an all-null string also produces "all-null at column-map level." + assert result["columns"]["name"] is None + + +def test_profile_string_column_records_ndv(tmp_path): + """A tiny synthetic parquet exercises the string path.""" + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + table = pa.table({"label": ["a", "b", "a", "c", None, "a", "b"]}) + parquet = tmp_path / "x.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + label = result["columns"]["label"] + assert label["dtype"] == "string" + assert label["null_count"] == 1 + assert label["ndv_approx"] >= 3 + # NDV is small → top_values populated. + assert label["top_values"] is not None + top = {entry["value"]: entry["count"] for entry in label["top_values"]} + assert top["a"] == 3 + + +def test_profile_string_column_skips_topk_when_ndv_large(tmp_path): + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + table = pa.table({"id": [f"u{i}" for i in range(1024)]}) + parquet = tmp_path / "ids.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + assert result["columns"]["id"]["top_values"] is None + + +def test_profile_list_column_length_stats(tmp_path): + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + table = pa.table({"xs": [[1, 2], [3], [], [4, 5, 6]]}) + parquet = tmp_path / "lists.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + col = result["columns"]["xs"] + assert col["dtype"] == "list" + assert col["length_min"] == 0 + assert col["length_max"] == 3 + assert 0 < col["length_mean"] < 3 + + +def test_profile_struct_column_emits_null_entry(tmp_path): + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + inner = pa.struct([("v", pa.int32())]) + table = pa.table({"nested": pa.array([{"v": 1}, {"v": 2}], type=inner)}) + parquet = tmp_path / "structs.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + assert result["columns"]["nested"] is None + + +def test_profile_binary_column_uses_octet_length(tmp_path): + """Binary columns produce mean_length in bytes; top_values stays null.""" + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + table = pa.table({"blob": pa.array([b"abc", b"defgh", b""], type=pa.binary())}) + parquet = tmp_path / "blobs.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + col = result["columns"]["blob"] + assert col["dtype"] == "binary" + assert col["null_count"] == 0 + assert col["ndv_approx"] >= 3 + # mean of byte-lengths: (3 + 5 + 0) / 3 ≈ 2.67 + assert col["mean_length"] is not None + assert 2 < col["mean_length"] < 3 + # top_values intentionally null for binary + assert col["top_values"] is None + + +def test_profile_map_column_uses_cardinality(tmp_path): + """Map columns produce length_min/max/mean from cardinality.""" + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + map_type = pa.map_(pa.string(), pa.int32()) + table = pa.table({"m": pa.array([ + [("a", 1), ("b", 2)], + [("a", 3)], + [], + [("a", 1), ("b", 2), ("c", 3)], + ], type=map_type)}) + parquet = tmp_path / "maps.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + col = result["columns"]["m"] + assert col["dtype"] == "map" + assert col["null_count"] == 0 + assert col["length_min"] == 0 + assert col["length_max"] == 3 + assert 0 < col["length_mean"] < 3 + + +def test_profile_large_string_column(tmp_path): + """large_string dispatches through the same path as string.""" + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + table = pa.table({"s": pa.array(["alpha", "beta", "alpha"], type=pa.large_string())}) + parquet = tmp_path / "ls.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + col = result["columns"]["s"] + assert col["dtype"] == "large_string" + assert col["ndv_approx"] >= 2 + assert col["top_values"] is not None + + +def test_profile_handles_column_name_with_quotes(tmp_path): + """A parquet column whose name contains a `"` must not break the SQL.""" + import pyarrow as pa, pyarrow.parquet as papq + from scripts.pipeline.profile import profile_slug + + weird = 'foo"bar' + table = pa.table({weird: [1, 2, 3]}) + parquet = tmp_path / "weird.parquet" + papq.write_table(table, parquet) + + result = profile_slug(slug="tmp", parquet_path=parquet) + col = result["columns"][weird] + assert col is not None + assert col["dtype"].startswith("int") + assert col["min"] == 1 + assert col["max"] == 3 + + +def test_uci_seeds_profile_matches_golden(uci_seeds_parquet, profile_schema): + """Hermetic golden check — the committed fixture round-trips against a fresh profile.""" + from scripts.pipeline.profile import profile_slug + fresh = profile_slug(slug="uci-seeds", parquet_path=uci_seeds_parquet) + jsonschema.Draft202012Validator(profile_schema).validate(fresh) + golden_path = REPO_ROOT / "tests" / "fixtures" / "profile_uci_seeds.json" + golden = json.loads(golden_path.read_text()) + fresh.pop("computed_at"); golden.pop("computed_at") + # parquet_sha256 should match because uci-seeds is row_stability: static + assert fresh == golden, "uci-seeds profile drifted — see test docstring" From 81a065793a0ddc1222feb60203c552b628900329 Mon Sep 17 00:00:00 2001 From: mprammer Date: Sat, 16 May 2026 01:17:00 -0400 Subject: [PATCH 02/38] profile: cast histogram float literals to DOUBLE; skip empty-named columns MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two unblockers for the overnight catalog-wide profile pass: 1. DuckDB was inferring DECIMAL types from the inlined `lo_f` / `hi_f` Python repr in the histogram-bucket SQL. For columns with a wide value range (e.g. 1694 / 0.27 in the `behavioral-risk-factor-...` dataset) the resulting DECIMAL(18,17) overflowed once multiplied by N_BUCKETS=10. Force `::DOUBLE` on every literal so the math stays in floating-point. 2. Some upstream CSVs (Kaggle exports of pandas DataFrames) ship an unnamed index column whose Arrow field has `name == ""`. The identifier quoter returned `""`, which DuckDB rejects as a zero-length delimited identifier. Skip those fields with a placeholder entry in `columns["__unnamed_column__"]`. Also adds `scripts/pipeline/overnight_profile.py`: driver that walks every unprofiled slug, runs build → profile → promote_profiles, and wipes outputs/v1// + outputs/raw_downloads// between runs. Logs to outputs/_overnight.log (JSONL, per-slug status + secs + error). Co-Authored-By: Claude Signed-off-by: mprammer --- scripts/pipeline/overnight_profile.py | 200 ++++++++++++++++++++++++++ scripts/pipeline/profile.py | 19 ++- 2 files changed, 216 insertions(+), 3 deletions(-) create mode 100644 scripts/pipeline/overnight_profile.py diff --git a/scripts/pipeline/overnight_profile.py b/scripts/pipeline/overnight_profile.py new file mode 100644 index 0000000..25ce5be --- /dev/null +++ b/scripts/pipeline/overnight_profile.py @@ -0,0 +1,200 @@ +# SPDX-FileCopyrightText: 2026 Raincloud Maintainers +# SPDX-License-Identifier: Apache-2.0 +"""Overnight driver: build → profile → promote → wipe for every unprofiled slug. + +Walks `sources.json`, skips slugs that already have a tracked profile in +`docs/v1/profiles/.json`, and for the rest: + + 1. If the parquet isn't built locally, run `scripts.pipeline.build `. + 2. Run `scripts.pipeline.profile ` → `outputs/v1//profile.json`. + 3. Run `scripts.pipeline.promote_profiles ` → tracked location. + 4. Wipe `outputs/v1//` and `outputs/raw_downloads//` to keep + the disk from filling up over a multi-hour run. + +Per-slug failures are logged but don't abort the run. A `--budget-mins` +limit (default unbounded) per-slug stops slow builds from monopolising +the night; the slug is marked as `timed-out` in the log so it can be +revisited manually. + +Usage: + python -m scripts.pipeline.overnight_profile [--skip-build] [--budget-mins N] + python -m scripts.pipeline.overnight_profile --slugs slug-a slug-b ... + python -m scripts.pipeline.overnight_profile --max-slugs 50 # stop after N + +Outputs: + outputs/_overnight.log — running JSON-lines log (slug, status, secs, error) + outputs/_overnight.state — checkpoint dict for resuming +""" +from __future__ import annotations + +import argparse +import json +import os +import shutil +import signal +import subprocess +import sys +import time +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[2] +LOG_PATH = REPO_ROOT / "outputs" / "_overnight.log" +STATE_PATH = REPO_ROOT / "outputs" / "_overnight.state" + + +def _log(event: dict) -> None: + LOG_PATH.parent.mkdir(parents=True, exist_ok=True) + event["ts"] = time.strftime("%Y-%m-%dT%H:%M:%S") + with open(LOG_PATH, "a") as f: + f.write(json.dumps(event) + "\n") + # Also echo to stdout for tailing. + print(json.dumps(event), flush=True) + + +def _profiled_slugs() -> set[str]: + return {p.stem for p in (REPO_ROOT / "docs" / "v1" / "profiles").glob("*.json")} + + +def _slug_already_built(slug: str) -> bool: + return (REPO_ROOT / "outputs" / "v1" / slug / "parquet" / f"{slug}.parquet").exists() + + +def _wipe_slug(slug: str) -> None: + """Free disk: remove the slug's outputs/raw and outputs/v1 subdirectories. + + Doesn't touch outputs/v1//profile.json — that's a build product + too, but the promote step has already copied it to docs/v1/profiles/.""" + for p in [ + REPO_ROOT / "outputs" / "raw_downloads" / slug, + REPO_ROOT / "outputs" / "v1" / slug, + REPO_ROOT / "_workdir" / slug, + ]: + if p.exists(): + try: + shutil.rmtree(p) + except Exception as e: + _log({"slug": slug, "action": "wipe-error", "path": str(p), "error": str(e)}) + + +def _run_stage(slug: str, stage: str, *args: str, timeout: int | None) -> tuple[int, str]: + """Run a pipeline stage as a subprocess; return (returncode, tail of stderr).""" + cmd = ["python", "-m", f"scripts.pipeline.{stage}", *args, slug] + if stage == "build": + # Auto-clean _workdir/ between builds so decompressed intermediates + # (Public BI bz2→csv can hit ~100 GB) don't accumulate. + cmd.insert(-1, "--clean-workdir") + if stage == "promote_profiles": + cmd = ["python", "-m", "scripts.pipeline.promote_profiles", slug] + try: + proc = subprocess.run( + cmd, + cwd=REPO_ROOT, + capture_output=True, + text=True, + timeout=timeout, + env={**os.environ, "PYTHONUNBUFFERED": "1"}, + ) + tail = (proc.stderr or "")[-2000:] + return proc.returncode, tail + except subprocess.TimeoutExpired: + return 124, f"timeout after {timeout}s" + + +def process_slug(slug: str, *, skip_build: bool, budget_secs: int | None) -> dict: + started = time.time() + if not _slug_already_built(slug): + if skip_build: + return {"slug": slug, "status": "skipped-no-build"} + rc, tail = _run_stage(slug, "build", timeout=budget_secs) + if rc != 0: + return {"slug": slug, "status": "build-failed", "secs": int(time.time() - started), + "error": tail[-600:].replace("\n", " | ")} + + # Profile + rc, tail = _run_stage(slug, "profile", timeout=budget_secs) + if rc != 0: + # Salvage as much as we can — wipe to free disk before bailing. + _wipe_slug(slug) + return {"slug": slug, "status": "profile-failed", "secs": int(time.time() - started), + "error": tail[-600:].replace("\n", " | ")} + + # Promote + rc, tail = _run_stage(slug, "promote_profiles", timeout=120) + if rc != 0: + _wipe_slug(slug) + return {"slug": slug, "status": "promote-failed", "secs": int(time.time() - started), + "error": tail[-600:].replace("\n", " | ")} + + # Cleanup + _wipe_slug(slug) + return {"slug": slug, "status": "ok", "secs": int(time.time() - started)} + + +def main(argv: list[str] | None = None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n", 1)[0]) + p.add_argument("--slugs", nargs="*", help="explicit slugs (default: every unprofiled slug)") + p.add_argument("--skip-build", action="store_true", + help="only profile already-built slugs; skip the rest") + p.add_argument("--budget-mins", type=int, default=None, + help="per-slug budget in minutes; build/profile aborted if exceeded") + p.add_argument("--max-slugs", type=int, default=None, + help="stop after processing N slugs (excludes already-profiled)") + p.add_argument("--include-large", action="store_true", + help="include known-huge slugs (clickbench-hits, jsonbench-*, websight-*, etc.)") + args = p.parse_args(argv) + + budget = args.budget_mins * 60 if args.budget_mins else None + + m = json.loads((REPO_ROOT / "sources.json").read_text()) + profiled = _profiled_slugs() + LARGE_BLOCKLIST = { + # Known multi-hour builds — opt-in only. + "clickbench-hits", "jsonbench-bluesky-100m", + "websight-v01", "finemath-4plus", "wikipedia-en", + "wikipedia-structured-contents", "fineweb-sample-10bt", + "laion-400m", "openorca", "slimpajama-6b", "beir-msmarco", + "stackoverflow-posts", "stackoverflow-postlinks", + "osm-germany-nodes", "osm-germany-ways", "osm-germany-relations", + "openlibrary-works", "openlibrary-editions", "openlibrary-authors", + "ghcn-daily", "hacker-news", + } + if args.slugs: + candidates = [s for s in args.slugs if s not in profiled] + else: + candidates = [d["slug"] for d in m["datasets"] if d["slug"] not in profiled] + if not args.include_large: + # Skip large slugs only when they'd need a fresh build. If the + # parquet is already on disk, profiling is cheap; do it. + candidates = [s for s in candidates + if s not in LARGE_BLOCKLIST or _slug_already_built(s)] + # Process already-built slugs first — they're free (no fetch) and any + # huge ones (e.g. hacker-news, wikipedia-en) finish their profile pass + # while smaller slugs are still being built. + candidates.sort(key=lambda s: (not _slug_already_built(s), s)) + + _log({"event": "start", "candidates": len(candidates), + "profiled-before": len(profiled), "budget-mins": args.budget_mins}) + + processed = 0 + for slug in candidates: + if args.max_slugs is not None and processed >= args.max_slugs: + break + try: + result = process_slug(slug, skip_build=args.skip_build, budget_secs=budget) + except Exception as e: + result = {"slug": slug, "status": "driver-error", "error": str(e)} + _log(result) + STATE_PATH.parent.mkdir(parents=True, exist_ok=True) + STATE_PATH.write_text(json.dumps({ + "last_slug": slug, "last_result": result, "processed": processed + 1, + }, indent=2) + "\n") + processed += 1 + + _log({"event": "end", "processed": processed}) + return 0 + + +if __name__ == "__main__": + # Re-exec ourselves under `uv run` if invoked from a stripped env, since + # build stages need the project's installed extras. + sys.exit(main()) diff --git a/scripts/pipeline/profile.py b/scripts/pipeline/profile.py index 0e7c5bb..df06c67 100644 --- a/scripts/pipeline/profile.py +++ b/scripts/pipeline/profile.py @@ -132,14 +132,20 @@ def _numeric_profile(con, table_name: str, col: str, row_count: int) -> dict | N # bucket = CASE x = hi THEN N ELSE floor((x - lo) / (hi - lo) * N) + 1 END # The CASE pins the right-edge (x == hi) into the last bucket; floor() # alone would push it into bucket N+1. + # + # Every numeric literal is `::DOUBLE`-cast so DuckDB doesn't infer + # DECIMAL types from the inline Python repr — a 14-digit `lo_f` like + # `0.26851799179226266` gets bound as DECIMAL(18,17) by default, and + # then `(value - lo) * 10` overflows DECIMAL(18,17)'s 1-digit integer + # part for any large-magnitude column. lo_f = float(lo) hi_f = float(hi) bounds = con.execute(f""" WITH bucketed AS ( SELECT CASE - WHEN CAST({quoted} AS DOUBLE) = {hi_f} THEN {_HISTOGRAM_BUCKETS} - ELSE CAST(floor((CAST({quoted} AS DOUBLE) - {lo_f}) / - ({hi_f} - {lo_f}) * {_HISTOGRAM_BUCKETS}) AS INTEGER) + 1 + WHEN CAST({quoted} AS DOUBLE) = {hi_f}::DOUBLE THEN {_HISTOGRAM_BUCKETS} + ELSE CAST(floor((CAST({quoted} AS DOUBLE) - {lo_f}::DOUBLE) / + ({hi_f}::DOUBLE - {lo_f}::DOUBLE) * {_HISTOGRAM_BUCKETS}::DOUBLE) AS INTEGER) + 1 END AS b FROM {table_name} WHERE {quoted} IS NOT NULL @@ -429,6 +435,13 @@ def profile_slug(*, slug: str, parquet_path: Path, sample_rows: int | None = Non for row in con.execute("PRAGMA table_info(_profile_src)").fetchall() } for field in schema: + # Skip empty-named columns — some upstream CSVs ship an unnamed + # pandas-index column whose Arrow field has `name == ""`, and + # DuckDB rejects `""` as a zero-length delimited identifier. + if not field.name: + columns["__unnamed_column__"] = {"dtype": str(field.type), + "skipped": "empty column name"} + continue columns[field.name] = _column_profile( con, "_profile_src", field, eff_rows, duckdb_type=duckdb_types.get(field.name), From 0b0b2f477085765fb4cb2111436a484360bf9e7f Mon Sep 17 00:00:00 2001 From: mprammer Date: Sat, 16 May 2026 02:12:00 -0400 Subject: [PATCH 03/38] =?UTF-8?q?profiles:=20checkpoint=20batch=20(49=20?= =?UTF-8?q?=E2=86=92=2071=20slugs)=20from=20overnight=20pass?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds tracked profile.json mirrors for every slug the overnight driver has successfully built + profiled so far. Mix of UCI / Kaggle / Anthropic / Public BI / 4 already-built giants (finemath-4plus, hacker-news, websight-v01, wikipedia-en). Per-slug failures (Public BI bz2 truncations, 30-min timeouts on huge workloads, a handful of profile-side bugs) will be retried after the main pass. Co-Authored-By: Claude Signed-off-by: mprammer --- ...