Red Hat Research Quarterly provides insights into the range of activities that bring universities, government organizations, industry partners together to share outcomes with open source communities. Here you’ll find articles on the latest research topics we are exploring together, views into the approaches and people behind that research, and examples of the impact that combining education with an open source perspective can have.
August 2021Latest Issue
The right idea at the right time: networking researchers use open source for real-world results
An interview with Anna Brunström.
More from this issue
BigDataStack delivers with contributions from industry and university partners
Faster hardware through software
Building better through research
August 2021Latest Issue
The right idea at the right time: networking researchers use open source for real-world results
An interview with Anna Brunström.
More from this issue
BigDataStack delivers with contributions from industry and university partners
Faster hardware through software
Building better through research
All issues
May 2021
Anat Bremler-Barr: when one plus one makes more than two
August 2021
Anna Brunström: the right idea at the right time: networking researchers use open source for real-world results
November 2021
Barbora Buhnová: On founding Czechitas and opening the doors of tech
May 2020
Kit Murdock—an open source swashbuckler.
August 2020
Voyage into the open Dataverse:
James Honaker and Mercè Crosas on the privacy balancing act
October 2020
Václav Matyáš: open source cybersecurity and the next generation
February 2021
Kate Saenko: minimizing dataset bias in AI
May 2019
Daniel Gruss—What can we do to improve security and resistance to the Spectres and Meltdowns of the future?
August 2019
How To Train Your Model: E. Ugur Kaynar’s Research Adds Object Store Caching to Ceph, Speeds Machine Learning.
November 2019
Rolling your own processor: Ahmed Sanaullah builds an open source toolchain for an FPGA.
February 2020
Finding Flipper: Newcastle PhDs Georgia Atkinson and Cameron Trotter use deep learning to identify and count marine mammals.
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