The FAIRification process for data stewardship: A comprehensive discourse on the implementation of the FAIR principles for data visibility,interoperability and management
Restricted accessResearch articleFirst published online June, 2025
The FAIRification process for data stewardship: A comprehensive discourse on the implementation of the FAIR principles for data visibility,interoperability and management
This article discusses the importance of good data management for knowledge discovery and innovation. It outlines the FAIR principles, which provide guidelines for publishing digital resources in a way that makes the data findable, accessible, interoperable and reusable. Using a systematic literature review, the study focuses on the implementation of these principles in research data management and their applicability in data repositories and data centres. It highlights the importance of implementing these principles systematically, allowing stakeholders to choose the minimum requirements and provide a vision for implementing them in data repositories and data centres. The article also highlights the steps in the FAIRification process, which can enhance data interoperability, discovery and reusability. The paper is unique in that it explores how implementing FAIR principles impacts data management for data hosting platforms on a global scale.
AdelekeOOtooEJ (2014) An integrated metadata access infrastructure for a network of federated curated data repositories. OCLC Systems and Services30(2): 91–107.
2.
AnanthakrishnanRChardKD’ArcyM, et al. (2020) An open ecosystem for pervasive use of persistent identifiers. In: Practice and Experience in Advanced Research Computing 2020: Catch the Wave (PEARC'20). New York, USA: Association of Computing Machinery, 99–105.
3.
AnjariaKA (2020) Computational implementation and formalism of FAIR data stewardship principles. Data Technologies and Applications54(2): 193–214.
4.
AyrisPBerthouJYBruceR, et al. (2016) Realising the European open science cloud. European Union. https://doi.org/10.2777/940154
5.
Bahim C, Casorrán-Amilburu C, Dekkers M, et al. (2020) The FAIR data maturity model: An approach to harmonise FAIR assessments. Data Science Journal 19(41): 1–7.
Betancort CabreraNBongartzECDörrenbächerN, et al. (2020) White Paper on implementing the FAIR principles for data in the social, behavioural, and economic sciences. RatSWD Working Paper no. 274, Rat für Sozial- und Wirtschaftsdaten (RatSWD), Berlin, Germany. Available at: https://www.konsortswd.de/en/publication/wp274-2020/
8.
BishopBWHankC (2018) Measuring FAIR principles to inform fitness for use. International Journal of Digital Curation13(1): 35–46.
9.
BloemersMMontesantiA (2020) The FAIR funding model: Providing a framework for research funders to drive the transition toward FAIR data management and stewardship practices. Data Intelligence2(1–2): 171–180.
10.
BoeckhoutMZielhuisGABredenoordAL (2018) The FAIR Guiding Principles for data stewardship: Fair enough?European Journal of Human Genetics26(7): 931–936.
11.
CalamaiSFrontiniF (2018) FAIR data principles and their application to speech and oral archives. Journal of New Music Research47(4): 339–354.
12.
CollinsSGenovaFHarrowerN, et al. (2018) Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data. Available at: https://www.vdu.lt/cris/handle/20.500.12259/103794
CookDJMulrowCDHaynesRB (1997) Systematic reviews: Synthesis of best evidence for clinical decisions. Annals of Internal Medicine126(5): 376–380.
15.
CrosswellLCThorntonJM (2012) ELIXIR: A distributed infrastructure for European biological data. Trends in Biotechnology30(5): 241–242.
16.
DegbeloA (2022) FAIR geovisualizations: Definitions, challenges, and the road ahead. International Journal of Geographical Information Science36(6): 1059–1099.
17.
De la HidalgaANGoodallJAnyikaC, et al. (2022) Designing a data infrastructure for catalysis science aligned to FAIR data principles. Catalysis Communications162: Article 106384.
18.
DevarajuAHuberR (2021) An automated solution for measuring the progress toward FAIR research data. Patterns2(11): Article 100370.
19.
DumontierMWesleyK (2018) Advancing discovery science with FAIR data stewardship: Findable, accessible, interoperable, reusable. Serials Librarian74(1–4): 39–48.
European Commission, Directorate-General for Research and Innovation (2016) Open Innovation, Open Science, Open to the World: A Vision for Europe. Brussels: European Commission Publications Office.
JacobsenADe Miranda AzevedoRJutyN, et al. (2020a) FAIR principles: Interpretations and implementation considerations. Data Intelligence2(1–2): 10–29.
33.
JacobsenAKaliyaperumalRDa Silva SantosLOB, et al. (2020b) A generic workflow for the data FAIRification process. Data Intelligence2(1–2): 56–65.
