IDEAS home Printed from https://ideas.repec.org/a/gam/jpubli/v8y2020i2p21-d344422.html
   My bibliography  Save this article

FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Author

Listed:
  • Koenraad De Smedt

    (Department of Linguistic, Literary and Aesthetic Studies, University of Bergen, P.O Box 7800, 5020 Bergen, Norway)

  • Dimitris Koureas

    (International Biodiversity Infrastructures, Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA Leiden, The Netherlands)

  • Peter Wittenburg

    (Max Planck Computing and Data Facility, Max-Planck-Gesellschaft, Gießenbachstraße 2, 85748 Garching, Germany)

Abstract

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

Suggested Citation

  • Koenraad De Smedt & Dimitris Koureas & Peter Wittenburg, 2020. "FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units," Publications, MDPI, vol. 8(2), pages 1-17, April.
    • Handle: RePEc:gam:jpubli:v:8:y:2020:i:2:p:21-:d:344422
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2304-6775/8/2/21/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2304-6775/8/2/21/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christine L. Borgman, 2012. "The conundrum of sharing research data," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1059-1078, June.
    2. Christine L. Borgman, 2012. "The conundrum of sharing research data," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(6), pages 1059-1078, June.
    3. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    4. Lindell Bromham & Russell Dinnage & Xia Hua, 2016. "Interdisciplinary research has consistently lower funding success," Nature, Nature, vol. 534(7609), pages 684-687, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Runsheng Miao & Yuchen Huang & Zhenyu Zhang, 2023. "A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies," Mathematics, MDPI, vol. 11(14), pages 1-27, July.
    2. Jan Schweikert & Karl-Uwe Stucky & Wolfgang Süß & Veit Hagenmeyer, 2023. "A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies," Energies, MDPI, vol. 16(3), pages 1-21, February.
    3. Henrik tom Wörden & Florian Spreckelsen & Stefan Luther & Ulrich Parlitz & Alexander Schlemmer, 2024. "Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems," Data, MDPI, vol. 9(2), pages 1-15, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vikas Jaiman & Leonard Pernice & Visara Urovi, 2022. "User incentives for blockchain-based data sharing platforms," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-22, April.
    2. Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2014. "What Drives Academic Data Sharing?," SOEPpapers on Multidisciplinary Panel Data Research 655, DIW Berlin, The German Socio-Economic Panel (SOEP).
    3. Carol Tenopir & Elizabeth D Dalton & Suzie Allard & Mike Frame & Ivanka Pjesivac & Ben Birch & Danielle Pollock & Kristina Dorsett, 2015. "Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-24, August.
    4. Andrea K. Thomer, 2022. "Integrative data reuse at scientifically significant sites: Case studies at Yellowstone National Park and the La Brea Tar Pits," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1155-1170, August.
    5. Jenny Bossaller & Anthony J. Million, 2023. "The research data life cycle, legacy data, and dilemmas in research data management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 701-706, June.
    6. Guillaume Cabanac & Thomas Preuss, 2013. "Capitalizing on order effects in the bids of peer-reviewed conferences to secure reviews by expert referees," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 405-415, February.
    7. Shibayama, Sotaro & Lawson, Cornelia, 2021. "The use of rewards in the sharing of research resources," Research Policy, Elsevier, vol. 50(7).
    8. Koutroumpis, Pantelis & Leiponen, Aija & Thomas, Llewellyn D W, 2017. "The (Unfulfilled) Potential of Data Marketplaces," ETLA Working Papers 53, The Research Institute of the Finnish Economy.
    9. Isabella Peters & Peter Kraker & Elisabeth Lex & Christian Gumpenberger & Juan Gorraiz, 2016. "Research data explored: an extended analysis of citations and altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 723-744, May.
    10. Jingfeng Xia, 2013. "Mandates and the Contributions of Open Genomic Data," Publications, MDPI, vol. 1(3), pages 1-14, October.
    11. Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2014. "What Drives Academic Data Sharing?," RatSWD Working Papers 236, German Data Forum (RatSWD).
    12. Youngseek Kim & Ayoung Yoon, 2017. "Scientists' data reuse behaviors: A multilevel analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2709-2719, December.
    13. Christian Hopp & Francis J. Greene & Benson Honig & Tomas Karlsson & Mikael Samuelsson, 2018. "Revisiting the influence of institutional forces on the written business plan: a replication study," Management Review Quarterly, Springer, vol. 68(4), pages 361-398, November.
    14. G. N. Kenyon & M. Goldsmith & B. D. Neureuther & D. Zhou, 2018. "Improving the return on investment in ports: opportunities in data management," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 20(4), pages 514-530, December.
    15. Nushrat Khan & Mike Thelwall & Kayvan Kousha, 2021. "Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3621-3639, April.
    16. Michael Weinhardt, 2021. "Big Data: Some Ethical Concerns for the Social Sciences," Social Sciences, MDPI, vol. 10(2), pages 1-14, January.
    17. Ana Trisovic & Katherine Mika & Ceilyn Boyd & Sebastian Feger & Mercè Crosas, 2021. "Repository Approaches to Improving the Quality of Shared Data and Code," Data, MDPI, vol. 6(2), pages 1-12, February.
    18. Mike Thelwall, 2020. "Data in Brief: Can a mega-journal for data be useful?," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 697-709, July.
    19. Federica Cugnata & Chiara Brombin & Chiara Maria Poli & Roberto Buccione & Clelia Serio, 2024. "Modelling perception and resilience factors to data sharing in clinical and basic research: an observational study," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3169-3192, June.
    20. Plantin, Jean-Christophe, 2021. "The data archive as factory: alienation and resistance of data processors," LSE Research Online Documents on Economics 109692, London School of Economics and Political Science, LSE Library.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jpubli:v:8:y:2020:i:2:p:21-:d:344422. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.