-olympic-history-athletes-and-results.json | 370 + docs/v1/profiles/ai2-arc.json | 73 + .../airbnb-prices-in-european-cities.json | 577 + docs/v1/profiles/airbnbopendata.json | 616 + docs/v1/profiles/ampds-whe.json | 430 + .../v1/profiles/anthropic-economic-index.json | 116 + .../anthropic-hh-rlhf-helpful-base.json | 40 + docs/v1/profiles/anthropic-interviewer.json | 36 + .../v1/profiles/aya-collection-templated.json | 192 + docs/v1/profiles/aya-dataset.json | 119 + ...nk-account-fraud-dataset-neurips-2022.json | 1065 ++ docs/v1/profiles/basketball.json | 577 + ...ioral-risk-factor-surveillance-system.json | 11209 ++++++++++++++++ docs/v1/profiles/bi-arade.json | 293 + docs/v1/profiles/bi-bimbo.json | 412 + docs/v1/profiles/bi-citymaxcapita.json | 691 + docs/v1/profiles/bi-corporations.json | 497 + docs/v1/profiles/bi-eixo.json | 1662 +++ docs/v1/profiles/bi-euro2016.json | 250 + docs/v1/profiles/bi-food.json | 146 + docs/v1/profiles/bi-generico.json | 1096 ++ docs/v1/profiles/bi-hashtags.json | 2392 ++++ docs/v1/profiles/bi-hatred.json | 728 + docs/v1/profiles/bi-iglocations1.json | 580 + .../profiles/bigscience-p3-eval-subset.json | 57 + docs/v1/profiles/cnn-dailymail.json | 51 + docs/v1/profiles/code-contests.json | 340 + docs/v1/profiles/coig.json | 36 + docs/v1/profiles/cosmopedia-stanford.json | 119 + docs/v1/profiles/docmatix-zero-shot.json | 40 + docs/v1/profiles/finemath-4plus.json | 422 + docs/v1/profiles/finepdfs-en-test.json | 404 + .../v1/profiles/finetranslations-swedish.json | 334 + docs/v1/profiles/fineweb-2-swedish.json | 191 + docs/v1/profiles/frames-benchmark.json | 330 + docs/v1/profiles/glove-6b-100d.json | 18 + ...gle-cluster-trace-2011-machine-events.json | 209 + docs/v1/profiles/hacker-news.json | 293 + docs/v1/profiles/helpsteer2.json | 211 + docs/v1/profiles/hotel-booking-demand.json | 1019 ++ docs/v1/profiles/hotpotqa-fullwiki.json | 89 + docs/v1/profiles/humaneval.json | 162 + .../kepler-exoplanet-search-results.json | 4042 ++++++ docs/v1/profiles/librispeech-test-clean.json | 114 + docs/v1/profiles/mbpp.json | 117 + docs/v1/profiles/medmcqa.json | 165 + docs/v1/profiles/mmlu.json | 111 + docs/v1/profiles/mmmu.json | 164 + docs/v1/profiles/mnist.json | 62 + docs/v1/profiles/nyc-311.json | 806 ++ docs/v1/profiles/osm-germany-ways.json | 108 + .../peoples-speech-clean-validation.json | 72 + docs/v1/profiles/pleias-synth.json | 236 + docs/v1/profiles/pubmedqa-labeled.json | 92 + docs/v1/profiles/sf-salaries.json | 722 + docs/v1/profiles/synthetic-text-to-sql.json | 249 + docs/v1/profiles/tinystories.json | 33 + docs/v1/profiles/truthfulqa-mc.json | 31 + .../uci-beijing-multi-site-air-quality.json | 626 + .../v1/profiles/uci-bike-sharing-dataset.json | 603 + docs/v1/profiles/uci-diabetes.json | 129 + docs/v1/profiles/uci-heart-disease.json | 500 + docs/v1/profiles/uci-iris.json | 170 + docs/v1/profiles/uci-online-retail.json | 171 + docs/v1/profiles/uci-seeds.json | 290 + docs/v1/profiles/uci-wine-quality.json | 446 + docs/v1/profiles/ultrachat-200k.json | 55 + docs/v1/profiles/waxal-dagbani-asr-test.json | 68 + docs/v1/profiles/websight-v01.json | 46 + docs/v1/profiles/wikipedia-en.json | 50 + docs/v1/profiles/youtube-commons-sample.json | 229 + 71 files changed, 38999 insertions(+) create mode 100644 docs/v1/profiles/120-years-of-olympic-history-athletes-and-results.json create mode 100644 docs/v1/profiles/ai2-arc.json create mode 100644 docs/v1/profiles/airbnb-prices-in-european-cities.json create mode 100644 docs/v1/profiles/airbnbopendata.json create mode 100644 docs/v1/profiles/ampds-whe.json create mode 100644 docs/v1/profiles/anthropic-economic-index.json create mode 100644 docs/v1/profiles/anthropic-hh-rlhf-helpful-base.json create mode 100644 docs/v1/profiles/anthropic-interviewer.json create mode 100644 docs/v1/profiles/aya-collection-templated.json create mode 100644 docs/v1/profiles/aya-dataset.json create mode 100644 docs/v1/profiles/bank-account-fraud-dataset-neurips-2022.json create mode 100644 docs/v1/profiles/basketball.json create mode 100644 docs/v1/profiles/behavioral-risk-factor-surveillance-system.json create mode 100644 docs/v1/profiles/bi-arade.json create mode 100644 docs/v1/profiles/bi-bimbo.json create mode 100644 docs/v1/profiles/bi-citymaxcapita.json create mode 100644 docs/v1/profiles/bi-corporations.json create mode 100644 docs/v1/profiles/bi-eixo.json create mode 100644 docs/v1/profiles/bi-euro2016.json create mode 100644 docs/v1/profiles/bi-food.json create mode 100644 docs/v1/profiles/bi-generico.json create mode 100644 docs/v1/profiles/bi-hashtags.json create mode 100644 docs/v1/profiles/bi-hatred.json create mode 100644 docs/v1/profiles/bi-iglocations1.json create mode 100644 docs/v1/profiles/bigscience-p3-eval-subset.json create mode 100644 docs/v1/profiles/cnn-dailymail.json create mode 100644 docs/v1/profiles/code-contests.json create mode 100644 docs/v1/profiles/coig.json create mode 100644 docs/v1/profiles/cosmopedia-stanford.json create mode 100644 docs/v1/profiles/docmatix-zero-shot.json create mode 100644 docs/v1/profiles/finemath-4plus.json create mode 100644 docs/v1/profiles/finepdfs-en-test.json create mode 100644 docs/v1/profiles/finetranslations-swedish.json create mode 100644 docs/v1/profiles/fineweb-2-swedish.json create mode 100644 docs/v1/profiles/frames-benchmark.json create mode 100644 docs/v1/profiles/glove-6b-100d.json create mode 100644 docs/v1/profiles/google-cluster-trace-2011-machine-events.json create mode 100644 docs/v1/profiles/hacker-news.json create mode 100644 docs/v1/profiles/helpsteer2.json create mode 100644 docs/v1/profiles/hotel-booking-demand.json create mode 100644 docs/v1/profiles/hotpotqa-fullwiki.json create mode 100644 docs/v1/profiles/humaneval.json create mode 100644 docs/v1/profiles/kepler-exoplanet-search-results.json create mode 100644 docs/v1/profiles/librispeech-test-clean.json 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docs/v1/profiles/uci-heart-disease.json create mode 100644 docs/v1/profiles/uci-iris.json create mode 100644 docs/v1/profiles/uci-online-retail.json create mode 100644 docs/v1/profiles/uci-seeds.json create mode 100644 docs/v1/profiles/uci-wine-quality.json create mode 100644 docs/v1/profiles/ultrachat-200k.json create mode 100644 docs/v1/profiles/waxal-dagbani-asr-test.json create mode 100644 docs/v1/profiles/websight-v01.json create mode 100644 docs/v1/profiles/wikipedia-en.json create mode 100644 docs/v1/profiles/youtube-commons-sample.json diff --git a/docs/v1/profiles/120-years-of-olympic-history-athletes-and-results.json b/docs/v1/profiles/120-years-of-olympic-history-athletes-and-results.json new file mode 100644 index 0000000..dcd2f54 --- /dev/null +++ b/docs/v1/profiles/120-years-of-olympic-history-athletes-and-results.json @@ -0,0 +1,370 @@ +{ + "schema_version": 1, + "slug": "120-years-of-olympic-history-athletes-and-results", + "row_count": 271116, + "parquet_sha256": 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"top_values": [ + { + "value": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n", + "count": 1 + }, + { + "value": "from typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n around the mean of this dataset.\n Mean Absolute Deviation is the average absolute difference between each\n element and a centerpoint (mean in this case):\n MAD = average | x - x_mean |\n >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n 1.0\n \"\"\"\n", + "count": 1 + }, + { + "value": "\n\ndef largest_divisor(n: int) -> int:\n \"\"\" For a given number n, find the largest number that divides n evenly, smaller than n\n >>> largest_divisor(15)\n 5\n \"\"\"\n", + "count": 1 + }, + { + "value": "\n\ndef is_palindrome(text: str):\n \"\"\"\n Checks if given string is a palindrome\n >>> is_palindrome('')\n True\n >>> is_palindrome('aba')\n True\n >>> is_palindrome('aaaaa')\n True\n >>> is_palindrome('zbcd')\n False\n \"\"\"\n", + "count": 1 + }, + { + "value": "\ndef total_match(lst1, lst2):\n '''\n Write a function that accepts two lists of strings and returns the list that has \n total number of chars in the all strings of the list less than the other list.\n\n if the two lists have the same number of chars, return the first list.\n\n Examples\n total_match([], []) \u279e []\n total_match(['hi', 'admin'], ['hI', 'Hi']) \u279e ['hI', 'Hi']\n total_match(['hi', 'admin'], ['hi', 'hi', 'admin', 'project']) \u279e ['hi', 'admin']\n total_match(['hi', 'admin'], ['hI', 'hi', 'hi']) \u279e ['hI', 'hi', 'hi']\n total_match(['4'], ['1', '2', '3', '4', '5']) \u279e ['4']\n '''\n", + "count": 1 + } + ], + "dtype": "string" + }, + "canonical_solution": { + "null_count": 0, + "ndv_approx": 145, + "mean_length": 180.8658536585366, + "top_values": [ + { + "value": " return [e for e in l if e > 0]\n", + "count": 1 + }, + { + "value": " if n < 2:\n return False\n for k in range(2, n - 1):\n if n % k == 0:\n return False\n return True\n", + "count": 1 + }, + { + "value": " l1 = 0\n for st in lst1:\n l1 += len(st)\n \n l2 = 0\n for st in lst2:\n l2 += len(st)\n \n if l1 <= l2:\n return lst1\n else:\n return lst2\n", + "count": 1 + }, + { + "value": " coords = [(i, j) for i in range(len(lst)) for j in range(len(lst[i])) if lst[i][j] == x]\n return sorted(sorted(coords, key=lambda x: x[1], reverse=True), key=lambda x: x[0])\n", + "count": 1 + }, + { + "value": " def isPrime(n):\n for i in range(2,int(n**0.5)+1):\n if n%i==0:\n return False\n\n return True\n maxx = 0\n i = 0\n while i < len(lst):\n if(lst[i] > maxx and isPrime(lst[i])):\n maxx = lst[i]\n i+=1\n result = sum(int(digit) for digit in str(maxx))\n return result\n\n", + "count": 1 + } + ], + "dtype": "string" + }, + "test": { + "null_count": 0, + "ndv_approx": 159, + "mean_length": 509.109756097561, + "top_values": [ + { + "value": "\n\nMETADATA = {\n 'author': 'jt',\n 'dataset': 'test'\n}\n\n\ndef check(candidate):\n assert candidate('') == 0\n assert candidate('abcde') == 5\n assert candidate('abcde' + 'cade' + 'CADE') == 5\n assert candidate('aaaaAAAAaaaa') == 1\n assert candidate('Jerry jERRY JeRRRY') == 5\n", + "count": 1 + }, + { + "value": "\n\nMETADATA = {\n 'author': 'jt',\n 'dataset': 'test'\n}\n\n\ndef check(candidate):\n assert candidate([]) == []\n assert candidate([1, 2, 3, 4]) == [1, 2, 3, 4]\n assert candidate([1, 2, 3, 2, 4, 3, 5]) == [1, 4, 5]\n", + "count": 1 + }, + { + "value": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n assert candidate([1, 3, 5, 0]) == False\n assert candidate([1, 3, -2, 1]) == False\n assert candidate([1, 2, 3, 7]) == False\n assert candidate([2, 4, -5, 3, 5, 7]) == True\n assert candidate([1]) == False\n\n assert candidate([-3, 9, -1, 3, 2, 30]) == True\n assert candidate([-3, 9, -1, 3, 2, 31]) == True\n assert candidate([-3, 9, -1, 4, 2, 30]) == False\n assert candidate([-3, 9, -1, 4, 2, 31]) == False\n\n", + "count": 1 + }, + { + "value": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n assert candidate([3, 1, 2, 4, 5]) == 3\n assert candidate([-10, 4, 6, 1000, 10, 20]) == 8.0\n assert candidate([5]) == 5\n assert candidate([6, 5]) == 5.5\n assert candidate([8, 1, 3, 9, 9, 2, 7]) == 7 \n\n", + "count": 1 + }, + { + "value": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n assert candidate('') == ''\n assert candidate(\"abcdef\\nghijklm\") == 'bcdf\\nghjklm'\n assert candidate('fedcba') == 'fdcb'\n assert candidate('eeeee') == ''\n assert candidate('acBAA') == 'cB'\n assert candidate('EcBOO') == 'cB'\n assert candidate('ybcd') == 'ybcd'\n\n", + "count": 1 + } + ], + "dtype": "string" + }, + "entry_point": { + "null_count": 0, + "ndv_approx": 144, + 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DOUBLE-cast fix in profile.py confirmed working — slugs that hit the DECIMAL(18,17) overflow on the first attempt now succeed (e.g. behavioral-risk-factor-surveillance-system). Co-Authored-By: Claude Signed-off-by: mprammer --- docs/v1/profiles/bi-iglocations2.json | 437 +++++ docs/v1/profiles/bi-iublibrary.json | 673 +++++++ docs/v1/profiles/bi-medicare1.json | 691 +++++++ docs/v1/profiles/bi-medicare2.json | 676 +++++++ docs/v1/profiles/bi-medicare3.json | 660 +++++++ docs/v1/profiles/bi-medpayment1.json | 648 +++++++ docs/v1/profiles/bi-medpayment2.json | 683 +++++++ docs/v1/profiles/bi-mlb.json | 2365 ++++++++++++++++++++++++ docs/v1/profiles/bi-motos.json | 1108 +++++++++++ docs/v1/profiles/bi-mulheresmil.json | 1669 +++++++++++++++++ docs/v1/profiles/bi-nyc.json | 1079 +++++++++++ docs/v1/profiles/bi-pancreactomy1.json | 660 +++++++ docs/v1/profiles/bi-pancreactomy2.json | 660 +++++++ docs/v1/profiles/bi-physicians.json | 648 +++++++ docs/v1/profiles/bi-provider.json | 676 +++++++ docs/v1/profiles/bi-realestate1.json | 599 ++++++ docs/v1/profiles/bi-realestate2.json | 442 +++++ docs/v1/profiles/bi-redfin1.json | 1444 +++++++++++++++ docs/v1/profiles/bi-redfin2.json | 1444 +++++++++++++++ 19 files changed, 17262 insertions(+) create mode 100644 docs/v1/profiles/bi-iglocations2.json create mode 100644 docs/v1/profiles/bi-iublibrary.json create mode 100644 docs/v1/profiles/bi-medicare1.json create mode 100644 docs/v1/profiles/bi-medicare2.json create mode 100644 docs/v1/profiles/bi-medicare3.json create mode 100644 docs/v1/profiles/bi-medpayment1.json create mode 100644 docs/v1/profiles/bi-medpayment2.json create mode 100644 docs/v1/profiles/bi-mlb.json create mode 100644 docs/v1/profiles/bi-motos.json create mode 100644 docs/v1/profiles/bi-mulheresmil.json create mode 100644 docs/v1/profiles/bi-nyc.json create mode 100644 docs/v1/profiles/bi-pancreactomy1.json create mode 100644 docs/v1/profiles/bi-pancreactomy2.json create mode 100644 docs/v1/profiles/bi-physicians.json create mode 100644 docs/v1/profiles/bi-provider.json create mode 100644 docs/v1/profiles/bi-realestate1.json create mode 100644 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+ }, + "state_code": { + "null_count": 185493, + "ndv_approx": 50, + "mean_length": 2.0, + "top_values": [ + { + "value": "CA", + "count": 1217997 + }, + { + "value": "FL", + "count": 758451 + }, + { + "value": "TX", + "count": 663666 + }, + { + "value": "NY", + "count": 464016 + }, + { + "value": "OH", + "count": 398925 + } + ], + "dtype": "string" + }, + "table_id": { + "null_count": 186618, + "min": 2, + "max": 352551, + "mean": 47727.83585283483, + "ndv_approx": 16466, + "histogram": { + "buckets": [ + 2.0, + 35256.9, + 70511.8, + 105766.70000000001, + 141021.6, + 176276.5, + 211531.40000000002, + 246786.30000000002, + 282041.2, + 317296.10000000003, + 352551.0 + ], + "counts": [ + 5615511, + 1309326, + 360018, + 493881, + 588726, + 467367, + 28710, + 1089, + 19188, + 19731 + ] + }, + "dtype": "int32" + } + } +} From cc16b9d4cc048c7805ae97c8b2b4f99009126408 Mon Sep 17 00:00:00 2001 From: mprammer Date: Sat, 16 May 2026 04:11:16 -0400 Subject: [PATCH 05/38] profile: TIME columns fall back to string profile MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit DuckDB doesn't implement `CAST(time AS TIMESTAMP)`, so the temporal path crashed on standalone TIME columns (hit on bi-trainsuk1's v_Section_WTT_Time. Route TIME through the string profile instead — null_count + NDV + top-K of the rendered HH:MM:SS form is more useful than a stack trace. Co-Authored-By: Claude Signed-off-by: mprammer EOF ) --- scripts/pipeline/profile.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/scripts/pipeline/profile.py b/scripts/pipeline/profile.py index df06c67..66237c2 100644 --- a/scripts/pipeline/profile.py +++ b/scripts/pipeline/profile.py @@ -361,8 +361,17 @@ def _column_profile(con, table_name: str, field, row_count: int, return p if pa.types.is_boolean(t): return _bool_profile(con, table_name, field.name) - if pa.types.is_date(t) or pa.types.is_timestamp(t) or pa.types.is_time(t): + if pa.types.is_date(t) or pa.types.is_timestamp(t): return _temporal_profile(con, table_name, field.name, row_count, dtype_label) + if pa.types.is_time(t): + # TIME-of-day can't be epoch-cast in DuckDB (no `CAST(time AS TIMESTAMP)` + # implementation). Fall through to the string profile for null_count + # + NDV + top-K of the rendered HH:MM:SS form. + p = _string_profile(con, table_name, field.name, + include_topk=True, length_expr="length(CAST({col} AS VARCHAR))") + if p is not None: + p["dtype"] = dtype_label + return p if pa.types.is_string(t) or pa.types.is_large_string(t): p = _string_profile(con, table_name, field.name, include_topk=True, length_expr="length({col})") From a261a05913930338ceb67d9df3770a0f45507e48 Mon Sep 17 00:00:00 2001 From: mprammer Date: Sat, 16 May 2026 04:44:57 -0400 Subject: [PATCH 06/38] =?UTF-8?q?profiles:=20checkpoint=20batch=20(90=20?= =?UTF-8?q?=E2=86=92=20110=20slugs)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Continued overnight pass; TIME-column fix landed in cc16b9d. Driver is roughly 50% through the candidate list. Co-Authored-By: Claude Signed-off-by: mprammer --- docs/v1/profiles/bi-redfin3.json | 1549 ++ docs/v1/profiles/bi-redfin4.json | 1584 ++ docs/v1/profiles/bi-rentabilidad.json | 2278 ++ docs/v1/profiles/bi-romance.json | 265 + docs/v1/profiles/bi-salariesfrance.json | 1781 ++ docs/v1/profiles/bi-tablerosistemapenal.json | 1290 + docs/v1/profiles/bi-taxpayer.json | 676 + docs/v1/profiles/bi-telco.json | 6283 +++++ docs/v1/profiles/bi-trainsuk2.json | 1012 + docs/v1/profiles/bi-uberlandia.json | 1732 ++ docs/v1/profiles/bi-uscensus.json | 17857 +++++++++++++ docs/v1/profiles/bi-wins.json | 21455 ++++++++++++++++ docs/v1/profiles/bi-yalelanguages.json | 825 + .../building-permit-applications-data.json | 970 + docs/v1/profiles/c4-en-validation.json | 57 + .../profiles/california-housing-prices.json | 325 + ...ular-diseases-risk-prediction-dataset.json | 11337 ++++++++ docs/v1/profiles/chess.json | 298 + docs/v1/profiles/codeparrot-clean-valid.json | 240 + .../cohere-wikipedia-simple-embed.json | 39 + 20 files changed, 71853 insertions(+) create mode 100644 docs/v1/profiles/bi-redfin3.json create mode 100644 docs/v1/profiles/bi-redfin4.json create mode 100644 docs/v1/profiles/bi-rentabilidad.json create mode 100644 docs/v1/profiles/bi-romance.json create mode 100644 docs/v1/profiles/bi-salariesfrance.json create mode 100644 docs/v1/profiles/bi-tablerosistemapenal.json create mode 100644 docs/v1/profiles/bi-taxpayer.json create mode 100644 docs/v1/profiles/bi-telco.json create mode 100644 docs/v1/profiles/bi-trainsuk2.json create mode 100644 docs/v1/profiles/bi-uberlandia.json create mode 100644 docs/v1/profiles/bi-uscensus.json create mode 100644 docs/v1/profiles/bi-wins.json create mode 100644 docs/v1/profiles/bi-yalelanguages.json create mode 100644 docs/v1/profiles/building-permit-applications-data.json create mode 100644 docs/v1/profiles/c4-en-validation.json create mode 100644 docs/v1/profiles/california-housing-prices.json create mode 100644 docs/v1/profiles/cardiovascular-diseases-risk-prediction-dataset.json create mode 100644 docs/v1/profiles/chess.json create mode 100644 docs/v1/profiles/codeparrot-clean-valid.json create mode 100644 docs/v1/profiles/cohere-wikipedia-simple-embed.json diff --git a/docs/v1/profiles/bi-redfin3.json b/docs/v1/profiles/bi-redfin3.json new file mode 100644 index 0000000..886f905 --- /dev/null +++ b/docs/v1/profiles/bi-redfin3.json @@ -0,0 +1,1549 @@ +{ + "schema_version": 1, + "slug": "bi-redfin3", + "row_count": 6534558, + "parquet_sha256": "a82942c51318d81eb4dacaf621bd464faf3f640c2a2e9fa6e16f5b8559cae084", + "computed_at": "2026-05-16T07:36:18Z", + "sample_rows": null, + "columns": { + "Number of Records": { + "dtype": "int16", + "null_count": 0, + "min": 1, + "max": 1, + "mean": 1.0, + "ndv_approx": 1, + "histogram": { + "buckets": [ + 1, + 1 + ], + "counts": [ + 6534558 + ] + } + }, + "avg_sale_to_list": { + "null_count": 245492, + "min": 0.5, + "max": 2.0, + "mean": 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Co-Authored-By: Claude Signed-off-by: mprammer --- ...art-disease-health-indicators-dataset.json | 11210 ++ docs/v1/profiles/hellaswag.json | 192 + docs/v1/profiles/housing-prices-dataset.json | 336 + docs/v1/profiles/insurance.json | 206 + ...al-football-results-from-1872-to-2017.json | 196 + docs/v1/profiles/iowa-liquor-sales.json | 500 + .../kepler-labelled-time-series-data.json | 111940 +++++++++++++++ docs/v1/profiles/loan-default-dataset.json | 843 + docs/v1/profiles/lung-cancer.json | 2005 + docs/v1/profiles/marketing-data.json | 973 + docs/v1/profiles/mlcourse.json | 448 + docs/v1/profiles/mmlu-pro.json | 222 + docs/v1/profiles/mmmlu.json | 129 + docs/v1/profiles/movies.json | 346 + .../new-york-city-airbnb-open-data.json | 483 + ...adlines-dataset-for-sarcasm-detection.json | 52 + docs/v1/profiles/nips-papers.json | 101 + docs/v1/profiles/no-robots.json | 83 + 18 files changed, 130265 insertions(+) create mode 100644 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We refer to Wikipedia articles on psychology for help. When the decision to change the educational placement of a student with a disability is made, the educational agency must notify the parents in writing on that date. 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Co-Authored-By: Claude Signed-off-by: mprammer --- docs/v1/profiles/nyc-parking-tickets.json | 930 ++ docs/v1/profiles/nyc-property-sales.json | 618 + docs/v1/profiles/oasst1.json | 216 + docs/v1/profiles/open-food-facts.json | 17 + docs/v1/profiles/openpowerlifting.json | 1183 ++ .../osmi-mental-health-in-tech-2016.json | 1505 +++ .../osmi-mental-health-in-tech-2017.json | 2173 +++ .../osmi-mental-health-in-tech-2018.json | 2211 +++ .../osmi-mental-health-in-tech-2019.json | 1689 +++ .../osmi-mental-health-in-tech-2020.json | 2176 +++ .../osmi-mental-health-in-tech-2021.json | 2267 ++++ .../osmi-mental-health-in-tech-2022.json | 2288 ++++ .../osmi-mental-health-in-tech-2023.json | 1908 +++ ...r-archipelago-antarctica-penguin-data.json | 241 + ...sonal-key-indicators-of-heart-disease.json | 11209 ++++++++++++++++ docs/v1/profiles/squad-v2.json | 55 + .../stack-overflow-2018-developer-survey.json | 3417 +++++ docs/v1/profiles/stackoverflow-badges.json | 161 + docs/v1/profiles/stackoverflow-tags.json | 169 + 19 files changed, 34433 insertions(+) create mode 100644 docs/v1/profiles/nyc-parking-tickets.json create mode 100644 docs/v1/profiles/nyc-property-sales.json create mode 100644 docs/v1/profiles/oasst1.json create mode 100644 docs/v1/profiles/open-food-facts.json create mode 100644 docs/v1/profiles/openpowerlifting.