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DOI:
https://doi.org/10.47909/978-9916-9974-5-1.92Keywords:
FAIR data, FAIRification, research data managementAbstract
The term FAIRification has become widespread among professionals whose work is related to research data management. However, little is known about FAIRification practices. This paper aims to examine FAIRification practices applied to research data from all areas of knowledge. The research is an exploratory study, using documentary analysis and content analysis methods. The results show that the literature on the subject is recent and generally in English. The papers, projects, and scientific articles analyzed show the development of infrastructures and tools but also the need for a culture of research data management. It is concluded that most of the experiences in FAIRification have been directed to the development of workflows, infrastructures, and tools to comply with FAIR principles. There is a predominance of FAIRification of data in the health research domain, with a greater boom after COVID-19.
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Alharbi, E., Gadiya, Y., Henderson, D., Zaliani, A., Delfin-Rossaro, A., Cambon-Thomsen, A., Kohler, M., Witt, G., Welter, D., Juty, N., Jay, C., Engkvist, O., Goble, C., Reilly, D. S., Satagopam, V., Ioannidis, V., Gu, W., & Gribbon, P. (2022). Selection of data sets for FAIRification in drug discovery and development: Which, why, and how? Drug Discovery Today, 27(8), 2080–2085. https://doi.org/10.1016/j.drudis.2022.05.010
Alharbi, E., Skeva, R., Juty, N., Jay, C., & Goble, C. (2023). A FAIR-Decide framework for pharmaceutical R&D: FAIR data cost–benefit assessment. Drug Discovery Today, 28(4), Article 103510. https://doi.org/10.1016/j.drudis.2023.103510
Alvarez-Romero, C., Martínez-García, A., Román-Villarán, E., & Parra-Calderón, C. L. (2021). FAIR4Health: Reutilizar y Compartir Datos de Salud Aplicando los Principios FAIR en un Modelo Híbrido de Nube y Datos Distribuidos. Informática + Salud, 143, 16–20.
Anglada, L. (2021). Implementação de serviços nacionais e estratégias institucionais para a Gestão de Dados de Investigação. Foro Portugués de Gestión de Datos de Investigación, Coimbra-Barcelona. https://forumgdi.rcaap.pt/wp-content/uploads/2021/11/Keynote_2111RDMiRDR_ForoPortuguesDatosCoimbraV1.pdf
Annane, A., Kamel, M., Trojahn, C., Aussenac-Gilles, N., Comparot, C., & Baehr, C. (2021). Towards the FAIRification of meteorological data: A meteorological semantic model. metadata and semantic research 15th international conference, MTSR 2021 (pp. 81–93). Springer International Publishing.
Aventurier, P., Fortuno, S., Szabo, D., Alaux, M., Bartelemy, C., Bonnet, P., Catherine, H., Dzalé, E., Deboin, M.-C., Decker, L., Desconnets, J.-C., Doux, G., Mougin, C., Perez, J., & Sabot, F. (2022, June 16). Data FAIRification in a cross-institutional governance framework: recommendations from the ANR-BRIDGE project. Research Data Alliance. 19th Plenary Meeting, Séoul, South Korea. Zenodo. https://doi.org/10.5281/zenodo.6652405
Azeroual, O., Schöpfel, J., Pölönen, J., & Nikiforova, A. (2023). FAIRification of CRIS: A review. In: F. Coenen, et al. (Eds.), Knowledge discovery, knowledge engineering and knowledge management. IC3K 2022. Communications in computer and information science (vol. 1842). Springer.
Bernabé, C. H., Jacobsen, A., Queralt Rosinach, N., Bonino da Silva Santos, L. O., Silva Souza, V. E., Mons, B., & Roos, M. (2021). Goal-models to support communication, planning and guiding of FAIRification. Zenodo. https://doi.org/10.5281/zenodo.5784628
European Commission. (n.d.). ‘FAIRification’ of valuable data sets to share the wealth. https://cordis.europa.eu/article/id/444085-fairification-of-valuable-data-sets-to-share-the-wealth
Cox, A. M., & Pinfield, S. (2014). Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science, 46(4), 299–316. https://doi.org/10.1177/0961000613492542
dos Santos, B., Bernabé, C. H., Zhang, S., Abaza, H., Benis, N., Cámara, A., Cornet, R., Cornec, L., Peter, Schaefer, F., van, Swertz, M. A., Wilkinson, M. D., Jacobsen, A., & Roos, M. (2022). Towards FAIRifcation of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries. Orphanet Journal of Rare Diseases, 17, 436. https://doi.org/10.1186/s13023-022-02558-5
Elsevier. (2019). Realizing the potential of FAIR data for pharmaceutical R&D. https://www.elsevier.com/__data/assets/pdf_file/0007/961972/Potential-of-FAIR-Data-in-Pharma_whitepaper_PLS_WEB.pdf
EOSC-Nordic. (2020). EOSC-NORDIC FAIRification study testing F-UJI. https://eoscsecretariat.eu/system/files/fairsfair_eosc_nordic_adoption_story.pdf
