FAIR for AI: An interdisciplinary and international community building perspective

E. A. Huerta; Ben Blaiszik; L. Catherine Brinson; Kristofer E. Bouchard; Daniel Diaz; Caterina Doglioni; Javier M. Duarte; Murali Emani; Ian Foster; Geoffrey Fox; Philip Harris; Lukas Heinrich; Shantenu Jha; Daniel S. Katz; Volodymyr Kindratenko; Christine R. Kirkpatrick; Kati Lassila-Perini; Ravi K. Madduri; Mark S. Neubauer; Fotis E. Psomopoulos; Avik Roy; Oliver Rübel; Zhizhen Zhao; Ruike Zhu

doi:10.1038/s41597-023-02298-6

FAIR for AI: An interdisciplinary and international community building perspective

E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. PsomopoulosAvik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu

Research output: Contribution to journal › Comment/debate › peer-review

Original language	English (US)
Article number	487
Journal	Scientific Data
Volume	10
Issue number	1
Early online date	Jul 26 2023
DOIs	https://doi.org/10.1038/s41597-023-02298-6
State	Published - Dec 2023

ASJC Scopus subject areas

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

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Huerta, E. A., Blaiszik, B., Brinson, L. C., Bouchard, K. E., Diaz, D., Doglioni, C., Duarte, J. M., Emani, M., Foster, I., Fox, G., Harris, P., Heinrich, L., Jha, S., Katz, D. S., Kindratenko, V., Kirkpatrick, C. R., Lassila-Perini, K., Madduri, R. K., Neubauer, M. S., ... Zhu, R. (2023). FAIR for AI: An interdisciplinary and international community building perspective. Scientific Data, 10(1), Article 487. https://doi.org/10.1038/s41597-023-02298-6

Huerta, EA, Blaiszik, B, Brinson, LC, Bouchard, KE, Diaz, D, Doglioni, C, Duarte, JM, Emani, M, Foster, I, Fox, G, Harris, P, Heinrich, L, Jha, S, Katz, DS , Kindratenko, V , Kirkpatrick, CR, Lassila-Perini, K, Madduri, RK, Neubauer, MS, Psomopoulos, FE, Roy, A, Rübel, O, Zhao, Z & Zhu, R 2023, 'FAIR for AI: An interdisciplinary and international community building perspective', Scientific Data, vol. 10, no. 1, 487. https://doi.org/10.1038/s41597-023-02298-6

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title = "FAIR for AI: An interdisciplinary and international community building perspective",

author = "Huerta, {E. A.} and Ben Blaiszik and Brinson, {L. Catherine} and Bouchard, {Kristofer E.} and Daniel Diaz and Caterina Doglioni and Duarte, {Javier M.} and Murali Emani and Ian Foster and Geoffrey Fox and Philip Harris and Lukas Heinrich and Shantenu Jha and Katz, {Daniel S.} and Volodymyr Kindratenko and Kirkpatrick, {Christine R.} and Kati Lassila-Perini and Madduri, {Ravi K.} and Neubauer, {Mark S.} and Psomopoulos, {Fotis E.} and Avik Roy and Oliver R{\"u}bel and Zhizhen Zhao and Ruike Zhu",

note = "E.A.H.: This work was supported by the FAIR Data program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-AC02-06CH11357. B.B.: This work was supported by the National Science Foundation under NSF Award Numbers: 1931306 and 2209892. C.K.: This work was supported by the National Science Foundation under NSF Award Number: 1916481 \u201CBD Hubs: Collaborative Proposal: West: Accelerating the Big Data Innovation Ecosystem\u201D and NSF Award Number: 2226453 \u201CDisciplinary Improvements: AI Readiness, Reproducibility, and FAIR: Connecting Computing and Domain Communities Across the ML Lifecycle\u201D. D.S.K., V.K., M.S.N, A.R.: This work was supported by the FAIR Data program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-SC0021258. G.F., S.J. This work was supported by the FAIR Data program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-SC0021352. C.D., L.H.: The ESCAPE project has received funding from the Horizon 2020 research and innovation programme, Grant Agreement no. 824064. The EOSC-Future project has received funding from the Horizon 2020 research and innovation programme, Grant Agreement no. 101017536. C.D. has received funding from the European Research Council under the European Union\u2019s Horizon 2020 research and innovation program (grant agreement no. 101002463) and from the Swedish Research Council. M.E. The HPCFAIR project is supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Program under Award Number DE-SC0021293. 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.",

year = "2023",

month = dec,

doi = "10.1038/s41597-023-02298-6",

language = "English (US)",

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TY - JOUR

T1 - FAIR for AI

T2 - An interdisciplinary and international community building perspective

AU - Huerta, E. A.

AU - Blaiszik, Ben

AU - Brinson, L. Catherine

AU - Bouchard, Kristofer E.

AU - Diaz, Daniel

AU - Doglioni, Caterina

AU - Duarte, Javier M.

AU - Emani, Murali

AU - Foster, Ian

AU - Fox, Geoffrey

AU - Harris, Philip

AU - Heinrich, Lukas

AU - Jha, Shantenu

AU - Katz, Daniel S.

AU - Kindratenko, Volodymyr

AU - Kirkpatrick, Christine R.

AU - Lassila-Perini, Kati

AU - Madduri, Ravi K.

AU - Neubauer, Mark S.

AU - Psomopoulos, Fotis E.

AU - Roy, Avik

AU - Rübel, Oliver

AU - Zhao, Zhizhen

AU - Zhu, Ruike

N1 - E.A.H.: This work was supported by the FAIR Data program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-AC02-06CH11357. B.B.: This work was supported by the National Science Foundation under NSF Award Numbers: 1931306 and 2209892. C.K.: This work was supported by the National Science Foundation under NSF Award Number: 1916481 \u201CBD Hubs: Collaborative Proposal: West: Accelerating the Big Data Innovation Ecosystem\u201D and NSF Award Number: 2226453 \u201CDisciplinary Improvements: AI Readiness, Reproducibility, and FAIR: Connecting Computing and Domain Communities Across the ML Lifecycle\u201D. D.S.K., V.K., M.S.N, A.R.: This work was supported by the FAIR Data program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-SC0021258. G.F., S.J. This work was supported by the FAIR Data program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-SC0021352. C.D., L.H.: The ESCAPE project has received funding from the Horizon 2020 research and innovation programme, Grant Agreement no. 824064. The EOSC-Future project has received funding from the Horizon 2020 research and innovation programme, Grant Agreement no. 101017536. C.D. has received funding from the European Research Council under the European Union\u2019s Horizon 2020 research and innovation program (grant agreement no. 101002463) and from the Swedish Research Council. M.E. The HPCFAIR project is supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Program under Award Number DE-SC0021293. 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.

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ER -

FAIR for AI: An interdisciplinary and international community building perspective

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