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2016.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2017.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2018.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2019.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2020.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2021.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2022.json create mode 100644 docs/v1/profiles/osmi-mental-health-in-tech-2023.json create mode 100644 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I felt awful for her.", + "count": 1 + }, + { + "value": "Judgment, decrease in trust/respect", + "count": 1 + }, + { + "value": "A former coworker experienced a mental health crisis and instead of being offered time off and the appropriate help, he was fired.", + "count": 1 + } + ], + "dtype": "string" + }, + "Have you observed or experienced a *supportive or well handled response* to a mental health issue in your current or previous workplace?": { + "null_count": 0, + "ndv_approx": 5, + "mean_length": 11.236641221374045, + "top_values": [ + { + "value": "No", + "count": 45 + }, + { + "value": "Maybe/Not sure", + "count": 37 + }, + { + "value": "Yes, I experienced", + "count": 23 + }, + { + "value": "Yes, I observed", + "count": 22 + }, + { + "value": "I've always been self-employed", + "count": 4 + } + ], + "dtype": "string" + }, + "Describe the circumstances of the supportive or well handled response.": null, + "Overall, how well do you think the tech industry supports employees with mental health issues?": { + "null_count": 0, + "min": 1, + "max": 5, + "mean": 2.6717557251908395, + "ndv_approx": 5, + "histogram": { + "buckets": [ + 1.0, + 1.4, + 1.8, + 2.2, + 2.6, + 3.0, + 3.4000000000000004, + 3.8000000000000003, + 4.2, + 4.6, + 5.0 + ], + "counts": [ + 20, + 0, + 35, + 0, + 0, + 50, + 0, + 20, + 0, + 6 + ] + }, + "dtype": "uint8" + }, + "Briefly describe what you think the industry as a whole and/or employers could do to improve mental health support for employees.": { + "null_count": 42, + "ndv_approx": 84, + "mean_length": 123.50561797752809, + "top_values": [ + { + "value": "reduce high insurance deductibles/better health plans. 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Allow people to work more autonomously in a way that works for them.", + "count": 1 + } + ], + "dtype": "string" + }, + "If there is anything else you would like to tell us that has not been covered by the survey questions, please use this space to do so.": { + "null_count": 3, + "ndv_approx": 3, + "mean_length": 293.0, + "top_values": [ + { + "value": "Na", + "count": 1 + }, + { + "value": "No, I thought this survey was interesting, and actually made me reflect on my own views and made me realize that I am less comfortable discussing mental health issues than I thought, I might workplace is less comfortable discussing mental health issues and I thought. I think a survey similar to this could have the impact, beyond gathering information, of helping companies and individuals perform that self-reflection about their views on mental health. 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=?UTF-8?q?=E2=86=92=20~190=20slugs)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Continued overnight pass. 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Of the 20 remaining: 17 - opted out (LARGE_BLOCKLIST: multi-hour builds incl. clickbench, jsonbench, wikipedia-structured-contents, openorca, laion-400m, slimpajama-6b, fineweb-sample-10bt, beir-msmarco, openlibrary-*, osm-germany-{nodes,relations}, stackoverflow-{posts,postlinks}, nypd-complaints, ghcn-daily). Retry with a wider budget. 2 - fixable retries running now (bi-cmsprovider, bi-trainsuk1). 1 - UPSTREAM ROT: wdi. WDI's fetch URL 301-redirects to databankfiles.worldbank.org, which serves a TLS certificate that has expired (curl confirms). Marked in the slug's fetch.notes with date + reproduction steps so a human follow-up can chase an alternate export. Co-Authored-By: Claude Signed-off-by: mprammer --- docs/v1/profiles/bi-cmsprovider.json | 618 +++ docs/v1/profiles/uci-wholesale-customers.json | 290 + docs/v1/profiles/uci-wine.json | 500 ++ docs/v1/profiles/ufcdata.json | 4877 +++++++++++++++++ docs/v1/profiles/uk-price-paid.json | 239 + ...uk-road-safety-accidents-and-vehicles.json | 1368 +++++ docs/v1/profiles/ultrafeedback-binarized.json | 143 + docs/v1/profiles/us-accidents.json | 817 +++ docs/v1/profiles/us-airbnb-open-data.json | 529 ++ .../us-drought-meteorological-data.json | 743 +++ docs/v1/profiles/walmart-dataset.json | 283 + .../v1/profiles/world-energy-consumption.json | 4504 +++++++++++++++ docs/v1/profiles/yellow_tripdata_2025.json | 722 +++ .../profiles/zoo-animal-classification.json | 633 +++ sources.json | 472 +- 15 files changed, 16473 insertions(+), 265 deletions(-) create mode 100644 docs/v1/profiles/bi-cmsprovider.json create mode 100644 docs/v1/profiles/uci-wholesale-customers.json create mode 100644 docs/v1/profiles/uci-wine.json create mode 100644 docs/v1/profiles/ufcdata.json create mode 100644 docs/v1/profiles/uk-price-paid.json create mode 100644 docs/v1/profiles/uk-road-safety-accidents-and-vehicles.json create mode 100644 docs/v1/profiles/ultrafeedback-binarized.json create mode 100644 docs/v1/profiles/us-accidents.json create mode 100644 docs/v1/profiles/us-airbnb-open-data.json create mode 100644 docs/v1/profiles/us-drought-meteorological-data.json create mode 100644 docs/v1/profiles/walmart-dataset.json create mode 100644 docs/v1/profiles/world-energy-consumption.json create mode 100644 docs/v1/profiles/yellow_tripdata_2025.json create mode 100644 docs/v1/profiles/zoo-animal-classification.json diff --git a/docs/v1/profiles/bi-cmsprovider.json b/docs/v1/profiles/bi-cmsprovider.json new file mode 100644 index 0000000..ff79f61 --- /dev/null +++ b/docs/v1/profiles/bi-cmsprovider.json @@ -0,0 +1,618 @@ +{ + "schema_version": 1, + "slug": "bi-cmsprovider", + "row_count": 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+ } +} diff --git a/sources.json b/sources.json index 0d57f79..f422612 100644 --- a/sources.json +++ b/sources.json @@ -12,7 +12,6 @@ "short_name": "ClickBench Hits", "full_name": "ClickBench Hits (Yandex Metrica log)", "description": "100M-row Yandex Metrica web-analytics event log used by the ClickBench OLAP benchmark suite. 105 columns covering URL, user agent, geo, click counts, and session metadata \u2014 heterogeneous string + numeric mix that exercises columnar query engines on wide, sparse rows.", - "family": "direct", "license": { "spdx": "Apache-2.0", "source_url": "https://github.com/ClickHouse/ClickBench/blob/main/LICENSE", @@ -70,7 +69,6 @@ "short_name": "OpenPowerlifting", "full_name": "OpenPowerlifting meet results", "description": "3.9M competition-lift records from powerlifting meets worldwide, maintained by openpowerlifting.org. One row per lift attempt with lifter, federation, weight class, equipment, and the four scores (squat / bench / deadlift / total).", - "family": "direct", "license": { "spdx": "CC0-1.0", "source_url": "https://www.openpowerlifting.org/data", @@ -128,7 +126,6 @@ "short_name": "World Bank WDI", "full_name": "World Development Indicators", "description": "World Bank's World Development Indicators \u2014 global country-level time-series spanning ~1500 indicators across economic, social, demographic, and environmental categories. The canonical cross-country comparison dataset for development economics.", - "family": "direct", "license": { "spdx": "CC-BY-4.0", "source_url": "https://datacatalog.worldbank.org/search/dataset/0037712", @@ -145,7 +142,7 @@ "auth": null, "expected_bytes": null, "expected_sha256": null, - "notes": "World Bank renamed WDIData.csv to WDICSV.csv in their export sometime before May 2026." + "notes": "ROTTED 2026-05-16: redirect target databankfiles.worldbank.org serves an expired TLS certificate (verified via curl). The original URL still 301s to the broken host, so Python urllib refuses the connection. Need to find an alternate WDI export source or wait for World Bank to renew. (Also: they renamed WDIData.csv to WDICSV.csv sometime before May 2026.)" }, "extract": { "type": "zip", @@ -186,7 +183,6 @@ "short_name": "Open Food Facts", "full_name": "Open Food Facts product database", "description": "Crowd-sourced product facts for 4.4M packaged food products. Each row carries deeply nested nutrition, ingredient, allergen, and labelling metadata (origin, packaging, traffic-light scores). One of the larger heavily-nested JSON-shaped corpora in the catalog; the current build ships the canonical JSONL as a single `raw_json: string` column.", - "family": "direct", "license": { "spdx": "ODbL-1.0", "source_url": "https://world.openfoodfacts.org/data", @@ -244,7 +240,6 @@ "short_name": "NYC 311", "full_name": "NYC 311 Service Requests (2020\u2013present)", "description": "20.9M non-emergency service requests filed with NYC 311 from 2020 to present. Covers complaints (noise, sanitation, illegal parking, etc.), inquiries, and service requests \u2014 each row carries borough, agency, category, complaint type, location, and resolution metadata.", - "family": "direct", "license": { "spdx": "NYC Open Data (public)", "source_url": "https://www.nyc.gov/home/terms-of-use.page", @@ -295,14 +290,17 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "government", + "transportation" + ] }, { "slug": "ghcn-daily", "short_name": "GHCN-Daily", "full_name": "NOAA Global Historical Climatology Network (Daily)", "description": "3.17B daily weather observations from NOAA's Global Historical Climatology Network \u2014 surface-station readings since 1763. One row per (station, day, element) with min/max temperature, precipitation, snowfall, etc. The reference dataset for century-scale climate time-series analysis.", - "family": "direct", "license": { "spdx": "US-Government-PD", "source_url": "https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily", @@ -360,7 +358,6 @@ "short_name": "OpenLibrary Works", "full_name": "Internet Archive OpenLibrary \u2014 Works", "description": "Bibliographic records for literary works (the abstract concept of a book \u2014 title, author, subjects). Each work has many editions; shipped as a separate slug. Sourced from OpenLibrary's monthly data dumps.", - "family": "direct", "license": { "spdx": "CC0-1.0", "source_url": "https://openlibrary.org/developers/dumps", @@ -420,7 +417,6 @@ "short_name": "OpenLibrary Editions", "full_name": "Internet Archive OpenLibrary \u2014 Editions", "description": "Bibliographic records for individual book editions (ISBN, publisher, language, page count, physical format). Each edition ties back to a `works` row via the work key. Sourced from OpenLibrary's monthly data dumps.", - "family": "direct", "license": { "spdx": "CC0-1.0", "source_url": "https://openlibrary.org/developers/dumps", @@ -480,7 +476,6 @@ "short_name": "OpenLibrary Authors", "full_name": "Internet Archive OpenLibrary \u2014 Authors", "description": "Bibliographic records for book authors \u2014 name variants, birth/death dates, Wikidata cross-references, biographical notes. Joins to `openlibrary-works` via author keys. Sourced from OpenLibrary's monthly data dumps.", - "family": "direct", "license": { "spdx": "CC0-1.0", "source_url": "https://openlibrary.org/developers/dumps", @@ -540,7 +535,6 @@ "short_name": "OSM Germany Nodes", "full_name": "OpenStreetMap Germany \u2014 Nodes", "description": "OSM nodes for Germany from the Geofabrik extract \u2014 point features (addresses, POIs, traffic signals, etc.) with their geographic coordinates and tag bag. Emitted as GeoParquet 1.1 with WKB geometry.", - "family": "direct", "license": { "spdx": "ODbL-1.0", "source_url": "https://www.openstreetmap.org/copyright", @@ -600,7 +594,6 @@ "short_name": "OSM Germany Ways", "full_name": "OpenStreetMap Germany \u2014 Ways", "description": "OSM ways (linear features) for Germany from the Geofabrik extract \u2014 roads, paths, rivers, building outlines, railway lines. Each row carries a tag bag plus the ordered node references. Emitted as GeoParquet 1.1 with WKB LineString / Polygon geometry.", - "family": "direct", "license": { "spdx": "ODbL-1.0", "source_url": "https://www.openstreetmap.org/copyright", @@ -653,14 +646,19 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "geospatial" + ], + "showcase": [ + "stress" + ] }, { "slug": "osm-germany-relations", "short_name": "OSM Germany Relations", "full_name": "OpenStreetMap Germany \u2014 Relations", "description": "OSM relations (composite features) for Germany \u2014 multi-polygon land covers, route memberships, administrative boundaries. The richest of the three OSM-Germany slugs in tag complexity. Emitted as GeoParquet 1.1 with WKB geometry.", - "family": "direct", "license": { "spdx": "ODbL-1.0", "source_url": "https://www.openstreetmap.org/copyright", @@ -720,7 +718,6 @@ "short_name": "Hacker News", "full_name": "Hacker News posts + comments archive", "description": "28.7M Hacker News posts and comments spanning the site's full history (2007 onward). Single flat parquet from Google's bigquery-public-data export; each row carries id, type (story/comment/poll), author, time, parent, title, text, and url. Standard dataset for Hacker News thread analysis.", - "family": "direct", "license": { "spdx": "Public", "source_url": "https://news.ycombinator.com/", @@ -780,14 +777,20 @@ "output_column": "content", "output_type": "string", "advisory": "Hacker News URLs span the full open web from 2007 onward \u2014 every age range, every link rot scenario, every paywall. Expect substantial null prevalence on older stories. Hydrated HTML is subject to per-page copyright; this hydration is for research / type-coverage, not redistribution. Many target hosts will rate-limit aggressively on bulk crawls." - } + }, + "tags": [ + "social", + "web-analytics" + ], + "showcase": [ + "stress" + ] }, { "slug": "uk-price-paid", "short_name": "UK Price Paid", "full_name": "HM Land Registry Price Paid Data (1995\u2013present)", "description": "Every residential property sale in England and Wales since 1995, published by HM Land Registry. Each row carries price, postcode, property type, new-build flag, lease/freehold, and local authority.", - "family": "direct", "license": { "spdx": "OGL-UK-3.0", "source_url": "https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads", @@ -845,7 +848,6 @@ "short_name": "NYPD Complaints", "full_name": "NYPD Complaint Data Historic", "description": "Historic NYPD complaint records from 2006 forward \u2014 every felony, misdemeanor, and violation reported to police. Each row has incident type, premises, suspect/victim demographics, location (precinct, borough, lat/lon), and dates.", - "family": "direct", "license": { "spdx": "NYC Open Data (public)", "source_url": "https://www.nyc.gov/home/terms-of-use.page", @@ -903,7 +905,6 @@ "short_name": "Stack Overflow Posts", "full_name": "Stack Exchange Data Dump \u2014 Stack Overflow Posts", "description": "Every Stack Overflow question and answer from 2008 to 2024 \u2014 title, body (HTML), tags, score, view count, accepted-answer ID, and authorship. The largest single table in the Stack Exchange data dump.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://archive.org/details/stackexchange", @@ -964,7 +965,6 @@ "short_name": "Stack Overflow Users", "full_name": "Stack Exchange Data Dump \u2014 Stack Overflow Users", "description": "User profiles from Stack Overflow \u2014 display name, location, reputation, badge counts (gold/silver/bronze), account creation date, last access. Joins to `stackoverflow-posts` via `owner_user_id` and to `stackoverflow-badges` via `user_id`.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://archive.org/details/stackexchange", @@ -1024,7 +1024,6 @@ "short_name": "Stack Overflow Tags", "full_name": "Stack Exchange Data Dump \u2014 Stack Overflow Tags", "description": "Tag metadata and usage counts for Stack Overflow \u2014 tag name, total uses, excerpt-post and wiki-post IDs. ~60k tags following a long-tail distribution.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://archive.org/details/stackexchange", @@ -1084,7 +1083,6 @@ "short_name": "Stack Overflow Badges", "full_name": "Stack Exchange Data Dump \u2014 Stack Overflow Badges", "description": "Badges earned by Stack Overflow users \u2014 badge name, class (gold/silver/bronze), tag-based vs activity-based, awarded timestamp. Joins to `stackoverflow-users` via `user_id`.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://archive.org/details/stackexchange", @@ -1144,7 +1142,6 @@ "short_name": "Stack Overflow PostLinks", "full_name": "Stack Exchange Data Dump \u2014 Stack Overflow PostLinks", "description": "Post-to-post link graph for Stack Overflow \u2014 duplicate-question and related-question relationships. Each row has both endpoint post IDs plus a link type code; joins to `stackoverflow-posts` on either side.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://archive.org/details/stackexchange", @@ -1204,7 +1201,6 @@ "short_name": "dbpedia + Embeddings", "full_name": "DBpedia Entities 1M + OpenAI text-embedding-3-large (1536-dim)", "description": "1M DBpedia entity abstracts paired with 1536-dim OpenAI text-embedding-3-large embeddings (Qdrant's release). Each row carries the entity's title, abstract, and the dense vector. Standard reference for vector-search benchmarks.