Have a personal or library account? Click to login
The FAIR Assessment Conundrum: Reflections on Tools and Metrics Cover

The FAIR Assessment Conundrum: Reflections on Tools and Metrics

Open Access
|May 2024

References

  1. Aguilar Gómez, F 2022 FAIR EVA (Evaluator, Validator & Advisor). Spanish National Research Council. DOI: 10.20350/DIGITALCSIC/14559
  2. Amdouni, E, Bouazzouni, S and Jonquet, C 2022 O’FAIRe: Ontology FAIRness Evaluator in the AgroPortal Semantic Resource Repository. In: Groth, P, et al. (eds.), The Semantic Web: ESWC 2022 Satellite Events. Cham: Springer International Publishing (Lecture Notes in Computer Science). pp. 8994. DOI: 10.1007/978-3-031-11609-4_17
  3. Ammar, A, et al. 2020 A semi-automated workflow for fair maturity indicators in the life sciences. Nanomaterials, 10(10): 2068. DOI: 10.3390/nano10102068
  4. Bahim, C, et al. 2020 The FAIR Data Maturity Model: An approach to harmonise FAIR Assessments. Data Science Journal, 19: 41. DOI: 10.5334/dsj-2020-041
  5. Bahim, C, Dekkers, M and Wyns, B 2019 Results of an Analysis of Existing FAIR Assessment Tools. RDA Report. DOI: 10.15497/rda00035
  6. Bonello, J, Cachia, E and Alfino, N 2022 AutoFAIR-A portal for automating FAIR assessments for bioinformatics resources. Biochimica et Biophysica Acta (BBA) – Gene Regulatory Mechanisms, 1865(1): 194767. DOI: 10.1016/j.bbagrm.2021.194767
  7. Clarke, D J B, et al. 2019 FAIRshake: Toolkit to Evaluate the FAIRness of Research Digital Resources. Cell Systems, 9(5): 417421. DOI: 10.1016/j.cels.2019.09.011
  8. Czerniak, A, et al. 2021 Lightweight FAIR assessment in the OpenAIRE Validator. In: Open Science Fair 2021. Available at: https://pub.uni-bielefeld.de/record/2958070.
  9. David, R, et al. 2023 Umbrella Data Management Plans to integrate FAIR Data: Lessons from the ISIDORe and BY-COVID Consortia for Pandemic Preparedness. Data Science Journal, 22: 35. DOI: 10.5334/dsj-2023-035
  10. d’Aquin, M, et al. 2023 FAIREST: A framework for assessing research repositories. Data Intelligence, 5(1): 202241. DOI: 10.1162/dint_a_00159
  11. De Miranda Azevedo, R and Dumontier, M 2020 considerations for the conduction and interpretation of FAIRness evaluations. Data Intelligence, 2(1–2): 285292. DOI: 10.1162/dint_a_00051
  12. Devaraju, A and Huber, R 2020 F-UJI – An automated FAIR Data Assessment tool. Zenodo. DOI: 10.5281/ZENODO.4063720
  13. Gaignard, A, et al. 2023 FAIR-Checker: Supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards. Journal of Biomedical Semantics, 14(1): 7. DOI: 10.1186/s13326-023-00289-5
  14. Garijo, D, Corcho, O and Poveda-Villalòn, M 2021 FOOPS!: An ontology pitfall scanner for the FAIR Principles. [Posters, Demos, and Industry Tracks]. In: International Semantic Web Conference (ISWC) 2021.
  15. Gehlen, K P, et al. 2022 Recommendations for discipline-specific FAIRness Evaluation derived from applying an ensemble of evaluation tools. Data Science Journal, 21: 7. DOI: 10.5334/dsj-2022-007
  16. Goble, C, et al. 2020 FAIR Computational Workflows. Data Intelligence, 2(1–2): 108121. DOI: 10.1162/dint_a_00033
  17. González, E, Benítez, A and Garijo, D 2022 FAIROs: Towards FAIR Assessment in research objects. Lecture Notes in Computer Science, vol 13541 In: Silvello, G, et al. (eds.), Linking Theory and Practice of Digital Libraries. Cham: Springer International Publishing. pp. 6880. DOI: 10.1007/978-3-031-16802-4_6
  18. Hettne, K M, et al. 2023 FIP2DMP: Linking data management plans with FAIR implementation profiles. FAIR Connect, 1(1): 2327. DOI: 10.3233/FC-221515
