h5rdmtoolbox – A Python Research Data Management Toolbox for HDF5
Presented by Matthias Probst from the Karlsruhe Institute of Technology, Institute of Thermal Turbomachinery
NEW DATE! Friday May 3, 2024
April 26, 2024 11:00 a.m. Central time (US/Canada)
Registration Required
The "HDF5 Research Data Management Toolbox“ (h5RDMtoolbox) is a Python package, that supports the FAIRification of HDF5 data by wrapping additional functionality around the core HDF5 package. The FAIR Principles have been proven to be effective guidelines during the research data lifecycle, with the specific aim of improving data exploration and reuse.
The presented toolbox has been designed to enhance the quality of the data by adding layers to the HDF5 interface h5py. These include the ability to perform simple queries, interact with data via xarray objects, and describe the file contents using semantic RDF triples. The latter significantly improves the readability and reusability for both humans and machines and allows an HDF5 file and its data to be structurally and semantically described and extracted.
The presentation will provide an overview of the background and motivation behind the development, as well as an insight into the features of the toolbox through the use of practical examples.
May 3, 2024 11:00 a.m. central time (US/Canada)
Register to join us
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Reminder! The h5rdmtoolbox – A Python Research Data Management Toolbox for HDF5 webinar will be this Friday (May 3) at 11:00 a.m. Central time (US/Canada)
Registration is required.
More info:
The "HDF5 Research Data Management Toolbox“ (h5RDMtoolbox) is a Python package, that supports the FAIRification of HDF5 data by wrapping additional functionality around the core HDF5 package. The FAIR Principles have been proven to be effective guidelines during the research data lifecycle, with the specific aim of improving data exploration and reuse.
The presented toolbox has been designed to enhance the quality of the data by adding layers to the HDF5 interface h5py. These include the ability to perform simple queries, interact with data via xarray objects, and describe the file contents using semantic RDF triples. The latter significantly improves the readability and reusability for both humans and machines and allows an HDF5 file and its data to be structurally and semantically described and extracted.
The presentation will provide an overview of the background and motivation behind the development, as well as an insight into the features of the toolbox through the use of practical examples.
We hope to see you on Friday!
Starting in about 90 minutes–
h5rdmtoolbox – A Python Research Data Management Toolbox for HDF5
Presented by Matthias Probst from the Karlsruhe Institute of Technology, Institute of Thermal Turbomachinery
TODAY, Friday, May 3, 2024 11:00 a.m. Central time (US/Canada)
Registration to Attend!