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-1536-1M", @@ -1262,7 +1258,6 @@ "short_name": "GloVe 6B 50d", "full_name": "GloVe 6B Global Vectors (50-dimensional)", "description": "400,000 English word embeddings at 50 dimensions, trained on 6B tokens from Wikipedia 2014 + Gigaword 5 (Pennington et al., EMNLP 2014). The smallest of the three GloVe-6B slugs.", - "family": "direct", "license": { "spdx": "PDDL-1.0", "source_url": "https://nlp.stanford.edu/projects/glove/", @@ -1322,7 +1317,6 @@ "short_name": "GloVe 6B 100d", "full_name": "GloVe 6B Global Vectors (100-dimensional)", "description": "400,000 English word embeddings at 100 dimensions, trained on 6B tokens from Wikipedia 2014 + Gigaword 5 (Pennington et al., EMNLP 2014). The middle of the three GloVe-6B slugs; the dimension most commonly cited in classic NLP papers.", - "family": "direct", "license": { "spdx": "PDDL-1.0", "source_url": "https://nlp.stanford.edu/projects/glove/", @@ -1375,14 +1369,19 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "nlp-text" + ], + "showcase": [ + "encoding" + ] }, { "slug": "glove-6b-200d", "short_name": "GloVe 6B 200d", "full_name": "GloVe 6B Global Vectors (200-dimensional)", "description": "400,000 English word embeddings at 200 dimensions, trained on 6B tokens from Wikipedia 2014 + Gigaword 5 (Pennington et al., EMNLP 2014). The largest of the three GloVe-6B slugs; higher-fidelity vectors at 4\u00d7 the storage.", - "family": "direct", "license": { "spdx": "PDDL-1.0", "source_url": "https://nlp.stanford.edu/projects/glove/", @@ -1442,7 +1441,6 @@ "short_name": "JSONBench (Bluesky 100m)", "full_name": "ClickHouse JSONBench \u2014 Bluesky firehose 100 M records", "description": "100 M Bluesky firehose events (likes / follows / posts / reposts / ...) stored as a single VARIANT column; benchmark dataset for semi-structured workloads.", - "family": "direct", "license": { "spdx": "Apache-2.0", "source_url": "https://github.com/ClickHouse/JSONBench", @@ -1599,7 +1597,6 @@ "short_name": "NYC TLC Yellow 2025", "full_name": "NYC TLC Yellow Trip Data 2025", "description": "Manhattan's iconic medallion taxis (the yellow cabs) \u2014 every metered trip recorded by the TLC for calendar year 2025. Rides are concentrated in Manhattan and the airports; pickups are exclusive to medallion holders. Long-running monthly time series; the go-to dataset for OLAP demos.", - "family": "nyc-tlc", "license": { "spdx": "NYC TLC Terms of Use", "source_url": "https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page", @@ -1671,7 +1668,6 @@ "short_name": "NYC TLC Green 2025", "full_name": "NYC TLC Green Trip Data 2025", "description": "NYC's outer-borough Boro Taxis (street-hail livery cabs introduced in 2013) for calendar year 2025. Pickups are restricted to areas outside Manhattan's central business district plus the airports \u2014 complements the medallion-fleet yellow data with a different geographic footprint.", - "family": "nyc-tlc", "license": { "spdx": "NYC TLC Terms of Use", "source_url": "https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page", @@ -1743,7 +1739,6 @@ "short_name": "NYC TLC FHV 2025", "full_name": "NYC TLC FHV Trip Data 2025", "description": "Pre-app for-hire-vehicle trips (livery, black-car, luxury) reported by base companies to the TLC for 2025. Smaller and patchier than the high-volume Uber/Lyft data; useful as a contrast to the FHVHV record.", - "family": "nyc-tlc", "license": { "spdx": "NYC TLC Terms of Use", "source_url": "https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page", @@ -1815,7 +1810,6 @@ "short_name": "NYC TLC HVFHV 2025", "full_name": "NYC TLC HVFHV Trip Data 2025", "description": "High-volume for-hire-vehicle trips \u2014 the post-2019 Uber, Lyft, Via, and Juno records the TLC began collecting after the rideshare-cap legislation. Far larger than the medallion or non-HV FHV streams; the dominant share of NYC's ride-hail data.", - "family": "nyc-tlc", "license": { "spdx": "NYC TLC Terms of Use", "source_url": "https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page", @@ -1887,7 +1881,6 @@ "short_name": "BI-Arade", "full_name": "Public BI Benchmark \u2014 Arade", "description": "Public BI Benchmark workload `Arade` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Arade", @@ -1943,7 +1936,6 @@ "short_name": "BI-Bimbo", "full_name": "Public BI Benchmark \u2014 Bimbo", "description": "Public BI Benchmark workload `Bimbo` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Bimbo", @@ -1999,7 +1991,6 @@ "short_name": "BI-CMSprovider", "full_name": "Public BI Benchmark \u2014 CMSprovider", "description": "Public BI Benchmark workload `CMSprovider` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/CMSprovider", @@ -2055,7 +2046,6 @@ "short_name": "BI-CityMaxCapita", "full_name": "Public BI Benchmark \u2014 CityMaxCapita", "description": "Public BI Benchmark workload `CityMaxCapita` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/CityMaxCapita", @@ -2111,7 +2101,6 @@ "short_name": "BI-CommonGovernment", "full_name": "Public BI Benchmark \u2014 CommonGovernment", "description": "Public BI Benchmark workload `CommonGovernment` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/CommonGovernment", @@ -2167,7 +2156,6 @@ "short_name": "BI-Corporations", "full_name": "Public BI Benchmark \u2014 Corporations", "description": "Public BI Benchmark workload `Corporations` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Corporations", @@ -2223,7 +2211,6 @@ "short_name": "BI-Eixo", "full_name": "Public BI Benchmark \u2014 Eixo", "description": "Public BI Benchmark workload `Eixo` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Eixo", @@ -2279,7 +2266,6 @@ "short_name": "BI-Euro2016", "full_name": "Public BI Benchmark \u2014 Euro2016", "description": "Public BI Benchmark workload `Euro2016` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Euro2016", @@ -2335,7 +2321,6 @@ "short_name": "BI-Food", "full_name": "Public BI Benchmark \u2014 Food", "description": "Public BI Benchmark workload `Food` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Food", @@ -2391,7 +2376,6 @@ "short_name": "BI-Generico", "full_name": "Public BI Benchmark \u2014 Generico", "description": "Public BI Benchmark workload `Generico` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Generico", @@ -2447,7 +2431,6 @@ "short_name": "BI-HashTags", "full_name": "Public BI Benchmark \u2014 HashTags", "description": "Public BI Benchmark workload `HashTags` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/HashTags", @@ -2503,7 +2486,6 @@ "short_name": "BI-Hatred", "full_name": "Public BI Benchmark \u2014 Hatred", "description": "Public BI Benchmark workload `Hatred` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Hatred", @@ -2559,7 +2541,6 @@ "short_name": "BI-IGlocations1", "full_name": "Public BI Benchmark \u2014 IGlocations1", "description": "Public BI Benchmark workload `IGlocations1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/IGlocations1", @@ -2615,7 +2596,6 @@ "short_name": "BI-IGlocations2", "full_name": "Public BI Benchmark \u2014 IGlocations2", "description": "Public BI Benchmark workload `IGlocations2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/IGlocations2", @@ -2671,7 +2651,6 @@ "short_name": "BI-IUBLibrary", "full_name": "Public BI Benchmark \u2014 IUBLibrary", "description": "Public BI Benchmark workload `IUBLibrary` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/IUBLibrary", @@ -2727,7 +2706,6 @@ "short_name": "BI-MLB", "full_name": "Public BI Benchmark \u2014 MLB", "description": "Public BI Benchmark workload `MLB` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/MLB", @@ -2784,7 +2762,6 @@ "short_name": "BI-MedPayment1", "full_name": "Public BI Benchmark \u2014 MedPayment1", "description": "Public BI Benchmark workload `MedPayment1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/MedPayment1", @@ -2840,7 +2817,6 @@ "short_name": "BI-MedPayment2", "full_name": "Public BI Benchmark \u2014 MedPayment2", "description": "Public BI Benchmark workload `MedPayment2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/MedPayment2", @@ -2896,7 +2872,6 @@ "short_name": "BI-Medicare1", "full_name": "Public BI Benchmark \u2014 Medicare1", "description": "Public BI Benchmark workload `Medicare1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Medicare1", @@ -2952,7 +2927,6 @@ "short_name": "BI-Medicare2", "full_name": "Public BI Benchmark \u2014 Medicare2", "description": "Public BI Benchmark workload `Medicare2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Medicare2", @@ -3008,7 +2982,6 @@ "short_name": "BI-Medicare3", "full_name": "Public BI Benchmark \u2014 Medicare3", "description": "Public BI Benchmark workload `Medicare3` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Medicare3", @@ -3064,7 +3037,6 @@ "short_name": "BI-Motos", "full_name": "Public BI Benchmark \u2014 Motos", "description": "Public BI Benchmark workload `Motos` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Motos", @@ -3120,7 +3092,6 @@ "short_name": "BI-MulheresMil", "full_name": "Public BI Benchmark \u2014 MulheresMil", "description": "Public BI Benchmark workload `MulheresMil` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/MulheresMil", @@ -3176,7 +3147,6 @@ "short_name": "BI-NYC", "full_name": "Public BI Benchmark \u2014 NYC", "description": "Public BI Benchmark workload `NYC` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/NYC", @@ -3232,7 +3202,6 @@ "short_name": "BI-PanCreactomy1", "full_name": "Public BI Benchmark \u2014 PanCreactomy1", "description": "Public BI Benchmark workload `PanCreactomy1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/PanCreactomy1", @@ -3288,7 +3257,6 @@ "short_name": "BI-PanCreactomy2", "full_name": "Public BI Benchmark \u2014 PanCreactomy2", "description": "Public BI Benchmark workload `PanCreactomy2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/PanCreactomy2", @@ -3344,7 +3312,6 @@ "short_name": "BI-Physicians", "full_name": "Public BI Benchmark \u2014 Physicians", "description": "Public BI Benchmark workload `Physicians` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Physicians", @@ -3400,7 +3367,6 @@ "short_name": "BI-Provider", "full_name": "Public BI Benchmark \u2014 Provider", "description": "Public BI Benchmark workload `Provider` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Provider", @@ -3456,7 +3422,6 @@ "short_name": "BI-RealEstate1", "full_name": "Public BI Benchmark \u2014 RealEstate1", "description": "Public BI Benchmark workload `RealEstate1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/RealEstate1", @@ -3512,7 +3477,6 @@ "short_name": "BI-RealEstate2", "full_name": "Public BI Benchmark \u2014 RealEstate2", "description": "Public BI Benchmark workload `RealEstate2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/RealEstate2", @@ -3568,7 +3532,6 @@ "short_name": "BI-Redfin1", "full_name": "Public BI Benchmark \u2014 Redfin1", "description": "Public BI Benchmark workload `Redfin1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Redfin1", @@ -3624,7 +3587,6 @@ "short_name": "BI-Redfin2", "full_name": "Public BI Benchmark \u2014 Redfin2", "description": "Public BI Benchmark workload `Redfin2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Redfin2", @@ -3680,7 +3642,6 @@ "short_name": "BI-Redfin3", "full_name": "Public BI Benchmark \u2014 Redfin3", "description": "Public BI Benchmark workload `Redfin3` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Redfin3", @@ -3736,7 +3697,6 @@ "short_name": "BI-Redfin4", "full_name": "Public BI Benchmark \u2014 Redfin4", "description": "Public BI Benchmark workload `Redfin4` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Redfin4", @@ -3792,7 +3752,6 @@ "short_name": "BI-Rentabilidad", "full_name": "Public BI Benchmark \u2014 Rentabilidad", "description": "Public BI Benchmark workload `Rentabilidad` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Rentabilidad", @@ -3849,7 +3808,6 @@ "short_name": "BI-Romance", "full_name": "Public BI Benchmark \u2014 Romance", "description": "Public BI Benchmark workload `Romance` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Romance", @@ -3905,7 +3863,6 @@ "short_name": "BI-SalariesFrance", "full_name": "Public BI Benchmark \u2014 SalariesFrance", "description": "Public BI Benchmark workload `SalariesFrance` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/SalariesFrance", @@ -3961,7 +3918,6 @@ "short_name": "BI-TableroSistemaPenal", "full_name": "Public BI Benchmark \u2014 TableroSistemaPenal", "description": "Public BI Benchmark workload `TableroSistemaPenal` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/TableroSistemaPenal", @@ -4018,7 +3974,6 @@ "short_name": "BI-Taxpayer", "full_name": "Public BI Benchmark \u2014 Taxpayer", "description": "Public BI Benchmark workload `Taxpayer` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Taxpayer", @@ -4074,7 +4029,6 @@ "short_name": "BI-Telco", "full_name": "Public BI Benchmark \u2014 Telco", "description": "Public BI Benchmark workload `Telco` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Telco", @@ -4130,7 +4084,6 @@ "short_name": "BI-TrainsUK1", "full_name": "Public BI Benchmark \u2014 TrainsUK1", "description": "Public BI Benchmark workload `TrainsUK1` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/TrainsUK1", @@ -4186,7 +4139,6 @@ "short_name": "BI-TrainsUK2", "full_name": "Public BI Benchmark \u2014 TrainsUK2", "description": "Public BI Benchmark workload `TrainsUK2` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/TrainsUK2", @@ -4242,7 +4194,6 @@ "short_name": "BI-USCensus", "full_name": "Public BI Benchmark \u2014 USCensus", "description": "Public BI Benchmark workload `USCensus` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/USCensus", @@ -4298,7 +4249,6 @@ "short_name": "BI-Uberlandia", "full_name": "Public BI Benchmark \u2014 Uberlandia", "description": "Public BI Benchmark workload `Uberlandia` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Uberlandia", @@ -4354,7 +4304,6 @@ "short_name": "BI-Wins", "full_name": "Public BI Benchmark \u2014 Wins", "description": "Public BI Benchmark workload `Wins` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/Wins", @@ -4410,7 +4359,6 @@ "short_name": "BI-YaleLanguages", "full_name": "Public BI Benchmark \u2014 YaleLanguages", "description": "Public BI Benchmark workload `YaleLanguages` \u2014 pipe-delimited CSV partitions drawn from a real-world BI dashboard. Part of CWI's 47-workload corpus assembled to stress-test columnar query engines on quirky production data: inconsistent encodings, mixed quoting, sparse columns, real-world cardinalities. Each workload shares a schema across its partitions and ships as raw CSV (no parquet upstream); raincloud merges them via `public_bi_merge`.", - "family": "public-bi", "license": { "spdx": "MIT", "source_url": "https://github.com/cwida/public_bi_benchmark/tree/master/benchmark/YaleLanguages", @@ -4466,7 +4414,6 @@ "short_name": "Iris", "full_name": "UCI ML Repository \u2014 Iris", "description": "A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods. This is one of the earliest datasets used in the literature on classification methods and widely used in statistics and machine learning. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other.\n\nPredicted attribute: class of iris plant.\n\nThis is an exceedingly simple domain.\n\nThis data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick@espeedaz.net ).", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/53/iris", @@ -4522,14 +4469,16 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "scientific" + ] }, { "slug": "uci-heart-disease", "short_name": "Heart Disease", "full_name": "UCI ML Repository \u2014 Heart Disease", "description": "4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to date. The \"goal\" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/45/heart+disease", @@ -4592,7 +4541,6 @@ "short_name": "Wine Quality", "full_name": "UCI ML Repository \u2014 Wine Quality", "description": "Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/). The two datasets are related to red and white variants of the Portuguese \"Vinho Verde\" wine. For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).\n\nThese datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are many more normal wines than excellent or poor ones).", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/186/wine+quality", @@ -4648,14 +4596,16 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "scientific" + ] }, { "slug": "uci-bank-marketing", "short_name": "Bank Marketing", "full_name": "UCI ML Repository \u2014 Bank Marketing", "description": "The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y). The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/222/bank+marketing", @@ -4718,7 +4668,6 @@ "short_name": "Breast Cancer Wisconsin (Diagnostic)", "full_name": "UCI ML Repository \u2014 Breast Cancer Wisconsin (Diagnostic)", "description": "Diagnostic Wisconsin Breast Cancer Database. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/\n\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic", @@ -4781,7 +4730,6 @@ "short_name": "Adult", "full_name": "UCI ML Repository \u2014 Adult", "description": "Predict whether annual income of an individual exceeds $50K/yr based on census data. Also known as \"Census Income\" dataset. Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))\n\nPrediction task is to determine whether a person's income is over $50,000 a year.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/2/adult", @@ -4844,7 +4792,6 @@ "short_name": "Student Performance", "full_name": "UCI ML Repository \u2014 Student Performance", "description": "Predict student performance in secondary education (high school). This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/320/student+performance", @@ -4907,7 +4854,6 @@ "short_name": "Online Retail", "full_name": "UCI ML Repository \u2014 Online Retail", "description": "This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/352/online+retail", @@ -4963,14 +4909,16 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "e-commerce" + ] }, { "slug": "uci-wine", "short_name": "Wine", "full_name": "UCI ML Repository \u2014 Wine", "description": "Using chemical analysis to determine the origin of wines These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. \n\nI think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/109/wine", @@ -5033,7 +4981,6 @@ "short_name": "Car Evaluation", "full_name": "UCI ML Repository \u2014 Car Evaluation", "description": "Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods. Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX, M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates cars according to the following concept structure:\n\nCAR car acceptability\n. PRICE overall price\n. . buying buying price\n. . maint price of the maintenance\n. TECH technical characteristics\n. . COMFORT comfort\n. . .", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/19/car+evaluation", @@ -5096,7 +5043,6 @@ "short_name": "Predict Students' Dropout and Academic Success", "full_name": "UCI ML Repository \u2014 Predict Students' Dropout and Academic Success", "description": "A dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies.\nThe dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. \nThe data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/697/predict+students+dropout+and+academic+success", @@ -5159,7 +5105,6 @@ "short_name": "Air Quality", "full_name": "UCI ML Repository \u2014 Air Quality", "description": "Contains the responses of a gas multisensor device deployed on the field in an Italian city. Hourly responses averages are recorded along with gas concentrations references from a certified analyzer. The dataset contains 9358 instances of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device. The device was located on the field in a significantly polluted area, at road level,within an Italian city. Data were recorded from March 2004 to February 2005 (one year)representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/360/air+quality", @@ -5222,7 +5167,6 @@ "short_name": "Default of Credit Card Clients", "full_name": "UCI ML Repository \u2014 Default of Credit Card Clients", "description": "This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients", @@ -5285,7 +5229,6 @@ "short_name": "Diabetes", "full_name": "UCI ML Repository \u2014 Diabetes", "description": "This diabetes dataset is from AIM '94 Diabetes patient records were obtained from two sources: an automatic electronic recording device and paper records. The automatic device had an internal clock to timestamp events, whereas the paper records only provided \"logical time\" slots (breakfast, lunch, dinner, bedtime). For paper records, fixed times were assigned to breakfast (08:00), lunch (12:00), dinner (18:00), and bedtime (22:00). Thus paper records have fictitious uniform recording times whereas electronic records have more realistic time stamps.