Update PLAN.md

Author: papayiv

PLAN.md

@@ -40,3 +40,63 @@ DataMetaMap aims to compare datasets within a unified vector space to identify s
 
 - **Demo Examples and Blog Post**  
   Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights.
+
+## Remastered 
+
+### Phase 1: Research and Preparation
+- **Literature Review**  
+  Study existing methods for dataset embedding, similarity measurement, and transferability estimation to identify best practices.
+
+- **Baseline Selection**  
+  Identify and select baseline methods from literature for comparison during benchmarking.
+
+- **Data Collection**  
+  Gather a diverse collection of datasets for experimentation, ensuring they represent various domains and formats.
+
+- **Data Preprocessing Pipeline**  
+  Design and implement preprocessing steps to handle different dataset formats and ensure consistent input for embedding methods.
+
+- **Evaluation Metrics Definition**  
+  Define quantitative metrics to evaluate embedding quality and similarity measurement accuracy.
+
+- **Planning and Specifications**  
+  Define technical specifications and success criteria based on research findings and data availability.
+
+---
+
+### Phase 2: Implementation and Testing
+- **Core Algorithm Development**  
+  Implement algorithms to embed datasets into a shared vector space and compute similarity metrics between them.
+
+- **Baseline Implementations**  
+  Implement selected baseline methods from literature for comparison.
+
+- **Testing and Quality Assurance**  
+  Develop unit and integration tests to validate correctness, reliability, and performance of the implemented methods.
+
+- **Performance Optimization**  
+  Profile and optimize code for memory efficiency and computational speed, especially for large datasets.
+
+- **Error Handling and Logging**  
+  Implement robust error handling and logging mechanisms for debugging and monitoring.
+
+- **Benchmarking and Visualization**  
+  Run benchmarks on collected datasets and produce visual outputs such as similarity matrices to analyze and interpret results.
+
+---
+
+### Phase 3: Documentation and Dissemination
+- **Technical Report**  
+  Document the methodology, experimental setup, and findings in a comprehensive technical report.
+
+- **User and Developer Documentation**  
+  Create detailed documentation for users and contributors, including setup guides and API references.
+
+- **Demo Examples and Blog Post**  
+  Prepare example notebooks or scripts demonstrating real-world use cases, and write an explanatory blog post highlighting project value and insights.
+
+- **Benchmark Results Repository**  
+  Publish benchmark results, precomputed embeddings, and similarity matrices in a public repository for reproducibility.
+
+- **Future Work Roadmap**  
+  Outline potential extensions, improvements, and research directions based on current findings.