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      FAIR for AI: An interdisciplinary and international community building perspective

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          Abstract

          A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

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          ImageNet classification with deep convolutional neural networks

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            The FAIR Guiding Principles for scientific data management and stewardship

            There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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              The rise of artificial intelligence in healthcare applications

              Big data and machine learning are having an impact on most aspects of modern life, from entertainment, commerce, and healthcare. Netflix knows which films and series people prefer to watch, Amazon knows which items people like to buy when and where, and Google knows which symptoms and conditions people are searching for. All this data can be used for very detailed personal profiling, which may be of great value for behavioral understanding and targeting but also has potential for predicting healthcare trends. There is great optimism that the application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. It is generally believed that AI tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff as such. AI is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring. In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.
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                Author and article information

                Contributors
                elihu@anl.gov
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                26 July 2023
                26 July 2023
                2023
                : 10
                : 487
                Affiliations
                [1 ]GRID grid.187073.a, ISNI 0000 0001 1939 4845, Data Science and Learning Division, , Argonne National Laboratory, ; Lemont, Illinois 60439 USA
                [2 ]GRID grid.170205.1, ISNI 0000 0004 1936 7822, Department of Computer Science, , University of Chicago, ; Chicago, Illinois 60637 USA
                [3 ]GRID grid.170205.1, ISNI 0000 0004 1936 7822, Globus, , University of Chicago, ; Chicago, Illinois 60637 USA
                [4 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Mechanical Engineering and Materials Science, , Duke University, ; Durham, North Carolina 27708 USA
                [5 ]GRID grid.184769.5, ISNI 0000 0001 2231 4551, Scientific Data Division, , Lawrence Berkeley National Laboratory, ; Berkeley, CA 94720 USA
                [6 ]GRID grid.184769.5, ISNI 0000 0001 2231 4551, Biological Systems & Engineering, , Lawrence Berkeley National Laboratory, ; Berkeley, California 94720 USA
                [7 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Helen Wills Neuroscience Institute, , University of California Berkeley, ; Berkeley, California 94720 USA
                [8 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Physics, , University of California San Diego, ; La Jolla, California 92093 USA
                [9 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Lund University, Department of Physics, ; Box 118, 221 00 Lund, Sweden
                [10 ]GRID grid.5379.8, ISNI 0000000121662407, School of Physics & Astronomy, , The University of Manchester, ; Manchester, M13 9PL UK
                [11 ]GRID grid.187073.a, ISNI 0000 0001 1939 4845, Leadership Computing Facility, , Argonne National Laboratory, ; Lemont, Illinois 60439 USA
                [12 ]GRID grid.27755.32, ISNI 0000 0000 9136 933X, Biocomplexity Institute and Department of Computer Science, , University of Virginia, ; Charlottesville, Virginia 22904 USA
                [13 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Physics, , Massachusetts Institute of Technology, ; Cambridge, Massachusetts 02139 USA
                [14 ]GRID grid.6936.a, ISNI 0000000123222966, Technical University Munich, ; Arcisstraβe 21, 80333 München, Germany
                [15 ]GRID grid.202665.5, ISNI 0000 0001 2188 4229, Computational Science Initiative Brookhaven National Laboratory Upton, ; New York, 11973 USA
                [16 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Electrical and Computer Engineering, Rutgers, , The State University of New Jersey, ; Piscataway, New Jersey 08854 USA
                [17 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, National Center for Supercomputing Applications, , University of Illinois, Urbana-Champaign, ; Urbana, Illinois 61801 USA
                [18 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Computer Science, , University of Illinois at Urbana-Champaign, ; Urbana, Illinois 61801 USA
                [19 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Electrical & Computer Engineering, , University of Illinois at Urbana-Champaign, ; Urbana, Illinois 61801 USA
                [20 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, School of Information Sciences, , University of Illinois at Urbana-Champaign, ; Urbana, Illinois 61801 USA
                [21 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, San Diego Supercomputer Center, , University of California San Diego, ; La Jolla, California 92093 USA
                [22 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Helsinki Institute of Physics, , University of Helsinki, ; P.O. Box 64, Helsinki, 00014 Finland
                [23 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Physics, , University of Illinois at Urbana-Champaign, ; Urbana, Illinois 61801 USA
                [24 ]GRID grid.423747.1, ISNI 0000 0001 2216 5285, Institute of Applied Biosciences, , Centre for Research and Technology Hellas, ; Thessaloniki, 57001 Greece
                Author information
                http://orcid.org/0000-0002-9682-3604
                http://orcid.org/0000-0003-2551-1563
                http://orcid.org/0000-0001-6834-1176
                http://orcid.org/0000-0003-2129-5269
                http://orcid.org/0000-0003-1017-1391
                http://orcid.org/0000-0001-5934-7525
                http://orcid.org/0000-0002-9336-4756
                http://orcid.org/0000-0003-2130-2887
                http://orcid.org/0000-0001-8434-9274
                http://orcid.org/0000-0002-0222-4273
                http://orcid.org/0000-0002-0116-1012
                Article
                2298
                10.1038/s41597-023-02298-6
                10372139
                37495591
                1043d026-8581-40b0-b526-f1ba4cecfbb2
                © UChicago Argonne, LLC, Operator of Argonne National Laboratory 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 October 2022
                : 9 June 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000015, U.S. Department of Energy (DOE);
                Award ID: DE-AC02-06CH11357
                Award ID: DE-SC0021396
                Award ID: DE-SC0021293
                Award ID: DE-AC02-06CH11357
                Award ID: DE-SC0021352
                Award ID: DE-SC0021225
                Award ID: DE-SC0021352
                Award ID: DE-SC0021258
                Award ID: DE-SC0021258
                Award ID: DE-SC0021258
                Award ID: DE-SC0021258
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: 1931306
                Award ID: 2209892
                Award ID: 1916481
                Award ID: 2226453
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 101002463
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Award ID: 101017536
                Award Recipient :
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