HDF5 Research Data Management Toolbox

HDF5 Research Data Management Toolbox#

The “HDF5 Research Data Management Toolbox” (h5rdmtoolbox) is a Python package designed to assist those engaged in the management of HDF5 data, enabling the implementation of a sustainable data lifecycle, that adheres to the FAIR principles (Findable, Accessible, Interoperable, Reusable).

Note

This project is under current development and is happy to receive ideas, code contributions as well as bug and issue reports. Thank you!

Highlights#

Effortless, FAIR Metadata#

HDF5 already supports rich metadata—but using it consistently can be tedious. h5rdmtoolbox removes that barrier. Add structured metadata with minimal effort, validate physical units automatically, and integrate metadata seamlessly into your analysis workflow with xarray. Metadata can be enriched using RDF (subject–predicate–object) statements and URI-based identifiers, enabling machine-readable, FAIR descriptions when needed.

Result: your data becomes more findable and interoperable—without extra complexity. (Findable, Interoperable)

Turn Your HDF5 Files into FAIR Knowledge Graphs#

Every HDF5 file encodes structure—groups, datasets, attributes. h5rdmtoolbox unlocks that structure as a knowledge graph. Automatically extract RDF, query your data with SPARQL, export to JSON-LD or Turtle, and validate metadata using SHACL constraints. What was once static metadata becomes connected, searchable, and reusable across systems.

Result: your data gains strong interoperability and reusability. (Interoperable, Reusable)

Persistent Identifiers for Unambiguous Reuse#

Ambiguity limits reuse—but it doesn’t have to. h5rdmtoolbox enables precise, persistent identification. Assign identifiers (DOI, ORCID, or custom URIs) to datasets, people, and metadata elements. You can link units to ontologies like QUDT or describe authors using FOAF—demonstrating how scientific data can be made unambiguous and machine-interpretable.

Result: your data is more findable and easier to reuse correctly. (Findable, Reusable)

From Experiment to FAIR Publication#

When your work is ready to share, h5rdmtoolbox bridges the gap to publication. Publish directly to repositories like Zenodo, including rich, standardized metadata extracted from your files. The same metadata that supports your workflow is preserved and shared, ensuring others can discover, understand, and reuse your data.

Result: your data remains accessible and reusable over time. (Accessible, Reusable)

Built on FAIR Semantic Web Principles#

Interoperability starts with shared meaning. h5rdmtoolbox is built on semantic web technologies. Using RDF, SPARQL, and URI-based identifiers, it turns HDF5 metadata into machine-readable, linkable information, with validation through SHACL to ensure quality and consistency. It provides a flexible framework to incorporate scientific conventions such as NeXus, metadata4ing, and standard-name-based approaches—without enforcing a fixed schema.

Result: metadata that is interoperable, reusable, and ready to connect across domains. (Interoperable, Reusable)

Why h5rdmtoolbox?#

For scientists working with HDF5 data, h5rdmtoolbox addresses critical challenges:

Time Savings - No more hunting for metadata scattered across spreadsheets and README files - Automatic validation catches errors before data is published - Conventions enforce consistency without manual checking

Reproducibility - Every dataset carries its context: units, coordinates, processing history - Standardized attributes make data understandable years later - Full provenance tracking from raw data to publication-ready results

FAIR Compliance - Funding agencies increasingly require FAIR data management plans - h5rdmtoolbox makes FAIR principles practical, not just aspirational - Community standard support (NeXus, domain-specific conventions)

Collaboration - Shared conventions ensure everyone on a team uses the same metadata standards - Layout validation catches structure issues before sharing files - Rich metadata exports enable integration with data catalogs and repositories

Scientific Domains - Native HDF5 support ideal for: imaging, spectroscopy, simulations, experimental data - Dimension scales and coordinates preserved throughout analysis - Physical units validated using the pint library