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http://dx.doi.org/10.14404/JKSARM.2021.21.2.155

FAIR Principles: Considerations for Implementing Digital Archives from a Data Perspective  

Kim, Haklae (중앙대학교 사회과학대학 문헌정보학과)
Publication Information
Journal of Korean Society of Archives and Records Management / v.21, no.2, 2021 , pp. 155-172 More about this Journal
Abstract
Digital archives are electronic storages used to preserve and utilize digital resources sustainably. Theoretical research on digital archives is being conducted actively, and digital archives for recording various resources in heterogeneous domains are being built and serviced. However, although the original purpose of digitizing the resources of digital archives is achievable, the discovery and reuse are still limited. This study examines the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles in detail and proposes a maturity assessment framework for digital archives. The FAIR Data Principles is a set of guidelines that enable machines to read and understand digital resources that are applied to any online resource. The evaluation model of the FAIR data principle defines the planning and application stages separately. However, criteria for evaluating the application of individual principles are still ambiguous, and discussions on evaluation criteria for the field of digital archives are insufficient. This study proposes a framework for applying the FAIR data principle to digital archives and discusses issues for future application.
Keywords
FAIR; findability; accessibility; interoperability; reusability; machine readability;
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1 David, R., Mabile, L., Specht, A., Stryeck, S., Thomsen, M., Yahia, M., Jonquet, C., Dolle, L., Jacob, D., Bailo, D., Bravo, E., Gachet, S., Gunderman, H., Hollebecq, J. E., Ioannidis, V., Bras, Y. L, Lerigoleur, E., & Cambon-Thomsen, A. (2020). FAIRness Literacy: The Achilles' Heel of Applying FAIR Principles. Data Science Journal, 19(1), 32. https://doi.org/10.5334/dsj-2020-032   DOI
2 Korean Society of Archives and Records Management (2020). Records and Archives Management: Theory and Practice. Goodwriting Publishing.
3 Kim, S. & Oh, S. G. (2018). Key Factors in the Implementation of Research Data Management Services. Journal of the Korean society for information management, 35(2), 141-165. https://doi.org/10.3743/KOSIM.2018.35.2.141   DOI
4 Kim, You-Seung (2010). A Theoretical Study on Establishing Archive 2.0. Journal of Korean Society of Archives and Records Management, 10(2), 31-52. https://doi.org/10.14404/JKSARM.2010.10.2.031   DOI
5 Lee, Geauchul (2020). Development and Current Trend of Digital Archive. Review of Architecture and Building Science, 64(5), 35-38.
6 Lee, S. (2002). Standardization of Digital Archiving and OAIS Reference Model. Journal of Information Science Theory and Practice, 33(3), 45-68. http://dx.doi.org/10.1633/JIM.2002.33.3.045   DOI
7 Bahim, C., Casorran-Amilburu, C., Dekkers, M., Herczog, E., Loozen, N., Repanas, K., Russell, K., & Stall, S. (2020). The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments. Data Science Journal, 19(1), 41. http://doi.org/10.5334/dsj-2020-041   DOI
8 Berners-Lee, T. (2006). Design Issues: Linked Data. Retrieved April 19, 2021, Available: https://www.w3.org/DesignIssues/LinkedData.html
9 Boeckhout, M., Zielhuis, G. A., & Bredenoord, A. L. (2018). The FAIR guiding principles for data stewardship: fair enough?. European journal of human genetics: EJHG, 26(7), 931-936. https://doi.org/10.1038/s41431-018-0160-0   DOI
10 Calamai, S. & Frontini, F. (2018). FAIR data principles and their application to speech and oral archives. Journal of New Music Research, 47, 339-354.   DOI
11 Guizzardi, G. (2020). Ontology, Ontologies and the "I" of FAIR. Data Intelligence, 2, 181-191. https://doi.org/10.1162/dint_a_00040   DOI
12 Hall, W. & Tiropanis, T. (2012). Web evolution and Web Science. Computer Networks, 56, 3859-3865.   DOI
13 Kim, Haklae (2017). Knowledge Graph. Seoul: CommunicationBooks, Inc. https://doi.org/10.979.11288/05141
14 Devaraju, A., Mokrane, M., Cepinskas, L., Huber, R., Herterich, P., de Vries, J., Akerman, V., L'Hours, H., Davidson, J., & Diepenbroek, M. (2021). From Conceptualization to Implementation: FAIR Assessment of Research Data Objects. Data Science Journal, 20(1), 4. http://doi.org/10.5334/dsj-2021-004   DOI