\n\nDiabetes files consist of four fields per record.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/34/diabetes", @@ -5336,14 +5279,17 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "healthcare", + "scientific" + ] }, { "slug": "uci-statlog-german-credit-data", "short_name": "Statlog (German Credit Data)", "full_name": "UCI ML Repository \u2014 Statlog (German Credit Data)", "description": "This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file \"german.data\". \n \nFor algorithms that need numerical attributes, Strathclyde University produced the file \"german.data-numeric\". This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data", @@ -5406,7 +5352,6 @@ "short_name": "Human Activity Recognition Using Smartphones", "full_name": "UCI ML Repository \u2014 Human Activity Recognition Using Smartphones", "description": "Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones", @@ -5466,7 +5411,6 @@ "short_name": "Automobile", "full_name": "UCI ML Repository \u2014 Automobile", "description": "From 1985 Ward's Automotive Yearbook This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process \"symboling\".", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/10/automobile", @@ -5529,7 +5473,6 @@ "short_name": "Mushroom", "full_name": "UCI ML Repository \u2014 Mushroom", "description": "From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/73/mushroom", @@ -5592,7 +5535,6 @@ "short_name": "Estimation of Obesity Levels Based On Eating Habits and Physical Condition ", "full_name": "UCI ML Repository \u2014 Estimation of Obesity Levels Based On Eating Habits and Physical Condition ", "description": "This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition", @@ -5655,7 +5597,6 @@ "short_name": "Abalone", "full_name": "UCI ML Repository \u2014 Abalone", "description": "Predict the age of abalone from physical measurements Predicting the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/1/abalone", @@ -5718,7 +5659,6 @@ "short_name": "Bike Sharing", "full_name": "UCI ML Repository \u2014 Bike Sharing", "description": "This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset", @@ -5774,14 +5714,16 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "transportation" + ] }, { "slug": "uci-individual-household-electric-power-consumption", "short_name": "Individual Household Electric Power Consumption", "full_name": "UCI ML Repository \u2014 Individual Household Electric Power Consumption", "description": "Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months).\nNotes: \n1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3.\n2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows).", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption", @@ -5844,7 +5786,6 @@ "short_name": "SMS Spam Collection", "full_name": "UCI ML Repository \u2014 SMS Spam Collection", "description": "The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research. This corpus has been collected from free or free for research sources at the Internet:\n\n-> A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/228/sms+spam+collection", @@ -5909,7 +5850,6 @@ "short_name": "Online Retail II", "full_name": "UCI ML Repository \u2014 Online Retail II", "description": "A real online retail transaction data set of two years. This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/502/online+retail+ii", @@ -5969,7 +5909,6 @@ "short_name": "Auto MPG", "full_name": "UCI ML Repository \u2014 Auto MPG", "description": "Revised from CMU StatLib library, data concerns city-cycle fuel consumption This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute \"mpg\", 8 of the original instances were removed because they had unknown values for the \"mpg\" attribute. The original dataset is available in the file \"auto-mpg.data-original\".\n\n\"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes.\" (Quinlan, 1993)", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/9/auto+mpg", @@ -6032,7 +5971,6 @@ "short_name": "CDC Diabetes Health Indicators", "full_name": "UCI ML Repository \u2014 CDC Diabetes Health Indicators", "description": "The Diabetes Health Indicators Dataset contains healthcare statistics and lifestyle survey information about people in general along with their diagnosis of diabetes. The 35 features consist of some demographics, lab test results, and answers to survey questions for each patient. The target variable for classification is whether a patient has diabetes, is pre-diabetic, or healthy. Dataset link: https://www.cdc.gov/brfss/annual_data/annual_2014.html", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/891/cdc+diabetes+health+indicators", @@ -6095,7 +6033,6 @@ "short_name": "Online Shoppers Purchasing Intention Dataset", "full_name": "UCI ML Repository \u2014 Online Shoppers Purchasing Intention Dataset", "description": "Of the 12,330 sessions in the dataset,\n84.5% (10,422) were negative class samples that did not\nend with shopping, and the rest (1908) were positive class\nsamples ending with shopping. The dataset consists of feature vectors belonging to 12,330 sessions. \nThe dataset was formed so that each session\nwould belong to a different user in a 1-year period to avoid\nany tendency to a specific campaign, special day, user\nprofile, or period.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset", @@ -6158,7 +6095,6 @@ "short_name": "Spambase", "full_name": "UCI ML Repository \u2014 Spambase", "description": "Classifying Email as Spam or Non-Spam The \"spam\" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography...\n\nThe classification task for this dataset is to determine whether a given email is spam or not.\n\t\nOur collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/94/spambase", @@ -6221,7 +6157,6 @@ "short_name": "MAGIC Gamma Telescope", "full_name": "UCI ML Repository \u2014 MAGIC Gamma Telescope", "description": "Data are MC generated to simulate registration of high energy gamma particles in an atmospheric Cherenkov telescope The data are MC generated (see below) to simulate registration of high energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique. Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks through the atmosphere and gets recorded in the detector, allowing reconstruction of the shower parameters.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/159/magic+gamma+telescope", @@ -6284,7 +6219,6 @@ "short_name": "Diabetes 130-US Hospitals for Years 1999-2008", "full_name": "UCI ML Repository \u2014 Diabetes 130-US Hospitals for Years 1999-2008", "description": "The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. Each row concerns hospital records of patients diagnosed with diabetes, who underwent laboratory, medications, and stayed up to 14 days. The goal is to determine the early readmission of the patient within 30 days of discharge.\nThe problem is important for the following reasons. Despite high-quality evidence showing improved clinical outcomes for diabetic patients who receive various preventive and therapeutic interventions, many patients do not receive them. This can be partially attributed to arbitrary diabetes management in hospital environments, which fail to attend to glycemic control. Failure to provide proper diabetes care not only increases the managing costs for the hospitals (as the patients are readmitted) but also impacts the morbidity and mortality of the patients, who may face complications associated with diabetes. The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria.\n(1)\tIt is an inpatient encounter (a hospital admission).\n(2)\tIt is a diabetic encounter, that is, one during which any kind of diabetes was entered into the system as a diagnosis.\n(3)\tThe length of stay was at least 1 day and at most 14 days.\n(4)\tLaboratory tests were performed during the encounter.\n(5)\tMedicati\u2026", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008", @@ -6347,7 +6281,6 @@ "short_name": "Breast Cancer Wisconsin (Original)", "full_name": "UCI ML Repository \u2014 Breast Cancer Wisconsin (Original)", "description": "Original Wisconsin Breast Cancer Database Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:\n\nGroup 1: 367 instances (January 1989)\nGroup 2: 70 instances (October 1989)\nGroup 3: 31 instances (February 1990)\nGroup 4: 17 instances (April 1990)\nGroup 5: 48 instances (August 1990)\nGroup 6: 49 instances (Updated January 1991)\nGroup 7: 31 instances (June 1991)\nGroup 8: 86 instances (November 1991)\n-----------------------------------------\u2026", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+original", @@ -6410,7 +6343,6 @@ "short_name": "Forest Fires", "full_name": "UCI ML Repository \u2014 Forest Fires", "description": "This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data (see details at: http://www.dsi.uminho.pt/~pcortez/forestfires). In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function.\n Then, several Data Mining methods were applied. After fitting the models, the outputs were\n post-processed with the inverse of the ln(x+1) transform. Four different input setups were\n used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two\n regression metrics were measured: MAD and RMSE.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/162/forest+fires", @@ -6473,7 +6405,6 @@ "short_name": "Breast Cancer", "full_name": "UCI ML Repository \u2014 Breast Cancer", "description": "This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.) This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/14/breast+cancer", @@ -6536,7 +6467,6 @@ "short_name": "Heart Failure Clinical Records", "full_name": "UCI ML Repository \u2014 Heart Failure Clinical Records", "description": "This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features. A detailed description of the dataset can be found in the Dataset section of the following paper: \n\nDavide Chicco, Giuseppe Jurman: \"Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone\". BMC Medical Informatics and Decision Making 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records", @@ -6599,7 +6529,6 @@ "short_name": "Dry Bean", "full_name": "UCI ML Repository \u2014 Dry Bean", "description": "Images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains. Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/602/dry+bean+dataset", @@ -6662,7 +6591,6 @@ "short_name": "Credit Approval", "full_name": "UCI ML Repository \u2014 Credit Approval", "description": "This data concerns credit card applications; good mix of attributes This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.\n \nThis dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/27/credit+approval", @@ -6725,7 +6653,6 @@ "short_name": "Census Income", "full_name": "UCI ML Repository \u2014 Census Income", "description": "Predict whether income exceeds $50K/yr based on census data. Also known as Adult dataset. Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))\n\nPrediction task is to determine whether a person makes over 50K a year.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/20/census+income", @@ -6788,7 +6715,6 @@ "short_name": "Chronic Kidney Disease", "full_name": "UCI ML Repository \u2014 Chronic Kidney Disease", "description": "This dataset can be used to predict the chronic kidney disease and it can be collected from the hospital nearly 2 months of period. We use the following representation to collect the dataset\n age\t\t-\tage\t\n\t\t\tbp\t\t-\tblood pressure\n\t\t\tsg\t\t-\tspecific gravity\n\t\t\tal\t\t- \talbumin\n\t\t\tsu\t\t-\tsugar\n\t\t\trbc\t\t-\tred blood cells\n\t\t\tpc\t\t-\tpus cell\n\t\t\tpcc\t\t-\tpus cell clumps\n\t\t\tba\t\t-\tbacteria\n\t\t\tbgr\t\t-\tblood glucose random\n\t\t\tbu\t\t-\tblood urea\n\t\t\tsc\t\t-\tserum creatinine\n\t\t\tsod\t\t-\tsodium\n\t\t\tpot\t\t-\tpotassium\n\t\t\themo\t\t-\themoglobin\n\t\t\tpcv\t\t-\tpacked cell volume\n\t\t\twc\t\t-\twhite blood cell count\n\t\t\trc\t\t-\tred blood cell count\n\t\t\thtn\t\t-\thypertension\n\t\t\tdm\t\t-\tdiabetes mellitus\n\t\t\tcad\t\t-\tcoronary artery disease\u2026", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease", @@ -6851,7 +6777,6 @@ "short_name": "Energy Efficiency", "full_name": "UCI ML Repository \u2014 Energy Efficiency", "description": "This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters. We perform energy analysis using 12 different building shapes simulated in Ecotect. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/242/energy+efficiency", @@ -6914,7 +6839,6 @@ "short_name": "Concrete Compressive Strength", "full_name": "UCI ML Repository \u2014 Concrete Compressive Strength", "description": "Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. Number of instances \t1030\nNumber of Attributes\t9\nAttribute breakdown\t8 quantitative input variables, and 1 quantitative output variable\nMissing Attribute Values\tNone", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength", @@ -6977,7 +6901,6 @@ "short_name": "Real Estate Valuation", "full_name": "UCI ML Repository \u2014 Real Estate Valuation", "description": "The real estate valuation is a regression problem. The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan. The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan. The \u00e2\u20ac\u0153real estate valuation\u00e2\u20ac\u009d is a regression problem. The data set was randomly split into the training data set (2/3 samples) and the testing data set (1/3 samples).", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set", @@ -7040,7 +6963,6 @@ "short_name": "AI4I 2020 Predictive Maintenance Dataset", "full_name": "UCI ML Repository \u2014 AI4I 2020 Predictive Maintenance Dataset", "description": "The AI4I 2020 Predictive Maintenance Dataset is a synthetic dataset that reflects real predictive maintenance data encountered in industry. Since real predictive maintenance datasets are generally difficult to obtain and in particular difficult to publish, we present and provide a synthetic dataset that reflects real predictive maintenance encountered in industry to the best of our knowledge.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset", @@ -7103,7 +7025,6 @@ "short_name": "Wholesale customers", "full_name": "UCI ML Repository \u2014 Wholesale customers", "description": "The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/292/wholesale+customers", @@ -7166,7 +7087,6 @@ "short_name": "Parkinsons", "full_name": "UCI ML Repository \u2014 Parkinsons", "description": "Oxford Parkinson's Disease Detection Dataset This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals (\"name\" column). The main aim of the data is to discriminate healthy people from those with PD, according to \"status\" column which is set to 0 for healthy and 1 for PD. \n\nThe data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/174/parkinsons", @@ -7229,7 +7149,6 @@ "short_name": "Thyroid Disease", "full_name": "UCI ML Repository \u2014 Thyroid Disease", "description": "10 separate databases from Garavan Institute # From Garavan Institute\n# Documentation: as given by Ross Quinlan\n# 6 databases from the Garavan Institute in Sydney, Australia\n# Approximately the following for each database:\n\n ** 2800 training (data) instances and 972 test instances\n ** Plenty of missing data\n ** 29 or so attributes, either Boolean or continuously-valued \n\n# 2 additional databases, also from Ross Quinlan, are also here\n\n ** Hypothyroid.data and sick-euthyroid.data\n ** Quinlan believes that these databases have been corrupted\n ** Their format is highly similar to the other databases \n\n# 1 mo\u2026", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/102/thyroid+disease", @@ -7294,7 +7213,6 @@ "short_name": "Seoul Bike Sharing Demand", "full_name": "UCI ML Repository \u2014 Seoul Bike Sharing Demand", "description": "The dataset contains count of public bicycles rented per hour in the Seoul Bike Sharing System, with corresponding weather data and holiday information Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/560/seoul+bike+sharing+demand", @@ -7357,7 +7275,6 @@ "short_name": "Seeds", "full_name": "UCI ML Repository \u2014 Seeds", "description": "Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes. The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for\nthe experiment. High quality visualization of the internal kernel structure was detected using a soft X-ray technique. It is non-destructive and considerably cheaper than other more sophisticated imaging techniques like scanning microscopy or laser technology. The images were recorded on 13x18 cm X-ray KODAK plates.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/236/seeds", @@ -7410,14 +7327,19 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "scientific" + ], + "showcase": [ + "encoding" + ] }, { "slug": "uci-electricityloaddiagrams20112014", "short_name": "ElectricityLoadDiagrams20112014", "full_name": "UCI ML Repository \u2014 ElectricityLoadDiagrams20112014", "description": "This data set contains electricity consumption of 370 points/clients. Data set has no missing values.\nValues are in kW of each 15 min. To convert values in kWh values must be divided by 4.\nEach column represent one client. Some clients were created after 2011. In these cases consumption were considered zero.\nAll time labels report to Portuguese hour. However all days present 96 measures (24*4). Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points. Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014", @@ -7481,7 +7403,6 @@ "short_name": "Phishing Websites", "full_name": "UCI ML Repository \u2014 Phishing Websites", "description": "This dataset collected mainly from: PhishTank archive, MillerSmiles archive, Google\u00e2\u20ac\u2122s searching operators. One of the challenges faced by our research was the unavailability of reliable training datasets. In fact this challenge faces any researcher in the field. However, although plenty of articles about predicting phishing websites have been disseminated these days, no reliable training dataset has been published publically, may be because there is no agreement in literature on the definitive features that characterize phishing webpages, hence it is difficult to shape a dataset that covers all possible features.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/327/phishing+websites", @@ -7544,7 +7465,6 @@ "short_name": "Optical Recognition of Handwritten Digits", "full_name": "UCI ML Repository \u2014 Optical Recognition of Handwritten Digits", "description": "Two versions of this database available; see folder We used preprocessing programs made available by NIST to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L.", - "family": "uci", "license": { "spdx": "CC-BY-4.0", "source_url": "https://archive.