hdmf
A hierarchical data modeling framework for modern science data standards
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; h5py depends on HDF5 system libraries.
- Low friction: pure Python wheel, active maintenance with a release 50 days ago, and five well-established runtime dependencies (h5py, jsonschema, numpy, pandas, ruamel-yaml).
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; redistribution requires retaining copyright notice and disclaimer.
last release 2026-06-25 (50 days) · last repo commit 2026-08-11 · 57 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 390,197 downloads/mo, #7,024 on PyPI
Alternatives
Verify before relying
pip install hdmf
import hdmf
from hdmf.backends.hdf5 import HDF5IO
# Define and serialize hierarchical data structures- Whether the package is actively used in production scientific workflows beyond its citation count.
- Performance characteristics when handling large hierarchical datasets.
- Compatibility with newer HDF5 versions and potential breaking changes.
What it is and 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—you define your data model once, and HDMF handles the mechanics of storing and retrieving it consistently.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
hdmf on PyPI
Before you install
Low friction: pure Python wheel, active maintenance with a release 50 days ago, and five well-established runtime dependencies (h5py, jsonschema, numpy, pandas, ruamel-yaml).
Requires Python 3.10 or later; h5py depends on HDF5 system libraries.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; redistribution requires retaining copyright notice and disclaimer.
Quickstart
pip install hdmf
import hdmf
from hdmf.backends.hdf5 import HDF5IO
# Define and serialize hierarchical data structuresVerify before relying
- Whether the package is actively used in production scientific workflows beyond its citation count.
- Performance characteristics when handling large hierarchical datasets.
- Compatibility with newer HDF5 versions and potential breaking changes.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesh5pyjsonschemanumpypandasruamel-yaml |
| Maintenance | Actively maintained 50 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 390,197 / month, #7,024 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Medical Science Apps. |
Evidence: hdmf-6.1.0-py3-none-any.whl
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