15 Cousijn, H., Kenall, A., Ganley, E., Harrison, M., Kernohan, D., Lemberger, T., Murphy, F., Polischuk, P., Taylor, S., Martone, M., & Clark, T. (2018). A data citation roadmap for scientific publishers. Scientific Data, 5. https://doi.org/10.1038/sdata.2018.259   DOI
16 DANS (2021). FAIR self assessment tool. Retrieved May 12, 2021, Available: https://satifyd.dans.knaw.nl/
17 Devaraju, A., Huber, R., Mokrane, M., Herterich, P., Cepinskas, L., Vries, J., L'Hours, H., Davidson, J., & Whyte, A. (2020). FAIRsFAIR Data Object Assessment Metrics (Version 0.4). https://doi.org/10.5281/zenodo.4081213
18 DTL (2021). European Commission embraces the FAIR principles. Retrieved April 19, 2021, Available: https://www.dtls.nl/2016/04/20/european-commission-allocates-e2-billion-to-make-research-data-fair/
19 EU (2016). G20 Leaders' Communique Hangzhou Summit. Retrieved April 19, 2021, Available: https://ec.europa.eu/commission/presscorner/detail/en/STATEMENT_16_2967
20 Mons, B., Neylon, C., Velterop, J., Dumontier, M., Da Silva Santos, L. O. B., & Wilkinson, M. D. (2017). Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services and Use, 37(1), 49-56. https://doi.org/10.3233/ISU-170824   DOI
21 Niu, J. (2016). Linked Data for Archives. Archivaria, 82, 83-110. https://www.muse.jhu.edu/article/687083.
22 Sinaci, A. A., Nunez-Benjumea, F. J., & Gencturk, M., et al. (2020). From Raw Data to FAIR Data: The FAIRification Workflow for Health Research. Methods of information in medicine, 59(S 01), e21-e32. doi:10.1055/s-0040-1713684   DOI
23 Stall, S., Yarmey, L., Cutcher-Gershenfeld, J., Hanson, B., Lehnert, K., Nosek, B., Parsons, M., Robinson, E., & Wyborn, L. (2019). Make scientific data FAIR. Nature, 570(7759), 27-29. https://doi.org/10.1038/d41586-019-01720-7   DOI
24 Lee, S. (2013). Trends Analysis of Digital Preservation Research in Korea. Journal of Korean Society of Archives and Records Management, 13(2), 247-283. https://doi.org/10.14404/JKSARM.2013.13.2.247   DOI
25 National Research Council of Science & Technology (2019). Guidelines of Research Data Management. National Research Council of Science & Technology.
26 Park, H., Han, S., & Oh, S.-G. (2018). A Study of a Digital Archiving Model based on E-ARK. Journal of the Korean Society for Information Management, 35(1), 83-101. https://doi.org/10.3743/KOSIM.2018.35.1.083   DOI
27 Park, O. (2012). A Study on Developing Preservation Metadata Based on PREMIS Focusing on Digital Data in National Library of Korea, 46(2), 83-113. https://doi.org/10.4275/KSLIS.2012.46.2.083   DOI
28 Kim, S. & Kim, S. (2020). A Study on the Research Data Management Methods for the Condensed Matter Physics. Journal of the Korean society for information management, 37(3), 77-106. https://doi.org/10.3743/KOSIM.2020.37.3.077   DOI
29 GO FAIR (2021). FAIRification process. Retrieved April 19, 2021, Available: https://www.go-fair.org/fair-principles/fairification-process/
30 Barbuti, N. (2020). Thinking digital libraries for preservation as digital cultural heritage: by R to R4 facet of FAIR principles. International Journal on Digital Libraries, 1-10. https://doi.org/10.1007/s00799-020-00291-7   DOI
31 Collins, S., Genova, F., Harrower, N., Hodson, S., Jones, S., Loaksonen, L., Mietchen, D., Petrauskaite, R., & Wittenburg, P. (2018). Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data. http://dx.doi.org/10.2777/1524
32 Corpas M, Kovalevskaya NV, McMurray A, & Nielsen FGG (2018). A FAIR guide for data providers to maximise sharing of human genomic data. PLoS Comput Biol, 14(3), e1005873. https://doi.org/10.1371/journal.pcbi.1005873   DOI
33 Government of the Netherlands (2017). Progress towards the European Open Science Cloud. Retrieved April 19, 2021, Available: https://www.government.nl/latest/news/2017/12/01/progress-towards-the-european-open-science-cloud
34 FAIRMetrics (2021). FAIR Maturity Indicators and Tools. Retrieved May 12, 2021, Available: https://github.com/FAIRMetrics/Metrics
35 Shin, J. & Kwak, S. (2013). A Review of Literature and Cases for Developing Digital Content Archives. Journal of Social Science, 24(1), 305-330.