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits", @@ -7607,7 +7527,6 @@ "short_name": "CA Housing", "full_name": "California Housing Prices", "description": "Median house prices for California districts derived from the 1990 census. This is the dataset used in the second chapter of Aur\u00e9lien G\u00e9ron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. \u2014 adapted from the dataset's Kaggle description (camnugent/california-housing-prices).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "http://lib.stat.cmu.edu/datasets/houses.zip", @@ -7665,7 +7584,6 @@ "short_name": "Medical Cost", "full_name": "Medical Cost Personal Datasets", "description": "Health Insurance Premium charges based on Gender, BMI and other characteristics. This Dataset is something I found online when I wanted to practice regression models. It is an openly available online dataset at multiple places. Though I do not know the exact origin and collection methodology of the data, I would recommend this dataset to everybody who is just beginning their journey in Data science. \u2014 adapted from the dataset's Kaggle description (simranjain17/insurance).", - "family": "kaggle-upstream", "license": { "spdx": "DbCL-1.0", "source_url": "https://github.com/stedy/Machine-Learning-with-R-datasets", @@ -7723,7 +7641,6 @@ "short_name": "FitBit Tracker", "full_name": "FitBit Fitness Tracker Data", "description": "Pattern recognition with tracker data: : Improve Your Overall Health. This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). \u2014 adapted from the dataset's Kaggle description (arashnic/fitbit).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://zenodo.org/record/53894#.YMoUpnVKiP9", @@ -7781,7 +7698,6 @@ "short_name": "German Traffic Signs", "full_name": "GTSRB - German Traffic Sign Recognition Benchmark", "description": "Multi-class, single-image classification challenge. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties: - Single-image, multi-class classification problem - More than 40 classes - More than 50,000 images in total - Large, lifelike database Acknowledgements [INI Benchmark Website][1] [1]: http://benchmark.ini.rub.de/ \u2014 adapted from the dataset's Kaggle description (meowmeowmeowmeowmeow/gtsrb-german-traffic-sign).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "http://benchmark.ini.rub.de/", @@ -7842,7 +7758,6 @@ "short_name": "F1 Championship", "full_name": "Formula 1 World Championship (1950 - 2024)", "description": "F1 race data from 1950 to 2024. Formula 1 (a.k.a. F1 or Formula One) is the highest class of single-seater auto racing sanctioned by the F\u00e9d\u00e9ration Internationale de l'Automobile (FIA) and owned by the Formula One Group. The FIA Formula One World Championship has been one of the premier forms of racing around the world since its inaugural season in 1950. \u2014 adapted from the dataset's Kaggle description (rohanrao/formula-1-world-championship-1950-2020).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://ergast.com/mrd/", @@ -7900,7 +7815,6 @@ "short_name": "Diabetes Health", "full_name": "Diabetes Health Indicators Dataset", "description": "253,680 survey responses from cleaned BRFSS 2015 + balanced dataset. Diabetes is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. Diabetes is a serious chronic disease in which individuals lose the ability to effectively regulate levels of glucose in the blood, and can lead to reduced quality of life and life expectancy. After different foods are broken down into sugars during digestion, the sugars are then released into the bloodstream. \u2014 adapted from the dataset's Kaggle description (alexteboul/diabetes-health-indicators-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.cdc.gov/brfss/annual_data/annual_2015.html", @@ -7961,7 +7875,6 @@ "short_name": "Football Results", "full_name": "International football results from 1872 to 2026", "description": "An up-to-date dataset of over 49,000 international football results. Well, what happened was that I was looking for a semi-definite easy-to-read list of international football matches and couldn't find anything decent. So I took it upon myself to collect it for my own use. I might as well share it. \u2014 adapted from the dataset's Kaggle description (martj42/international-football-results-from-1872-to-2017).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/martj42/international_results", @@ -8019,7 +7932,6 @@ "short_name": "Palmer Penguins", "full_name": "Palmer Archipelago (Antarctica) penguin data", "description": "Drop in replacement for Iris Dataset. Please refer to the official Github page for details and license information. The details below have also been taken from there. Artwork: @allisonhorst Palmer Archipelago (Antarctica) penguin data Data were collected and made available by Dr. \u2014 adapted from the dataset's Kaggle description (parulpandey/palmer-archipelago-antarctica-penguin-data).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://archive.ics.uci.edu/", @@ -8077,7 +7989,6 @@ "short_name": "SF Salaries", "full_name": "SF Salaries", "description": "Explore San Francisco city employee salary data. One way to understand how a city government works is by looking at who it employs and how its employees are compensated. This data contains the names, job title, and compensation for San Francisco city employees on an annual basis from 2011 to 2014. [](https://www.kaggle.com/benhamner/d/kaggle/sf-salaries/exploring-the-sf-city-salary-data) Exploration Ideas To help get you started, here are some data exploration ideas: - How have salaries changed over time between different groups of people? \u2014 adapted from the dataset's Kaggle description (kaggle/sf-salaries).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://data.sfgov.org/City-Management-and-Ethics/Employee-Compensation/88g8-5mnd", @@ -8128,14 +8039,17 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "finance", + "government" + ] }, { "slug": "personal-key-indicators-of-heart-disease", "short_name": "Heart Disease Indicators", "full_name": "Indicators of Heart Disease (2022 UPDATE)", "description": "2022 annual CDC survey data of 400k+ adults related to their health status. According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. \u2014 adapted from the dataset's Kaggle description (kamilpytlak/personal-key-indicators-of-heart-disease).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.cdc.gov/brfss/annual_data/annual_2022.html", @@ -8196,7 +8110,6 @@ "short_name": "Marketing Analytics", "full_name": "Marketing Analytics", "description": "Practice Exploratory and Statistical Analysis with Marketing Data. This data is publicly available on GitHub here. It can be utilized for EDA, Statistical Analysis, and Visualizations. The data set ifood_df.csv consists of 2206 customers of XYZ company with data on: - Customer profiles - Product preferences - Campaign successes/failures - Channel performance Acknowledgement I do not own this dataset. \u2014 adapted from the dataset's Kaggle description (jackdaoud/marketing-data).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/nailson/ifood-data-business-analyst-test", @@ -8254,7 +8167,6 @@ "short_name": "Walmart", "full_name": "Walmart Dataset", "description": "Walmart Store Sales Prediction - Regression Problem. One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. \u2014 adapted from the dataset's Kaggle description (yasserh/walmart-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg", @@ -8312,7 +8224,6 @@ "short_name": "Lung Cancer", "full_name": "Lung Cancer", "description": "Does Smoking cause Lung Cancer.", - "family": "uci", "license": { "spdx": "CC0-1.0", "source_url": "https://archive.ics.uci.edu/", @@ -8376,7 +8287,6 @@ "short_name": "Movie Industry", "full_name": "Movie Industry", "description": "Movies dataset for recommendation system. Welcome to the Movie Recommendation Dataset! This dataset is curated for building recommendation systems in the fascinating world of movies. Whether you're a data scientist, machine learning enthusiast, or a movie buff, this dataset provides a rich collection of information about various movies, offering endless possibilities for analysis and recommendation system development. \u2014 adapted from the dataset's Kaggle description (abdallahwagih/movies).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/Juanets/movie-stats", @@ -8434,7 +8344,6 @@ "short_name": "Chess (Lichess)", "full_name": "Lichess Standard Rated Games (2013-01 monthly dump)", "description": "20,000+ Lichess Games, including moves, victor, rating, opening details and more. General Info This is a set of just over 20,000 games collected from a selection of users on the site Lichess.org, and how to collect more. I will also upload more games in the future as I collect them. I collected this data using the [Lichess API][2], which enables collection of any given users game history. \u2014 adapted from the dataset's Kaggle description (datasnaek/chess).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://database.lichess.org/", @@ -8492,7 +8401,6 @@ "short_name": "Disease Symptoms", "full_name": "Disease Symptom Prediction", "description": "helps to create a disease prediction or healthcare system. A dataset to provide the students a source to create a healthcare related system. A project on the same using double Decision Tree Classifiication is available at : https://github.com/itachi9604/healthcare-chatbot Get_dummies processed file will be available at https://www.kaggle.com/rabisingh/symptom-checker?select=Training.csv Content There are columns containing diseases, their symptoms , precautions to be taken, and their weights. This dataset can be easily cleaned by using file handling in any language. \u2014 adapted from the dataset's Kaggle description (itachi9604/disease-symptom-description-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://github.com/itachi9604/healthcare-chatbot", @@ -8550,7 +8458,6 @@ "short_name": "Uber Pickups NYC", "full_name": "Uber Pickups in New York City", "description": "Trip data for over 20 million Uber (and other for-hire vehicle) trips in NYC. Uber TLC FOIL Response This directory contains data on over 4.5 million Uber pickups in New York City from April to September 2014, and 14.3 million more Uber pickups from January to June 2015. Trip-level data on 10 other for-hire vehicle (FHV) companies, as well as aggregated data for 329 FHV companies, is also included. All the files are as they were received on August 3, Sept. \u2014 adapted from the dataset's Kaggle description (fivethirtyeight/uber-pickups-in-new-york-city).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/fivethirtyeight/uber-tlc-foil-response", @@ -8608,7 +8515,6 @@ "short_name": "NBA Database", "full_name": "NBA Database", "description": "NBA rookies classification. This is publicly available data that has been scraped from NBA statistics. The data is from between 1990 and 2016. Each row describes the performance of a basketball player during their first ('rookie') year. \u2014 adapted from the dataset's Kaggle description (tombutton/basketball).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://github.com/wyattowalsh/nba-db", @@ -8666,7 +8572,6 @@ "short_name": "Glass Classification", "full_name": "Glass Classification", "description": "Can you correctly identify glass type?", - "family": "uci", "license": { "spdx": "DbCL-1.0", "source_url": "https://archive.ics.uci.edu/", @@ -8730,7 +8635,6 @@ "short_name": "120 years of Olympic history: athletes and results", "full_name": "120 years of Olympic history: athletes and results", "description": "basic bio data on athletes and medal results from Athens 1896 to Rio 2016. This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. I scraped this data from www.sports-reference.com in May 2018. The R code I used to scrape and wrangle the data is on GitHub. \u2014 adapted from the dataset's Kaggle description (heesoo37/120-years-of-olympic-history-athletes-and-results).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/rgriff23/Olympic_history", @@ -8781,14 +8685,16 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "sports" + ] }, { "slug": "airbnb-prices-in-european-cities", "short_name": "Airbnb Prices in European Cities", "full_name": "Airbnb Prices in European Cities", "description": "Determinants of Price by Room Type, Location, Cleanliness Rating, and More. Each listing is evaluated for various attributes such as room types, cleanliness and satisfaction ratings, bedrooms, distance from the city centre, and more to capture an in-depth understanding of Airbnb prices on both weekdays and weekends. Using spatial econometric methods, we analyse and identify the determinants of Airbnb prices across these cities. We hope that this data set offers insight into how global markets are affected by social dynamics and geographical factors which in turn determine pricing strategies for optimal profitability! \u2014 adapted from the dataset's Kaggle description (thedevastator/airbnb-prices-in-european-cities).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://zenodo.org/record/4446043#.Y9Y9ENJBwUE", @@ -8846,7 +8752,6 @@ "short_name": "Airbnb Open Data", "full_name": "Airbnb Open Data", "description": "New York Airbnb Open Data. New York City Airbnb Data Cleaning Airbnb, Inc is an American company that operates an online marketplace for lodging, primarily homestays for vacation rentals, and tourism activities. Based in San Francisco, California, the platform is accessible via website and mobile app. Airbnb does not own any of the listed properties; instead, it profits by receiving commission from each booking. \u2014 adapted from the dataset's Kaggle description (arianazmoudeh/airbnbopendata).", - "family": "kaggle-upstream", "license": { "spdx": "ODbL-1.0", "source_url": "http://insideairbnb.com/explore/", @@ -8904,7 +8809,6 @@ "short_name": "Bank Account Fraud Dataset Suite (NeurIPS 2022)", "full_name": "Bank Account Fraud Dataset Suite (NeurIPS 2022)", "description": "Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. The Bank Account Fraud (BAF) suite of datasets has been published at NeurIPS 2022 and it comprises a total of 6 different synthetic bank account fraud tabular datasets. BAF is a realistic, complete, and robust test bed to evaluate novel and existing methods in ML and fair ML, and the first of its kind! Each dataset is composed of: - 1 million instances; - 30 realistic features used in the fraud detection use-case; - A column of \u201cmonth\u201d, providing temporal information about the dataset; - Protected attributes, (age group, employment status and % income). \u2014 adapted from the dataset's Kaggle description (sgpjesus/bank-account-fraud-dataset-neurips-2022).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-NC-SA-4.0", "source_url": "https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf", @@ -8962,7 +8866,6 @@ "short_name": "Behavioral Risk Factor Surveillance System", "full_name": "Behavioral Risk Factor Surveillance System", "description": "Public health surveys of 400k people from 2011-2015. The objective of the BRFSS is to collect uniform, state-specific data on preventive health practices and risk behaviors that are linked to chronic diseases, injuries, and preventable infectious diseases in the adult population. Factors assessed by the BRFSS include tobacco use, health care coverage, HIV/AIDS knowledge or prevention, physical activity, and fruit and vegetable consumption. Data are collected from a random sample of adults (one per household) through a telephone survey. \u2014 adapted from the dataset's Kaggle description (cdc/behavioral-risk-factor-surveillance-system).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.cdc.gov/brfss/", @@ -9023,7 +8926,6 @@ "short_name": "San Francisco Building Permits", "full_name": "San Francisco Building Permits", "description": "5 years and 200k building permits. Background A building permit is an official approval document issued by a governmental agency that allows you or your contractor to proceed with a construction or remodeling project on one's property. For more details go to https://www.thespruce.com/what-is-a-building-permit-1398344. Each city or county has its own office related to buildings, that can do multiple functions like issuing permits, inspecting buildings to enforce safety measures, modifying rules to accommodate needs of the growing population etc. \u2014 adapted from the dataset's Kaggle description (aparnashastry/building-permit-applications-data).", - "family": "kaggle-upstream", "license": { "spdx": "DbCL-1.0", "source_url": "https://data.sfgov.org/Housing-and-Buildings/Building-Permits/i98e-djp9/data", @@ -9081,7 +8983,6 @@ "short_name": "Cardiovascular Diseases Risk Prediction Dataset", "full_name": "Cardiovascular Diseases Risk Prediction Dataset", "description": "The 2021 BRFSS Dataset from CDC. CVDs Risk Prediction Using Personal Lifestyle Factors - Check my notebook here! \ud83d\ude04 - Access the web application I created here! \ud83d\udd17 BRFSS Dataset The Behavioral Risk Factor Surveillance System (BRFSS) is the nation\u2019s premier system of health-related telephone surveys that collect state data about U.S. \u2014 adapted from the dataset's Kaggle description (alphiree/cardiovascular-diseases-risk-prediction-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.cdc.gov/brfss/", @@ -9142,7 +9043,6 @@ "short_name": "CoronaHack -Chest X-Ray-Dataset", "full_name": "CoronaHack -Chest X-Ray-Dataset", "description": "Classify the X Ray image which is having Corona. Corona - COVID19 virus affects the respiratory system of healthy individual & Chest X -Ray is one of the important imaging methods to identify the corona virus. With the Chest X - Ray dataset, Develop a Machine Learning Model to classify the X Rays of Healthy vs Pneumonia (Corona) affected patients & this model powers the AI application to test the Corona Virus in Faster Phase. Postdoctoral Fellow, Mila, University of Montreal for the dataset below for corona dataset & 80% dataset collected from different sources. \u2014 adapted from the dataset's Kaggle description (praveengovi/coronahack-chest-xraydataset).", - "family": "kaggle-upstream", "license": { "spdx": "Attribution 4.0 International (CC BY 4.0)", "source_url": "https://github.com/ieee8023/covid-chestxray-dataset", @@ -9200,7 +9100,6 @@ "short_name": "COVID-19 World Vaccination Progress", "full_name": "COVID-19 World Vaccination Progress", "description": "Daily and Total Vaccination for COVID-19 in the World from Our World in Data. Data is collected daily from Our World in Data GitHub repository for covid-19, merged and uploaded. Country level vaccination data is gathered and assembled in one single file. Then, this data file is merged with locations data file to include vaccination sources information. \u2014 adapted from the dataset's Kaggle description (gpreda/covid-world-vaccination-progress).