No country for old data: Increasing FAIR-ness of research outcomes through standardization and community-driven development
Author(s) / Creator(s)
Herholz, Peer
Abstract / Description
The current status of scientific research and academia is defined by a multitude of high-dimensional, interacting factors. At the core, especially aspects like decentralization, digitalization, “big data” and “the reproducibility crisis” shaped recent discussions and continue to do so. Common to all of these aspects and one of the driving forces of cumbersome scientific progress is that the majority of research outcomes is not FAIR (Findable, Accessible, Interoperable, Reusable). While a myriad of potential solutions and approaches to tackle these problems were proposed, the combination of comprehensive standardization (e.g., The Brain Imaging Data Structure) and community-driven development of resources (e.g., the open science/brainhack community, The Turing Way) manifested itself as a well-functioningand sustainable way forward. Throughout the life cycle of a scientific project, the respective implementations, chances and limitations of this approach will be discussed and its capacities to increase FAIR-ness outlined based on examples. Furthermore, it will be addressed how this approach can drastically enhance scientific progress through creating a more open and inclusive form of academia.
Persistent Identifier
Date of first publication
2020-12-07
Is part of
CSPD 2020, online
Publisher
ZPID (Leibniz Institute for Psychology)
Citation
Herholz, P. (2020). No country for old data: Increasing FAIR-ness of research outcomes through standardization and community-driven development. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.4472
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CSPD2020_NoCountryForOldData_Keynote_PeerHerholz.pdfAdobe PDF - 18.86MBMD5: 82e8c2fb4d6c13117a6757699ddee6e0
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Author(s) / Creator(s)Herholz, Peer
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PsychArchives acquisition timestamp2021-01-18T09:33:00Z
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Made available on2021-01-18T09:33:00Z
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Date of first publication2020-12-07
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Abstract / DescriptionThe current status of scientific research and academia is defined by a multitude of high-dimensional, interacting factors. At the core, especially aspects like decentralization, digitalization, “big data” and “the reproducibility crisis” shaped recent discussions and continue to do so. Common to all of these aspects and one of the driving forces of cumbersome scientific progress is that the majority of research outcomes is not FAIR (Findable, Accessible, Interoperable, Reusable). While a myriad of potential solutions and approaches to tackle these problems were proposed, the combination of comprehensive standardization (e.g., The Brain Imaging Data Structure) and community-driven development of resources (e.g., the open science/brainhack community, The Turing Way) manifested itself as a well-functioningand sustainable way forward. Throughout the life cycle of a scientific project, the respective implementations, chances and limitations of this approach will be discussed and its capacities to increase FAIR-ness outlined based on examples. Furthermore, it will be addressed how this approach can drastically enhance scientific progress through creating a more open and inclusive form of academia.
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Review statusunknown
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CitationHerholz, P. (2020). No country for old data: Increasing FAIR-ness of research outcomes through standardization and community-driven development. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.4472en
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/4051
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.4472
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Language of contenteng
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PublisherZPID (Leibniz Institute for Psychology)
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Is part ofCSPD 2020, online
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Is related tohttps://www.conference-service.com/CSPD2020/xpage.html?xpage=244&lang=en
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Dewey Decimal Classification number(s)150
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TitleNo country for old data: Increasing FAIR-ness of research outcomes through standardization and community-driven developmenten_US
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DRO typeconferenceObject
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Visible tag(s)ZPID Conferences and Workshops