36 AFAWG (2017). Policy statement on F.A.I.R. access to Austrailia's research outputs. Retrieved April 19, 2021, Available: https://www.fair-access.net.au/fair-statement
37 ARDC (2021). SATIFYD: Self-Assessment Tool to Improve the FAIRness of Your Dataset. Retrieved May 12, 2021, Available: https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/
38 Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3, 160018. https://doi.org/10.1038/sdata.2016.18   DOI
39 Wilkinson, M. D., Dumontier, M., Sansone, S. A., Prieto, M., Batisata, D., McQuilton, P., Kuhn, T., Rocca-Serra, P., Crosas, M., & Schultes, E. (2019). Evaluating FAIR maturity through a scalable, automated, community-governed framework. Scientific Data, 6(174), 1-12. https://doi.org/10.1038/s41597-019-0184-5   DOI
40 FORCE11 (2016). The FAIR Data Principles. Retrieved April 19, 2021, Available: https://www.force11.org/group/fairgroup/fairprinciples
41 Helliwell, J. R., Minor, W., Weiss, M. S., Garman, E. F., Read, R. J., Newman, J., Raaij, M. J., Hajdu, J., & Baker, E. N. (2019). Findable Accessible Interoperable Re-usable(FAIR) diffraction data are coming to protein crystallography. Journal of applied crystallography, 52(Pt 3), 495-497. https://doi.org/10.1107/S2052252519005918   DOI
42 Jacobsen, A., Kaliyaperumal, R., Bonino, S., Mons, B., Schultes, E., Roos, M., & Thompson, M. (2019). A Generic Workflow for the Data FAIRification Process. Data Intelligence. 2(1-2). 56-65. https://doi.org/10.1162/dint_a_00028   DOI
43 Koster, L. & Woutersen-Windhouwer, S. (2018). FAIR Principles for Library, Archive and Museum Collections: A proposal for standards for reusable collections. Code4Lib Journal, 40. http://journal.code4lib.org/articles/13427
44 Choi, M., Lee, S., & Lee, S. (2017). Research Data Management of Science and Technology Research Institutes in Korea. The Journal of the Korea Contents Association, 17(12), 117-126. https://doi.org/10.5392/JKCA.2017.17.12.117   DOI
45 Candela, G., Saez, M. D., Escobar Esteban, Mp., & Marco-Such, M. (2020). Reusing digital collections from GLAM institutions. Journal of Information Science. https://doi.org/10.1177%2F0165551520950246   DOI
46 Haux, C. & Knaup, P. (2019). Using FAIR Metadata for Secondary Use of Administrative Claims Data. Studies in health technology and informatics, 264, 1472-1473. https://doi.org/10.3233/shti190490   DOI
47 Hettne, K. M., Verhaar, P., Schultes, E., & Sesink, L. (2020). From FAIR Leading Practices to FAIR Implementation and Back: An Inclusive Approach to FAIR at Leiden University Libraries. Data Science Journal, 19(1), 40. http://doi.org/10.5334/dsj-2020-040   DOI
48 Nitecki, D. A. & Alter, A. (2021). Leading FAIR Adoption Across the Institution: A Collaboration Between an Academic Library and a Technology Provider. Data Science Journal, 20(1), 6. http://doi.org/10.5334/dsj-2021-006   DOI