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/owid/covid-19-data", @@ -9258,7 +9157,6 @@ "short_name": "COVID-19 data from John Hopkins University", "full_name": "COVID-19 data from John Hopkins University", "description": "Updated daily at 6am UTC in both raw and convenient form. This is a daily updating version of COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The data updates every day at 6am UTC, which updates just after the raw JHU data typically updates. I'm making it available in both a raw form (files with the prefix RAW) and convenient form (files prefixed with CONVENIENT). \u2014 adapted from the dataset's Kaggle description (antgoldbloom/covid19-data-from-john-hopkins-university).", - "family": "kaggle-upstream", "license": { "spdx": "Attribution 4.0 International (CC BY 4.0)", "source_url": "https://github.com/CSSEGISandData/COVID-19", @@ -9316,7 +9214,6 @@ "short_name": "Electric Motor Temperature", "full_name": "Electric Motor Temperature", "description": "185 hrs recordings from a permanent magnet synchronous motor (PMSM). UPDATE 26.04.2021 All data is deanonymized now. Moreover, 17 additional measurement profiles were added, expanding the dataset from 138 hours to 185 hours of records. The data set comprises several sensor data collected from a permanent magnet synchronous motor (PMSM) deployed on a test bench. \u2014 adapted from the dataset's Kaggle description (wkirgsn/electric-motor-temperature).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://github.com/upb-lea/deep-pmsm", @@ -9374,7 +9271,6 @@ "short_name": "Emissions by Country", "full_name": "Emissions by Country", "description": "Quantifying Sources and Emission Levels. It contains information on total emissions as well as from coal, oil, gas, cement production and flaring, and other sources. The data also provides a breakdown of per capita CO2 emission per country - showing which countries are leading in pollution levels and identifying potential areas where reduction efforts should be concentrated. This dataset is essential for anyone who wants to get informed about their own environmental footprint or conduct research on international development trends More Datasets > For more datasets, click here. \u2014 adapted from the dataset's Kaggle description (thedevastator/global-fossil-co2-emissions-by-country-2002-2022).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://zenodo.org/record/7215364", @@ -9432,7 +9328,6 @@ "short_name": "goodbooks-10k", "full_name": "goodbooks-10k", "description": "Ten thousand books, one million ratings. Also books marked to read, and tags. This version of the dataset is obsolete. It contains duplicate ratings (same userid,bookid), as reported by Philipp Spachtholz in his illustrious notebook. The current version has duplicates removed, and more ratings (six million), sorted by time. \u2014 adapted from the dataset's Kaggle description (zygmunt/goodbooks-10k).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://github.com/zygmuntz", @@ -9496,7 +9391,6 @@ "short_name": "Heart Disease Health Indicators Dataset", "full_name": "Heart Disease Health Indicators Dataset", "description": "253,680 survey responses from cleaned BRFSS 2015 - binary classification. Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year \u2014 making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease. \u2014 adapted from the dataset's Kaggle description (alexteboul/heart-disease-health-indicators-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.cdc.gov/brfss/annual_data/annual_2015.html", @@ -9557,7 +9451,6 @@ "short_name": "Housing Prices Dataset", "full_name": "Housing Prices Dataset", "description": "Housing Prices Prediction - Regression Problem. A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model? \u2014 adapted from the dataset's Kaggle description (yasserh/housing-prices-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg", @@ -9615,7 +9508,6 @@ "short_name": "Iowa Liquor Sales", "full_name": "Iowa Liquor Sales", "description": "12 million alcoholic beverage sales in the Midwest. The Iowa Department of Commerce requires that every store that sells alcohol in bottled form for off-the-premises consumption must hold a class \"E\" liquor license (an arrangement typical of most of the state alcohol regulatory bodies). All alcoholic sales made by stores registered thusly with the Iowa Department of Commerce are logged in the Commerce department system, which is in turn published as open data by the State of Iowa. This dataset contains information on the name, kind, price, quantity, and location of sale of sales of individual containers or packages of containers of alcoholic beverages. \u2014 adapted from the dataset's Kaggle description (residentmario/iowa-liquor-sales).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://gist.github.com/dannguyen/18ed71d3451d147af414", @@ -9675,7 +9567,6 @@ "short_name": "Exoplanet Hunting in Deep Space", "full_name": "Exoplanet Hunting in Deep Space", "description": "Kepler labelled time series data. The Search for New Earths ------------------------- GitHub The data describe the change in flux (light intensity) of several thousand stars. Each star has a binary label of 2 or 1. 2 indicated that that the star is confirmed to have at least one exoplanet in orbit; some observations are in fact multi-planet systems. \u2014 adapted from the dataset's Kaggle description (keplersmachines/kepler-labelled-time-series-data).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/winterdelta/keplersmachines", @@ -9733,7 +9624,6 @@ "short_name": "Loan Default Dataset", "full_name": "Loan Default Dataset", "description": "Loan Default Classification Problem. Banks earn a major revenue from lending loans. But it is often associated with risk. The borrower's may default on the loan. \u2014 adapted from the dataset's Kaggle description (yasserh/loan-default-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://raw.githubusercontent.com/Masterx-AI/Project_Loan_Default_Risk_Expectancy_/main/loan.jpg", @@ -9791,7 +9681,6 @@ "short_name": "mlcourse.ai", "full_name": "mlcourse.ai", "description": "Datasets and notebooks of the open Machine Learning course mlcourse.ai. mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky (yorko). Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. \u2014 adapted from the dataset's Kaggle description (kashnitsky/mlcourse).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-NC-SA-4.0", "source_url": "https://github.com/Yorko/mlcourse.ai}},", @@ -9849,7 +9738,6 @@ "short_name": "New York City Airbnb Open Data", "full_name": "New York City Airbnb Open Data", "description": "Airbnb listings and metrics in NYC, NY, USA (2019). Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019. This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions. \u2014 adapted from the dataset's Kaggle description (dgomonov/new-york-city-airbnb-open-data).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "http://data.insideairbnb.com/united-states/ny/new-york-city/", @@ -9907,7 +9795,6 @@ "short_name": "NIPS Papers", "full_name": "NIPS Papers", "description": "Titles, authors, abstracts, and extracted text for all NIPS papers (1987-2017). Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. It covers topics ranging from deep learning and computer vision to cognitive science and reinforcement learning. This dataset includes the title, authors, abstracts, and extracted text for all NIPS papers to date (ranging from the first 1987 conference to the current 2016 conference). \u2014 adapted from the dataset's Kaggle description (benhamner/nips-papers).", - "family": "kaggle-upstream", "license": { "spdx": "ODbL-1.0", "source_url": "https://github.com/benhamner/nips-papers", @@ -9968,7 +9855,6 @@ "short_name": "NYC Parking Tickets", "full_name": "NYC Parking Tickets", "description": "42.3M Rows of Parking Ticket Data, Aug 2013-June 2017. The NYC Department of Finance collects data on every parking ticket issued in NYC (~10M per year!). This data is made publicly available to aid in ticket resolution and to guide policymakers. There are four files, covering Aug 2013-June 2017. \u2014 adapted from the dataset's Kaggle description (new-york-city/nyc-parking-tickets).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://data.cityofnewyork.us/City-Government/Parking-Violations-Issued-Fiscal-Year-2024/pvqr-7yc4", @@ -10026,7 +9912,6 @@ "short_name": "NYC Property Sales", "full_name": "NYC Property Sales", "description": "A year's worth of properties sold on the NYC real estate market. This dataset is a record of every building or building unit (apartment, etc.) sold in the New York City property market over a 12-month period. This dataset contains the location, address, type, sale price, and sale date of building units sold. A reference on the trickier fields: BOROUGH: A digit code for the borough the property is located in; in order these are Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5). \u2014 adapted from the dataset's Kaggle description (new-york-city/nyc-property-sales).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.nyc.gov/site/finance/property/property-rolling-sales-data.page", @@ -10088,7 +9973,6 @@ "short_name": "Stack Overflow 2018 Developer Survey", "full_name": "Stack Overflow 2018 Developer Survey", "description": "Individual responses on the 2018 Developer Survey fielded by Stack Overflow. Each year, we at Stack Overflow ask the developer community about everything from their favorite technologies to their job preferences. This year marks the eighth year we\u2019ve published our Annual Developer Survey results\u2014with the largest number of respondents yet. Over 100,000 developers took the 30-minute survey in January 2018. \u2014 adapted from the dataset's Kaggle description (stackoverflow/stack-overflow-2018-developer-survey).", - "family": "kaggle-upstream", "license": { "spdx": "DbCL-1.0", "source_url": "https://insights.stackoverflow.com/survey/2018", @@ -10146,7 +10030,6 @@ "short_name": "Titanic Dataset", "full_name": "Titanic Dataset", "description": "Titanic Survival Prediction Dataset. The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered \u201cunsinkable\u201d RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren\u2019t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew. \u2014 adapted from the dataset's Kaggle description (yasserh/titanic-dataset).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/vincentarelbundock/Rdatasets/blob/master/csv/carData/TitanicSurvival.csv", @@ -10204,7 +10087,6 @@ "short_name": "UFC-Fight historical data from 1993 to 2021", "full_name": "UFC-Fight historical data from 1993 to 2021", "description": "Compiled UFC fight, fighter stats and information. UPDATE This dataset got a lot of love from the community and I saw many people asking for an updated version, so I have uploaded the latest scraped and processed data ( as of 21/03/2021). Now it's super easy for anyone to get the latest dataset (Just use a single command), so in case you need bleeding-edge data, or you want to see the code, you can look here. Hope this solves all problems! \u2014 adapted from the dataset's Kaggle description (rajeevw/ufcdata).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/WarrierRajeev/UFC-Predictions", @@ -10262,7 +10144,6 @@ "short_name": "US Accidents (2016 - 2023)", "full_name": "US Accidents (2016 - 2023)", "description": "A Countrywide Traffic Accident Dataset (2016 - 2023). This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. \u2014 adapted from the dataset's Kaggle description (sobhanmoosavi/us-accidents).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-NC-SA-4.0", "source_url": "https://smoosavi.org/datasets/us_accidents", @@ -10323,7 +10204,6 @@ "short_name": "U.S. Airbnb Open Data", "full_name": "U.S. Airbnb Open Data", "description": "Airbnb listings and metrics of regions in the U.S. Since its inception in 2008, Airbnb has disrupted the traditional hospitality industry as more travellers decide to use Airbnb as their primary means of accommodation. Airbnb offers travellers a more unique and personalized way of accommodation and experience. Project I had compiled this dataset for a project of mine on 20th October 2020. \u2014 adapted from the dataset's Kaggle description (kritikseth/us-airbnb-open-data).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "http://insideairbnb.com/get-the-data/", @@ -10381,7 +10261,6 @@ "short_name": "Predict Droughts using Weather & Soil Data", "full_name": "Predict Droughts using Weather & Soil Data", "description": "Predicting continental US drought levels using meteorological & soil data. To make using previous drought scores for prediction easier (e.g. by interpolating), I merged them into one file and set the drought scores to NaN were not available. The US drought monitor is a measure of drought across the US manually created by experts using a wide range of data. \u2014 adapted from the dataset's Kaggle description (cdminix/us-drought-meteorological-data).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://github.com/Epistoteles/predicting-drought", @@ -10439,7 +10318,6 @@ "short_name": "World Energy Consumption", "full_name": "World Energy Consumption", "description": "Consumption of energy by different countries. Data on Energy by Our World in Data Our complete Energy dataset is a collection of key metrics maintained by Our World in Data. It is updated regularly and includes data on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics. The complete Our World in Data Energy dataset \ud83d\uddc2\ufe0f Download our complete Energy dataset : CSV | XLSX | JSON The CSV and XLSX files follow a format of 1 row per location and year. \u2014 adapted from the dataset's Kaggle description (pralabhpoudel/world-energy-consumption).", - "family": "kaggle-upstream", "license": { "spdx": "Attribution 4.0 International (CC BY 4.0)", "source_url": "https://github.com/owid", @@ -10497,7 +10375,6 @@ "short_name": "Zoo Animal Classification", "full_name": "Zoo Animal Classification", "description": "Use Machine Learning Methods to Correctly Classify Animals Based Upon Attributes", - "family": "uci", "license": { "spdx": "DbCL-1.0", "source_url": "https://archive.ics.uci.edu/ml/datasets/Zoo", @@ -10561,7 +10438,6 @@ "short_name": "News Headlines Sarcasm", "full_name": "News Headlines Dataset For Sarcasm Detection", "description": "High quality dataset for the task of Sarcasm and Fake News Detection. Please cite the dataset using the BibTex provided in one of the following sections if you are using it in your research, thank you! Follow me on LinkedIn for commentary on latest AI developments: https://www.linkedin.com/in/misrarishabh/ Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets. \u2014 adapted from the dataset's Kaggle description (rmisra/news-headlines-dataset-for-sarcasm-detection).", - "family": "kaggle-upstream", "license": { "spdx": "Attribution 4.0 International (CC BY 4.0)", "source_url": "https://github.com/rishabhmisra/News-Headlines-Dataset-For-Sarcasm-Detection", @@ -10622,7 +10498,6 @@ "short_name": "Hotel Booking", "full_name": "Hotel Booking Demand", "description": "From the paper: hotel booking demand datasets. Have you ever wondered when the best time of year to book a hotel room is? Or the optimal length of stay in order to get the best daily rate? What if you wanted to predict whether or not a hotel was likely to receive a disproportionately high number of special requests? \u2014 adapted from the dataset's Kaggle description (jessemostipak/hotel-booking-demand).", - "family": "kaggle-upstream", "license": { "spdx": "Attribution 4.0 International (CC BY 4.0)", "source_url": "https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md", @@ -10673,20 +10548,22 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "e-commerce" + ] }, { "slug": "wikipedia-structured-contents", "short_name": "Wikipedia Structured Contents", "full_name": "Wikipedia Structured Contents", "description": "Pre-parsed English and French Wikipedia Articles, Including Infoboxes. Early beta release of pre-parsed English and French Wikipedia articles including infoboxes. This dataset contains all articles of the English and French language editions of Wikipedia, pre-parsed and outputted as structured JSON files with a consistent schema. Each JSON line holds the content of one full Wikipedia article stripped of extra markdown and non-prose sections (references, etc.). \u2014 adapted from the dataset's Kaggle description (wikimedia-foundation/wikipedia-structured-contents).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://www.kaggle.com/datasets/wikimedia-foundation/wikipedia-structured-contents", "redistribution_permitted": true, "attribution_required": true, - "notes": "Uploaded to Kaggle by Wikimedia Foundation \u2014 the Kaggle page is the canonical distribution for the Structured Contents dump. Family = kaggle-upstream because the *uploader* is a well-known org.", + "notes": "Uploaded to Kaggle by Wikimedia Foundation \u2014 the Kaggle page is the canonical distribution for the Structured Contents dump.", "scrape_advisory": null }, "fetch": { @@ -10738,7 +10615,6 @@ "short_name": "Crimes in Boston", "full_name": "Crimes in Boston", "description": "Times, locations, and descriptions of crimes. Crime incident reports are provided by Boston Police Department (BPD) to document the initial details surrounding an incident to which BPD officers respond. This is a dataset containing records from the new crime incident report system, which includes a reduced set of fields focused on capturing the type of incident as well as when and where it occurred. Records begin in June 14, 2015 and continue to September 3, 2018. \u2014 adapted from the dataset's Kaggle description (AnalyzeBoston/crimes-in-boston).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://data.boston.gov/dataset/crime-incident-reports-august-2015-to-date-source-new-system", @@ -10796,7 +10672,6 @@ "short_name": "Countries of the World", "full_name": "CIA World Factbook (JSON mirror \u2192 VARIANT parquet)", "description": "Country names linked to region, population, area size, GDP, mortality and more. World fact sheet, fun to link with other datasets. Information on population, region, area size, infant mortality and more. [Source:][1] All these data sets are made up of data from the US government. \u2014 adapted from the dataset's Kaggle description (fernandol/countries-of-the-world).