34.
JagodnikKMKoplevSJenkinsSL, et al. (2017) Developing a framework for digital objects in the Big Data to Knowledge (BD2 K) commons: Report from the Commons Framework Pilots workshop. Journal of Biomedical Informatics71: 49–57.
35.
JonesSPerglRHooftR, et al. (2020) Data management planning: How requirements and solutions are beginning to converge. Data Intelligence2(1–2): 208–219.
36.
KosterLWoutersen-WindhouwerS (2018) FAIR principles for library, archive and museum collections: A proposal for standards for reusable collections. Code4Lib Journal40: 1–12.
LabastidaIMargoniT (2020) Licensing FAIR data for reuse. Data Intelligence2(1–2): 199–207.
39.
LamprechtALGarciaLKuzakM, et al. (2020) Towards FAIR principles for research software. Data Science3(1): 37–59.
40.
LandiAThompsonMGiannuzziV, et al. (2020) The ‘A’ of FAIR – as open as possible, as closed as necessary. Data Intelligence2(1–2): 47–55.
41.
LindenMProcházkaMLappalainenI, et al. (2018) Common ELIXIR service for researcher authentication and authorisation. F1000Research7: Article 1199.
42.
MasindeJMSanyaO (2022) Analysis of interoperability, security and usability of digital repositories in Kenyan institutions of higher learning. Data and Information Management6(4): Article 100011.
43.
McKibbonA (2006) Systematic reviews and librarians. Library Trends55(1): 202–215.
44.
McQuiltonPBatistaDBeyanO, et al. (2020) Helping the consumers and producers of standards, repositories and policies to enable FAIR data. Data Intelligence2(1–2): 151–157.
45.
MoherDLiberatiATetzlaffJ, et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine151(4): 264–269.
46.
MokraneMReckerJ (2019) CoreTrustSealCertified repositories: Enabling findable, accessible, interoperable and reusable (FAIR) data. In: iPRES 2019: 16th international conference on digital preservation, Amsterdam, The Netherlands, 16–20 September 2019.
47.
MonsB (2018) Data Stewardship for Open Science: Implementing FAIR Principles. Boca Raton, FL: CRC Press.
48.
MonsBNeylonCVelteropJ, et al. (2017) Cloudy, increasingly FAIR; revisiting the FAIR data guiding principles for the European open science cloud. Information Services and Use37(1): 49–56.
RocheDGKruukLELanfearR, et al. (2015) Public data archiving in ecology and evolution: How well are we doing?PLoS Biology13(11): Article e1002295.
52.
SansoneSAMcQuiltonPRocca-SerraP, et al. (2019) FAIRsharing as a community approach to standards, repositories and policies. Nature Biotechnology37(4): 358–367.
53.
SchefflerMAeschlimannMAlbrechtM, et al. (2022) FAIR data enabling new horizons for materials research. Nature604(7907): 635–642.
54.
SchultesEAStrawnGOMonsB (2018) Ready, set, GO FAIR: Accelerating convergence to an internet of FAIR data and services. DAMDID/RCDL19: 23.
55.
SinaciAANúñez-BenjumeaFJGencturkM, et al. (2020) From raw data to FAIR data: The FAIRification workflow for health research. Methods of Information in Medicine59(S01): e21–e32.
56.
StarrJCastroECrosasM, et al. (2015) Achieving human and machine accessibility of cited data in scholarly publications. PeerJ Computer Science1: Article e1.
57.
SykesJ (2008) Managing the UK’s research data: Towards a UK research data service. New Review of Information Networking14(1): 21–36.
58.
ThompsonMBurgerKKaliyaperumalR, et al. (2020) Making FAIR easy with FAIR tools: From creolization to convergence. Data Intelligence2(1–2): 87–95.
59.
TomoyoseKArakakiACS (2021) Vocabularies for publishing research data. Cataloging and Classification Quarterly60(1): 69–85.
VelteropJSchultesE (2020) An academic publishers’ GO FAIR implementation network (APIN). Information Services and Use40(4): 333–341.
63.
WilkinsonMDDumontierMAalbersbergIJ, et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data3: Article 160018.
64.
Wilkinson MD, Sansone SA, Schultes E, et al. (2018) A design framework and exemplar metrics for FAIRness. Scientific Data 5(1): 1–4.
65.
WiseJDe BarronAGSplendianiA, et al. (2019) Implementation and relevance of FAIR data principles in biopharmaceutical R&D. Drug Discovery Today24(4): 933–938.