  19. Jacobsen, A, et al. 2020 FAIR Principles: Interpretations and implementation considerations. Data Intelligence, 2(1–2): 1029. DOI: 10.1162/dint_r_00024
  20. Katz, D S, Gruenpeter, M and Honeyman, T 2021 Taking a fresh look at FAIR for research software. Patterns, 2(3): 100222. DOI: 10.1016/j.patter.2021.100222
  21. Krans, N A, et al. 2022 FAIR assessment tools: evaluating use and performance. NanoImpact, 27: 100402. DOI: 10.1016/j.impact.2022.100402
  22. Lamprecht, A L, et al. 2020 Towards FAIR principles for research software. Data Science, 3(1): 3759. DOI: 10.3233/DS-190026
  23. Mangione, D, Candela, L and Castelli, D 2022 A taxonomy of tools and approaches for FAIRification, In: 18th Italian Research Conference on Digital Libraries. IRCDL, Padua, Italy, 2022.
  24. Matentzoglu, N, et al. 2018 MIRO: guidelines for minimum information for the reporting of an ontology. Journal of Biomedical Semantics, 9(1):. 6. DOI: 10.1186/s13326-017-0172-7
  25. Mons, B, et al. 2017 Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use, 37(1): 4956. DOI: 10.3233/ISU-170824
  26. Musen, M A, O’Connor, M J, Schultes, E, et al. 2022 Modeling community standards for metadata as templates makes data FAIR. Sci Data, 9: 696. DOI: 10.1038/s41597-022-01815-3
  27. Rocca-Serra, P, et al. 2023 The FAIR Cookbook – The essential resource for and by FAIR doers. Scientific Data, 10(1): 292. DOI: 10.1038/s41597-023-02166-3
  28. Salazar, A, et al. 2023 How research data management plans can help in harmonizing open science and approaches in the digital economy. Chemistry – A European Journal, 29(9): e202202720. DOI: 10.1002/chem.202202720
  29. Schultes, E, Magagna, B, Hettne, K M, Pergl, R, Suchánek, M and Kuhn, T. 2020 Reusable FAIR implementation profiles as accelerators of FAIR convergence. In: Grossmann, G and Ram, S (eds.), Advances in Conceptual Modeling. ER 2020, Lecture Notes in Computer Science, Vol. 12584. Cham: Springer. DOI: 10.1007/978-3-030-65847-2_13
  30. SMD Data Repository Standards and Guidelines Working Group 2024 How to make NASA Science Data more FAIR. Available at: https://docs.google.com/document/d/1ELb2c7ajYywt8_pzHsNq2a352YjgzixmDh5KP4WfY9s/edit?usp=sharing.
  31. Soiland-Reyes, S, et al. 2022 Packaging research artefacts with RO-Crate. Data Science, 5(2): 97138. DOI: 10.3233/DS-210053
  32. Specht, A, et al. 2023 The Value of a data and digital object management plan (D(DO)MP) in fostering sharing practices in a multidisciplinary multinational project. Data Science Journal, 22: 38. DOI: 10.5334/dsj-2023-038
  33. Sun, C, Emonet, V and Dumontier, M 2022 A comprehensive comparison of automated FAIRness Evaluation Tools, In: Semantic Web Applications and Tools for Health Care and Life Sciences. 13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences. Leiden, Netherlands (Virtual Event) on 10th–14th January 2022, pp. 4453.
  34. Thompson, M, et al. 2020 Making FAIR easy with FAIR Tools: From creolization to convergence. Data Intelligence, 2(1–2): 8795. DOI: 10.1162/dint_a_00031
  35. Wilkinson, M D, et al. 2016 The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1): 160018. DOI: 10.1038/sdata.2016.18
  36. Wilkinson, M D, et al. 2019 Evaluating FAIR maturity through a scalable, automated, community-governed framework. Scientific Data, 6(1): 174. DOI: 10.1038/s41597-019-0184-5
Language: English
Submitted on: Nov 8, 2023
|
Accepted on: Apr 24, 2024
|
Published on: May 27, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Leonardo Candela, Dario Mangione, Gina Pavone, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.