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#
The combination of HDF5 and xarray facilitates convenient access to metadata and data during analysis and processing (find out more here).
Metadata can be assigned with “globally unique and persistent identifiers” as required by F1 of the FAIR principles. This is achieved by introducing RDF syntax to HDF5 and thus avoids “ambiguity in the meaning of your published data…”.
The definition of standard attributes through so-called conventions enforces users to use specific attributes, which get validated and are essential for the interpretation of the data.
HDF5 files can be uploaded directly to repositories like Zenodo.
A database interface allows querying for information in the file or moving metadata to noSQL databases like mongoDB for dedicated and more complex search queries.
