Application of Machine Learning Tools in DNA systems

This Data Management Plan (DMP) outlines the strategies for managing, storing, and sharing data generated during the project "Application of Machine Learning Tools in DNA Systems." The project integrates high-throughput genomic data, structural DNA representations, and machine learning model artifacts (including training sets, feature embeddings, and trained weights). To ensure alignment with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles: Raw & Processed Data: Genomic sequences, structural files (e.g., PDB/mmCIF), and preprocessed datasets will be version-controlled and archived in open repositories (e.g., NCBI GEO, Zenodo). Model & Code Artifacts: Training/validation scripts, model checkpoints, and inference pipelines will be hosted on GitHub and permanently snapshot with DOI indexing via Zenodo. Metadata & Standards: Comprehensive metadata will follow standard genomic schemas (e.g., MINSEQE/MIAME) alongside standardized documentation for model architectures, hyperparameters, and performance metrics. Ethical & IP Compliance: All dataset usage complies with relevant open-source licenses and institutional ethical guidelines, ensuring no restricted human genomic data is published without appropriate anonymization and access protocols.