GO FAIR. (2017). Progress towards the European Open Science Cloud: GO FAIR Office established. https://www.go-fair.org/2017/12/07/progress-towards-european-open-science-cloud-go-fair-office-established/
Groenen, K. H. J., Jacobsen, A., Kersloot, M. G., dos Santos, B., Enckevort, E., Kaliyaperumal, R., Arts, D. L., ‘t Hoen, P. A. C., Cornet, R., Roos, M., & Schultze, L. (2021). The de novo FAIRification process of a registry for vascular anomalies. Orphanet Journal of Rare Diseases, 16(1), 376. https://doi.org/10.1186/s13023-021-02004-y
Gundersen, S., Boddu, S., Capella-Gutierrez, S., Drabløs, F., Fernández, J. M., Kompova, R., Titov, D., Zerbino, D., & Hovig, E. (2021). Recommendations for the FAIRification of genomic track metadata [version 1; peer review: 2 approved]. F1000Research, 10, 268. https://doi.org/10.12688/f1000research.28449.1
Inau, E. T., Sack, J., Waltemath, D., & Zeleke, A. A. (2023). Initiatives, concepts, and implementation practices of the findable, accessible, interoperable, and reusable data principles in health data stewardship: Scoping review. Journal of Medical Internet Research, 25, e45013. https://doi.org/10.2196/45013
Jacobsen, A., Kaliyaperumal, R., Bonino, L. O., Mons, B., Schultes, E., Roos, M., & Thompson, M. (2020). A generic workflow for the data FAIRification process. Data Intelligence, 2(1–2), 56–65. https://doi.org/10.1162/dint_a_00028
Jacobsen, A., Thompson, M., Hanauer, M., Beltran, S., Gray, A., Juty, N., Ehrhart, F., Evelo, CT. T., & Ross, M. (2018). ELIXIR-EXCELERATE: Fast-track ELIXIR implementation and drive early user exploitation across the life sciences. ELIXIR Infrastructure for Rare Disease Research. https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5bd475987&appId=PPGMS
Mangione, D., Candela, L., & Castelli, D. (2022). A taxonomy of tools and approaches for FAIRification. 18th Italian Research Conference on Digital Libraries, Padova, Italy. https://ceur-ws.org/Vol-3160/paper6.pdf
Mavraki, D., Chatzinikolaoy, E., & Panteri, E. (2021). FAIRification process for RIs: The case of the LifeWatchGreece Research Infrastructure. LifeWatch ERIC Workshop “e-Science for NIS Research”. https://www.lifewatch.eu/wp-content/uploads/2022/01/IJINISW_S1_Mavraki.pdf
Österle, S., & Touré, V. (2022). FAIRification of health-related data in the Swiss Personalized Health Network. MILA Seminar, University of Greifswald. https://www.medizin.uni-greifswald.de/medizininformatik/fileadmin/user_upload/poster_presentation_articles/by_externals/2022-01-24-MILA_seminar_SO_VT.pdf
Parciak, M., Suhr, M., Schmidt, C., Bönisch, C., Löhnhardt, B., Kesztyüs, D., & Kesztyüs, T. (2023). FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital. BMC Medical Informatics and Decision Making, 23, 94. https://doi.org/10.1186/s12911-023-02195-3
Queiroz, N., Borges, V., Rodrigues, H. F., Machado, M. L., & Rabello, G. (2022). A practical approach of actions for FAIRification workflows. ArXiv, abs/2201.07866.
Ribeiro, C. J. S., de Sousa, C. S., Tertulino, C. Í., do Amaral, I. S., dos Santos, M. J. S., Cóquero, S. D. M. S., & de Ulhôa, M. T. (2023). Adopción de los principios FAIR (proceso de FAIRificación) en conjuntos de datos: interconexión de noticias de publicaciones periódicas musicales del siglo XIX. Ibersid: Revista De Sistemas De información Y documentación, 17(2), 123–124. https://doi.org/10.54886/ibersid.v17i2.4949
Sinaci, A. A., Núñez-Benjumea, F. J., Gencturk, M., Jauer, M. L., Deserno, T., Chronaki, C., Cangioli, G., Cavero-Barca, C., Rodríguez-Pérez, J. M., Pérez-Pérez, M. M., Laleci Erturkmen, G. B., Hernández-Pérez, T., Méndez-Rodríguez, E., & Parra-Calderón, C. L. (2020). From raw data to FAIR data: The FAIRification workflow for health research. Methods of Information in Medicine, 59, e21–e32.
Touré, V., Krauss, P., Gnodtke, K., Buchhorn, J., Unni, D., Horki, P., Raisaro, J. L., Kalt, K., Teixeira, D., Crameri, K., & Österle, S. (2023). FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network. Science Data, 10(127). https://doi.org/10.1038/s41597-023-02028-y
Welter, D., Juty, N., Rocca-Serra, P., Xu, F., Henderson, D., Gu, W., Strubel, J., Giessmann, R. T., Emam, I., Gadiya, Y., Abbassi-Daloii, T., Alharbi, E., Gray, A. J. G., Courtot, M., Gribbon, P., Ioannidis, V., Reilly, D. S., Lynch, N., Boiten, J.-W., … Burdett, T. (2023). FAIR in action—A flexible framework to guide FAIRification. Scientific Data, 10(1). https://doi.org/10.1038/s41597-023-02167-2
Wilkinson, M. D., Dumontier, M., Aalbersberg, I., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), Article 160018. https://doi.org/10.1038/sdata.2016.18
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Copyright (c) 2024 Dianelis Olivera Batista, Amed Abel Leiva Mederos, María Josefa Peralta González
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