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Authors
Huerta, E. A. http://orcid.org/0000-0002-9682-3604
Blaiszik, Ben
Brinson, L. Catherine http://orcid.org/0000-0003-2551-1563
Bouchard, Kristofer E.
Diaz, Daniel http://orcid.org/0000-0001-6834-1176
Doglioni, Caterina
Duarte, Javier M.
Emani, Murali
Foster, Ian http://orcid.org/0000-0003-2129-5269
Fox, Geoffrey http://orcid.org/0000-0003-1017-1391
Harris, Philip
Heinrich, Lukas
Jha, Shantenu
Katz, Daniel S. http://orcid.org/0000-0001-5934-7525
Kindratenko, Volodymyr http://orcid.org/0000-0002-9336-4756
Kirkpatrick, Christine R.
Lassila-Perini, Kati
Madduri, Ravi K. http://orcid.org/0000-0003-2130-2887
Neubauer, Mark S. http://orcid.org/0000-0001-8434-9274
Psomopoulos, Fotis E. http://orcid.org/0000-0002-0222-4273
Roy, Avik http://orcid.org/0000-0002-0116-1012
Rübel, Oliver
Zhao, Zhizhen
Zhu, Ruike
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Article History
Received: 17 October 2022
Accepted: 9 June 2023
First Online: 26 July 2023
Competing interests
: The authors declare the following competing interests: They are funded by the U.S. Department of Energy and/or the National Science Foundation (as described in detail in the Acknowledgements section) to lead the definition and application of FAIR principles for scientific data, AI models, research software, and workflows. They are the lead developers of scientific data infrastructure used to enable these advances, including Globus, funcX (now Globus Compute), the Data and Learning Hub for Science (DLHub), CookieCutter4FAIR, APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning, the Garden Project, the RDA FAIR for Machine Learning Interest Group, FARR: FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network, and the ELIXIR Machine Learning focus group.
