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These results are stored in the `ResultsTable` data type, which extends the `DynamicTable` data type within the base HDMF schema. The `ResultsTable` schema represents each data sample as a row and includes columns for storing model outputs and information about the AI/ML workflow, such as which data were used for training, validation, and testing.\n\nBy leveraging existing HDMF tools and standards, `HDMF-AI` provides a scalable and extensible framework for storing AI results in an accessible, standardized way that is compatible with other HDMF-based data formats, such as [Neurodata Without Borders (NWB)](https://nwb-overview.readthedocs.io/), a popular data standard for neurophysiology, and [HDMF-Seq](https://github.com/exabiome/deep-taxon), a format for storing taxonomic and genomic sequence data. By enabling standardized co-storage of data and AI results, `HDMF-AI` may enhance the reproducibility and explainability of AI for science.\n\n![UML diagram of the HDMF-AI schema. Data types with orange headers are introduced by HDMF-AI. Data types with blue headers are defined in HDMF. Fields colored in gray are optional.](paper/schema.png)\n\n## Installation\n\n```bash\npip install hdmf-ai\n```\n\n## Usage\n\nFor example usage, see `example_usage.ipynb`.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhdmf-dev%2Fhdmf-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhdmf-dev%2Fhdmf-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhdmf-dev%2Fhdmf-ai/lists"}