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Last month, our portal.hdfgroup.org site, which housed some documentation and downloads, went down. Fortunately, we were able to get it back up and running with some needed changes and improvements, which include: Documentation: Most of the HDF5 documentation now lives in doxygen in the HDF5 Github repo and can be viewed online at https://docs.hdfgroup.org. Please be patient with us as we complete this transition and feel free to reach out to us at https://help.hdfgroup.org if you need help locating anything. You can find links to documentation for our other products on the portal at https://portal.hdfgroup.org/documentation. Downloads: We are in the process of adding previous software releases to the new portal site at https://portal.hdfgroup.org/downloads. If you need a release that's not on the...

Hermes 0.9.8 has been released. This release features tagging. Tags enable users to semantically define associations between blobs and provide an intuitive way of locating blobs which are related....

The HDF5 Library and Tools 1.12.3 release is now available from the Download page. This is a maintenance release with a few updates: Brings the H5Dchunk_iter() API call from HDF5 1.14 Many CVE fixes as part of our continued effort to keep HDF5 CVE-free Please be aware that this will be the last release for HDF5 1.12. Users should move to HDF5 1.14. Please refer to the release schedule for future releases of these versions. Please see the full release notes for detailed information regarding this release, including a detailed list of changes. We also maintain a release schedule for all HDF5 versions in the README on Github. As always, feel free to post on the forum with any questions....

This poster from Aleksandar Jelenak and Dana Robinson of The HDF Group runs through several strategies to optimize HDF5 and netCDF-4 files for the cloud, including consolidating internal metadata, setting a large chunk size, and avoiding or minimizing the use of variable length datatypes. Several code examples for each situation are included. You're going to want to view the full size PDF....

The HDF Group has been selected to receive a Department of Energy grant to develop a platform where data from different fusion devices is managed according to Findable, Interoperable, Accessible, and Reusable (FAIR) standards and UNESCO’s Open Science recommendations. The data will also be adapted for use with machine learning (ML) tools. Led by researchers at MIT, this collaborative project also includes Auburn University, William & Mary, and the University of Wisconsin-Madison....