{"categories":[{"label":"Medical Science Apps.","url":"https://skillfed.io/packages/category/scientific-engineering-medical-science-apps"}],"enrichment":{"capability":"HDMF provides APIs for defining hierarchical data models, reading and writing data to storage backends like HDF5, and representing structured data as Python objects.","skillfed_tags":["data-serialization","hdf5","scientific-computing"],"use_cases":["Define and enforce a standardized data schema for multi-site neuroscience studies using HDF5 storage.","Build a data interchange format for a scientific collaboration where data must be validated on read and write.","Serialize complex nested Python objects (arrays, metadata, hierarchies) to HDF5 without manual I/O code.","Version and validate scientific datasets against evolving schema definitions.","Create a domain-specific data format (e.g., for medical imaging or genomics) with built-in schema support."],"what_it_does":"HDMF is a Python framework for working with hierarchical scientific data. It sits between your data and storage backends (primarily HDF5), letting you define schemas, validate data against those schemas, and read/write structured data without hand-coding serialization logic. The package is built on numpy, pandas, h5py, jsonschema, and ruamel-yaml, so it integrates naturally into scientific Python workflows. It's designed for teams building modern science data standards\u2014you define your data model once, and HDMF handles the mechanics of storing and retrieving it consistently.\n\nThe package targets researchers and developers working with complex, nested scientific datasets. Rather than flattening data into tables or writing custom I/O code, you describe your hierarchy declaratively and let HDMF enforce structure. It's particularly useful when you need versioned, validated, reproducible data formats across multiple tools or institutions.","worth_installing":"Yes. HDMF is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. Install it if you need to define, validate, and serialize hierarchical scientific data with schema enforcement. Skip it if your data is simple tabular or you prefer hand-coded I/O."},"id":"hdmf","links":{"html":"https://skillfed.io/packages/hdmf","md":"https://skillfed.io/packages/hdmf.md","pypi":"https://pypi.org/project/hdmf/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-25","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"hdmf","python_support":"supports_current","summary":"A hierarchical data modeling framework for modern science data standards"},"popularity":{"monthly_downloads":390197,"position":7024,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"6.1.0"}