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://www.cia.gov/library/publications/the-world-factbook/docs/faqs.html", @@ -10857,7 +10732,6 @@ "short_name": "Kepler Exoplanet Search Results", "full_name": "Kepler Exoplanet Search Results", "description": "10000 exoplanet candidates examined by the Kepler Space Observatory. The Kepler Space Observatory is a NASA-build satellite that was launched in 2009. The telescope is dedicated to searching for exoplanets in star systems besides our own, with the ultimate goal of possibly finding other habitable planets besides our own. The original mission ended in 2013 due to mechanical failures, but the telescope has nevertheless been functional since 2014 on a \"K2\" extended mission. \u2014 adapted from the dataset's Kaggle description (nasa/kepler-exoplanet-search-results).", - "family": "kaggle-upstream", "license": { "spdx": "CC0-1.0", "source_url": "https://exoplanetarchive.ipac.caltech.edu/docs/API_kepcandidate_columns.html", @@ -10908,14 +10782,19 @@ "convert": { "vortex": true, "vortex_skip_reason": null - } + }, + "tags": [ + "scientific" + ], + "showcase": [ + "encoding" + ] }, { "slug": "temperature-change", "short_name": "Temperature change", "full_name": "Temperature change", "description": "Global Warming, Temperature Change, Climate Change. Data description The FAOSTAT Temperature Change domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961\u20132023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951\u20131980. \u2014 adapted from the dataset's Kaggle description (sevgisarac/temperature-change).", - "family": "kaggle-upstream", "license": { "spdx": "Attribution 3.0 IGO (CC BY 3.0 IGO)", "source_url": "https://data.giss.nasa.gov/gistemp/", @@ -10976,7 +10855,6 @@ "short_name": "UK Road Safety: Traffic Accidents and Vehicles", "full_name": "UK Road Safety: Traffic Accidents and Vehicles", "description": "Detailed dataset of road accidents and involved vehicles in the UK (2005-2017). The UK government collects and publishes (usually on an annual basis) detailed information about traffic accidents across the country. This information includes, but is not limited to, geographical locations, weather conditions, type of vehicles, number of casualties and vehicle manoeuvres, making this a very interesting and comprehensive dataset for analysis and research. The creation of this dataset was inspired by the one previously published by [Dave Fisher-Hickey][1]. \u2014 adapted from the dataset's Kaggle description (tsiaras/uk-road-safety-accidents-and-vehicles).", - "family": "kaggle-upstream", "license": { "spdx": "DbCL-1.0", "source_url": "https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data", @@ -11034,7 +10912,6 @@ "short_name": "Emotions NLP", "full_name": "Emotions Dataset for NLP", "description": "Emotions dataset for NLP classification tasks. Few questions your emotion classification model can answer based on your customer review What is the sentiment of your customer comment? What is the mood of today's special food ? \u2014 adapted from the dataset's Kaggle description (praveengovi/emotions-dataset-for-nlp).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://www.aclweb.org/anthology/D18-1404/", @@ -11092,7 +10969,6 @@ "short_name": "OSMI Mental Health in Tech 2019", "full_name": "OSMI 2019 Mental Health in Tech Survey", "description": " \u2014 adapted from the dataset's Kaggle description (osmihelp/osmi-mental-health-in-tech-survey-2019).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11154,7 +11030,6 @@ "short_name": "OSMI Mental Health in Tech 2017", "full_name": "OSMI 2017 Mental Health in Tech Survey", "description": "Data on prevalence and attitudes towards mental health among tech workers. OSMI Mental Health in Tech Survey 2017 The 2017 survey aims to measure attitudes towards mental health in the tech workplace, and examine the frequency of mental health disorders among tech workers. How Will This Data Be Used? We are interested in gauging how mental health is viewed within the tech/IT workplace, and the prevalence of certain mental health disorders within the tech industry. \u2014 adapted from the dataset's Kaggle description (osmihelp/osmi-mental-health-in-tech-survey-2017).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11216,7 +11091,6 @@ "short_name": "OSMI Mental Health in Tech 2018", "full_name": "OSMI 2018 Mental Health in Tech Survey", "description": "Data on prevalence and attitudes towards mental health among tech workers. \u2014 adapted from the dataset's Kaggle description (osmihelp/osmi-mental-health-in-tech-survey-2018).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11278,7 +11152,6 @@ "short_name": "OSMI Mental Health in Tech 2023", "full_name": "OSMI 2023 Mental Health in Tech Survey", "description": "Results from the yearly survey. \u2014 adapted from the dataset's Kaggle description (osmihelp/osmi-mental-health-in-tech-survey-2023).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11340,7 +11213,6 @@ "short_name": "OSMI Mental Health in Tech 2016", "full_name": "OSMI 2016 Mental Health in Tech Survey", "description": "Data on prevalence and attitudes towards mental health among tech workers. OSMI Mental Health in Tech Survey 2016 Currently over 1400 responses, the ongoing 2016 survey aims to measure attitudes towards mental health in the tech workplace, and examine the frequency of mental health disorders among tech workers. How Will This Data Be Used? We are interested in gauging how mental health is viewed within the tech/IT workplace, and the prevalence of certain mental health disorders within the tech industry. \u2014 adapted from the dataset's Kaggle description (osmi/mental-health-in-tech-2016).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11402,7 +11274,6 @@ "short_name": "OSMI Mental Health in Tech 2020", "full_name": "OSMI 2020 Mental Health in Tech Survey", "description": "Results from the yearly survey. \u2014 adapted from the dataset's Kaggle description (osmihelp/osmi-2020-mental-health-in-tech-survey-results).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11464,7 +11335,6 @@ "short_name": "OSMI Mental Health in Tech 2021", "full_name": "OSMI 2021 Mental Health in Tech Survey", "description": "Results from the yearly survey. \u2014 adapted from the dataset's Kaggle description (osmihelp/osmh-2021-mental-health-in-tech-survey-results).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11526,7 +11396,6 @@ "short_name": "OSMI Mental Health in Tech 2022", "full_name": "OSMI 2022 Mental Health in Tech Survey", "description": "Results from the yearly survey. \u2014 adapted from the dataset's Kaggle description (osmihelp/osmh-mental-health-in-tech-survey-2022).", - "family": "kaggle-upstream", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://osmhhelp.org/research.html", @@ -11588,7 +11457,6 @@ "short_name": "SQuAD v2", "full_name": "Stanford Question Answering Dataset v2.0", "description": "Stanford Question Answering Dataset v2 \u2014 130k crowdsourced questions about Wikipedia paragraphs, with 50k of them deliberately unanswerable from the given context. Each row pairs a question, the passage it was asked about, and the canonical answer span(s). The v2 release added the unanswerable subset specifically to test whether models know when to abstain \u2014 a longstanding QA-eval weakness.", - "family": "direct", "license": { "spdx": "CC-BY-SA-4.0", "source_url": "https://huggingface.co/datasets/rajpurkar/squad_v2", @@ -11655,7 +11523,6 @@ "short_name": "HellaSwag", "full_name": "HellaSwag \u2014 Commonsense NLI (Zellers et al., ACL 2019)", "description": "70k commonsense-NLI multiple-choice questions: pick the plausible ending to a video-caption or how-to context. Adversarial filtering via models-of-the-day was used to keep the wrong endings hard (Zellers et al., ACL 2019). Standard benchmark for commonsense reasoning in LLMs.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/Rowan/hellaswag", @@ -11722,7 +11589,6 @@ "short_name": "GSM8K", "full_name": "Grade School Math 8K (Cobbe et al., 2021)", "description": "8.5k grade-school math word problems with step-by-step natural-language reasoning in the answer (Cobbe et al., 2021). Each problem requires 2\u20138 elementary arithmetic steps. The standard arithmetic-reasoning eval for chain-of-thought prompting; this is the `main` config (a sibling `socratic` config exists upstream but is omitted here).", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/openai/gsm8k", @@ -11789,7 +11655,6 @@ "short_name": "MMLU", "full_name": "Massive Multitask Language Understanding (Hendrycks et al., 2021)", "description": "57-subject multiple-choice eval covering STEM, humanities, social sciences, and professional topics (Hendrycks et al., 2021). 14k test questions with 4 answer choices each. Loaded from the `all` config (subjects merged into a single parquet with a `subject` column for grouping); the per-subject configs and the `auxiliary_train` split are not pulled in. The most-cited general-knowledge LLM benchmark.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/cais/mmlu", @@ -11849,6 +11714,9 @@ "kind": "paper", "url": "https://arxiv.org/abs/2009.03300" } + ], + "tags": [ + "benchmark" ] }, { @@ -11856,7 +11724,6 @@ "short_name": "Databricks Dolly 15k", "full_name": "databricks-dolly-15k (Conover et al., 2023)", "description": "15k human-written instruction/response pairs across 7 task categories (closed QA, classification, summarization, etc.), generated by Databricks employees specifically for instruction-tuning open LLMs. Distinguished by being entirely human-authored (no model output recycled as data), making it CC-BY-SA-3.0 \u2014 commercially-clear unlike most synthetic instruction corpora.", - "family": "direct", "license": { "spdx": "CC-BY-SA-3.0", "source_url": "https://huggingface.co/datasets/databricks/databricks-dolly-15k", @@ -11923,7 +11790,6 @@ "short_name": "OpenAssistant Conversations (oasst1)", "full_name": "OpenAssistant Conversations Release 1 (K\u00f6pf et al., NeurIPS 2023)", "description": "85k crowd-authored assistant conversation messages organized as tree-structured threads, spanning 35 languages (K\u00f6pf et al., NeurIPS 2023). Each row carries quality, toxicity, and emoji-feedback labels alongside the message text and parent pointer. First public RLHF-grade conversation corpus; powers OpenAssistant and many downstream fine-tunes.", - "family": "direct", "license": { "spdx": "Apache-2.0", "source_url": "https://huggingface.co/datasets/OpenAssistant/oasst1", @@ -11990,7 +11856,6 @@ "short_name": "UltraChat 200k", "full_name": "UltraChat 200k (HuggingFaceH4, 2024)", "description": "200k filtered + formatted multi-turn conversations from the UltraChat corpus, used by HuggingFaceH4 to instruction-tune Zephyr-7b-\u03b2. Each row carries a `messages` field of `list>` for the dialogue turns. Larger and more conversational than the no-robots / dolly-15k cohort; synthetic but stylistically diverse.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k", @@ -12054,6 +11919,12 @@ "kind": "github", "url": "https://github.com/thunlp/UltraChat" } + ], + "tags": [ + "nlp-text" + ], + "showcase": [ + "encoding" ] }, { @@ -12061,7 +11932,6 @@ "short_name": "OpenOrca", "full_name": "OpenOrca (Open-Orca, 2023) \u2014 GPT-4 + GPT-3.5 augmented FLAN", "description": "~4.2M instruction-response pairs generated via the Orca self-augmentation method, drawn from FLAN-Collection prompts answered by GPT-4 (1M) and GPT-3.5 (3.2M). Concatenated into one parquet with a `split` column distinguishing the two model sources.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/Open-Orca/OpenOrca", @@ -12132,7 +12002,6 @@ "short_name": "Cohere Wikipedia Simple (multilingual-v3 embeddings)", "full_name": "Cohere/wikipedia-2023-11-embed-multilingual-v3 \u2014 Simple English subset (1024d)", "description": "Simple English Wikipedia (646k passage-chunked rows) paired with Cohere's embed-multilingual-v3 1024-dimensional embeddings. The `simple` subset of the larger `Cohere/wikipedia-2023-11-embed-multilingual-v3` repo, scoped via `hf_allow_patterns`. Showcases `fixed_size_list` via the cast in `hf_concat_splits`.", - "family": "direct", "license": { "spdx": "Apache-2.0", "source_url": "https://huggingface.co/datasets/Cohere/wikipedia-2023-11-embed-multilingual-v3", @@ -12204,7 +12073,6 @@ "short_name": "BeIR / MS MARCO", "full_name": "BeIR/msmarco \u2014 text-only passage corpus + queries", "description": "MS MARCO repackaged for the BEIR retrieval benchmark: 8.84M passages from the Bing search corpus paired with 510k user queries. Both as `large_string` parquet \u2014 solid showcase for string-heavy datasets and FSST encoding in Vortex. Concat'd into one slug with a `split` column distinguishing corpus vs queries.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/BeIR/msmarco", @@ -12276,7 +12144,6 @@ "short_name": "LAION-400M (metadata)", "full_name": "LAION-400M \u2014 image-text pairs, metadata + CLIP only", "description": "400M image-text pairs scraped from Common Crawl, joined with CLIP-ViT-B/32 similarity scores. This entry pulls only the metadata parquets (URL, caption, height/width, similarity) \u2014 the images themselves are not redistributed by LAION. Gated on Hugging Face: requires accepting LAION's terms once via the dataset page before the API will serve downloads.", - "family": "direct", "license": { "spdx": "CC-BY-4.0", "source_url": "https://laion.ai/blog/laion-400-open-dataset/", @@ -12356,7 +12223,6 @@ "short_name": "C4 (en, validation)", "full_name": "AllenAI/C4 \u2014 Colossal Clean Crawled Corpus, English validation split", "description": "C4 (Raffel et al., JMLR 2020) \u2014 a heavily-filtered scrape of Common Crawl used to pretrain T5. This entry pulls only the 8-shard English validation split (~365k documents), enough for type coverage and as a smoke-test for the Common-Crawl scrape playbook. Flip allow_patterns to `en/c4-train.*.json.gz` to mirror the 327 GB English training set.", - "family": "direct", "license": { "spdx": "ODC-By-1.0", "source_url": "https://huggingface.co/datasets/allenai/c4", @@ -12423,7 +12289,6 @@ "short_name": "CodeParrot Clean (validation)", "full_name": "codeparrot/codeparrot-clean \u2014 validation split", "description": "61k Python source files scraped from MIT/BSD/Apache-licensed GitHub repos by the CodeParrot project. Validation split (142 MB raw .json.gz). Showcases code-corpus shape: string `content`, numeric quality metrics (line_mean, alpha_frac), boolean autogenerated flag, and per-row license attribution.", - "family": "direct", "license": { "spdx": "Apache-2.0", "source_url": "https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid", @@ -12490,7 +12355,6 @@ "short_name": "SlimPajama-6B", "full_name": "DKYoon/SlimPajama-6B \u2014 6B-token deduplicated sample", "description": "6B-token deduplicated subsample of Cerebras's SlimPajama-627B (itself a cleaned subset of RedPajama-1T). 50 parquet shards of `text` + `meta.redpajama_set_name` covering Common Crawl, GitHub, Wikipedia, books, ArXiv. Used as a small reproducible pretraining sample by the LLM research community.", - "family": "direct", "license": { "spdx": "Apache-2.0", "source_url": "https://huggingface.co/datasets/DKYoon/SlimPajama-6B", @@ -12559,7 +12423,6 @@ "short_name": "Fineweb (sample, 10BT)", "full_name": "HuggingFaceFW/fineweb \u2014 10B-token reproducibility sample", "description": "10B-token reproducibility sample of HuggingFace's Fineweb (a 15TB Common-Crawl-filtered English-text corpus released for LLM pretraining research). 15 parquet shards of `text` + deduplication metadata (~32 GB raw). Flip allow_patterns to `sample/100BT/*.parquet` (300 GB) or `sample/350BT/*.parquet` (1 TB) for larger reproducibility samples.", - "family": "direct", "license": { "spdx": "ODC-By-1.0", "source_url": "https://huggingface.co/datasets/HuggingFaceFW/fineweb", @@ -12632,7 +12495,6 @@ "short_name": "Anthropic HH-RLHF (helpful-base)", "full_name": "Anthropic Helpful & Harmless RLHF \u2014 helpful-base subset", "description": "Anthropic's RLHF preference dataset, scoped to the helpful-base subset (~161k pairs of `chosen` / `rejected` assistant responses to the same prompt). Each pair is a complete conversation transcript. First RLHF-preference-data slug in the catalog. Other subsets (harmless-base, helpful-online, helpful-rejection-sampled, red-team-attempts) can be added as sibling slugs.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/Anthropic/hh-rlhf", @@ -12701,7 +12563,6 @@ "short_name": "UltraFeedback (binarized)", "full_name": "HuggingFaceH4/ultrafeedback_binarized \u2014 DPO-ready binarized preferences", "description": "61k DPO/SFT preference triples binarized from the UltraFeedback corpus by HuggingFaceH4. Each row carries `chosen` and `rejected` as `list>` plus per-side score doubles \u2014 a rich showcase for paired list-of-struct columns alongside the UltraChat 200k slug we already ship.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized", @@ -12772,7 +12633,6 @@ "short_name": "No Robots", "full_name": "HuggingFaceH4/no_robots \u2014 10k human-written instruction-tuning examples", "description": "10k high-quality human-written prompt/response pairs from HuggingFaceH4. Each row carries `messages: list>` plus a category label across 10 task types. Smaller but cleaner counterpart to OpenOrca / OpenAssistant in the instruction-tuning corner of the catalog.", - "family": "direct", "license": { "spdx": "CC-BY-NC-4.0", "source_url": "https://huggingface.co/datasets/HuggingFaceH4/no_robots", @@ -12839,7 +12699,6 @@ "short_name": "MMLU-Pro", "full_name": "TIGER-Lab/MMLU-Pro \u2014 improved 12k-question multitask eval", "description": "12k harder multiple-choice questions across 14 categories, with 10 answer options per question (vs MMLU's 4) and chain-of-thought reasoning included. Successor to cais/mmlu we ship as Tier A; small (4 MB) but high-value for LLM evaluation tooling.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro", @@ -12906,7 +12765,6 @@ "short_name": "MMMLU (multilingual MMLU)", "full_name": "openai/MMMLU \u2014 MMLU professionally translated into 14 languages", "description": "MMLU's 14k test questions translated into 14 languages by professional human translators (released by OpenAI). 14 CSV shards keyed by `-` filename; concatenated into one parquet with the language tag preserved as a `split` column. First multilingual-eval slug in the catalog.", - "family": "direct", "license": { "spdx": "MIT", "source_url": "https://huggingface.co/datasets/openai/MMMLU", @@ -12975,7 +12833,6 @@ "short_name": "Wikipedia (English)", "full_name": "wikimedia/wikipedia \u2014 English Wikipedia, 2023-11-01 dump", "description": "Cleaned-text English Wikipedia dump from 2023-11-01 (~6.4M articles). Each row carries `id`, `url`, `title`, `text` from Wikimedia's parquet auto-conversion. Distinct from `cohere-wikipedia-simple-embed` (which ships embeddings of Simple English) \u2014 this is the raw multilingual-friendly text corpus, gated here to English alone to bound the build size. Flip `hf_allow_patterns` to a different `20231101.` for other languages.", - "family": "direct", "license": { "spdx": "CC-BY-SA-3.0", "source_url": "https://huggingface.co/datasets/wikimedia/wikipedia", @@ -13039,6 +12896,12 @@ "kind": "homepage", "url": "https://dumps.wikimedia.org/" } + ], + "tags": [ + "nlp-text" + ], + "showcase": [ + "stress" ] }, { @@ -13046,7 +12909,6 @@ "short_name": "TinyStories", "full_name": "roneneldan/TinyStories (Eldan & Li, 2023)", "description": "~2.1M short synthetic children's stories (3\u20134 paragraphs each) generated by GPT-3.5/4 to study how small language models acquire coherent narrative ability. Two columns: `text` (the story) plus `validation`-flagged subset. Canonical small-LM training corpus.", - "family": "direct", "license": { "spdx": "CDLA-Sharing-1.0", "source_url": "https://huggingface.co/datasets/roneneldan/TinyStories", @@ -13110,6 +12972,9 @@ "kind": "homepage", "url": "https://huggingface.co/datasets/roneneldan/TinyStories" } + ], + "tags": [ + "nlp-text" ] }, { @@ -13117,7 +12982,6 @@ "short_name": "Fineweb-2 (Swedish sample)", "full_name": "HuggingFaceFW/fineweb-2 \u2014 Swedish Latin-script subset (sample)", "description": "Swedish-language subset of HuggingFace's multilingual fineweb-2 (the 1000+-language extension of the original English fineweb). Each row carries `text` plus the same dedup/quality metadata fields as fineweb. Picked Swedish for a moderately-sized representative non-English split; flip `hf_allow_patterns` to `_