Towards FAIR principles for research software

Anna-Lena Lamprecht; Leyla Garcia; Mateusz Kuzak; Carlos Martinez; Ricardo Arcila; Eva Martin; Victoria Dominguez De Angel; Stephanie van de Sandt; Jon Ison; Paula Andrea Martinez; Peter McQuilton; Alfonso Valencia; Jennifer Harrow; Fotis Psomopoulos; Josep Ll. Gelpi; Neil Chue Hong; Carole Goble; Salvador Capella-Gutierrez

doi:10.3233/DS-190026

Towards FAIR principles for research software

Anna-Lena Lamprecht, Leyla Garcia, Mateusz Kuzak, Carlos Martinez, Ricardo Arcila, Eva Martin, Victoria Dominguez De Angel, Stephanie van de Sandt, Jon Ison, Paula Andrea Martinez, Peter McQuilton, Alfonso Valencia, Jennifer Harrow, Fotis Psomopoulos, Josep Ll. Gelpi, Neil Chue Hong, Carole Goble, Salvador Capella-Gutierrez

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as a basis for the development of definite community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with respect to the application of the FAIR principles, present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Furthermore, we discuss desired characteristics of research software that go beyond FAIR.

Original language	English
Pages (from-to)	37-59
Journal	Data Science
Volume	3
Issue number	1
Early online date	13 Nov 2019
DOIs	https://doi.org/10.3233/DS-190026
Publication status	Published - 12 Jun 2020

Access to Document

10.3233/DS-190026Licence: Creative Commons: Attribution (CC-BY)

Towards FAIR principles_LAMPRECHT_DOA14102019_VOR_CC-BYFinal published version, 257 KBLicence: Creative Commons: Attribution (CC-BY)

https://content.iospress.com/articles/data-science/ds190026Licence: Creative Commons: Attribution (CC-BY)

1 Finished

The UK Software Sustainability Institute: Phase 3
Chue Hong, N.
EPSRC
1/12/18 → 30/11/23
Project: Research

2 Paper
2 Article
1 Commissioned report
1 Abstract

FAIR Principles for Research Software (FAIR4RS Principles) 1.0
Chue Hong, N. P., Katz, D. S. & Barker, M., 24 May 2022, Research Data Alliance Recommendations and Outputs 30 p.
Research output: Contribution to specialist publication › Article

Open Access
File
The FAIR4RS team: Working together to make research software FAIR
Katz, D. S., Barker, M., Chue Hong, N. P., Garcia Castro, L. J. & Martinez, P. A., 8 Jun 2021. 4 p.
Research output: Contribution to conference › Paper › peer-review

Open Access
File
FAIR Research Software and Science Gateways
Katz, D. S., Barker, M., Garcia Castro, L. J., Chue Hong, N. P., Gruenpeter, M., Harrow, J. L., Martinez Ortiz, C., Martinez, P. A. & Psomopoulos, F., 24 May 2021, (Accepted/In press).
Research output: Contribution to conference › Abstract › peer-review

Open Access

Cite this

Lamprecht, A.-L., Garcia, L., Kuzak, M., Martinez, C., Arcila, R., Martin, E., Dominguez De Angel, V., van de Sandt, S., Ison, J., Martinez, P. A., McQuilton, P., Valencia, A., Harrow, J., Psomopoulos, F., Gelpi, J. L., Chue Hong, N., Goble, C., & Capella-Gutierrez, S. (2020). Towards FAIR principles for research software. Data Science, 3(1), 37-59. https://doi.org/10.3233/DS-190026

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title = "Towards FAIR principles for research software",

abstract = "The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as a basis for the development of definite community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with respect to the application of the FAIR principles, present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Furthermore, we discuss desired characteristics of research software that go beyond FAIR.",

author = "Anna-Lena Lamprecht and Leyla Garcia and Mateusz Kuzak and Carlos Martinez and Ricardo Arcila and Eva Martin and {Dominguez De Angel}, Victoria and {van de Sandt}, Stephanie and Jon Ison and Martinez, {Paula Andrea} and Peter McQuilton and Alfonso Valencia and Jennifer Harrow and Fotis Psomopoulos and Gelpi, {Josep Ll.} and {Chue Hong}, Neil and Carole Goble and Salvador Capella-Gutierrez",

year = "2020",

month = jun,

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doi = "10.3233/DS-190026",

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Lamprecht, A-L, Garcia, L, Kuzak, M, Martinez, C, Arcila, R, Martin, E, Dominguez De Angel, V, van de Sandt, S, Ison, J, Martinez, PA, McQuilton, P, Valencia, A, Harrow, J, Psomopoulos, F, Gelpi, JL, Chue Hong, N, Goble, C & Capella-Gutierrez, S 2020, 'Towards FAIR principles for research software', Data Science, vol. 3, no. 1, pp. 37-59. https://doi.org/10.3233/DS-190026

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T1 - Towards FAIR principles for research software

AU - Lamprecht, Anna-Lena

AU - Garcia, Leyla

AU - Kuzak, Mateusz

AU - Martinez, Carlos

AU - Arcila, Ricardo

AU - Martin, Eva

AU - Dominguez De Angel, Victoria

AU - van de Sandt, Stephanie

AU - Ison, Jon

AU - Martinez, Paula Andrea

AU - McQuilton, Peter

AU - Valencia, Alfonso

AU - Harrow, Jennifer

AU - Psomopoulos, Fotis

AU - Gelpi, Josep Ll.

AU - Chue Hong, Neil

AU - Goble, Carole

AU - Capella-Gutierrez, Salvador

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AB - The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as a basis for the development of definite community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with respect to the application of the FAIR principles, present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Furthermore, we discuss desired characteristics of research software that go beyond FAIR.

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JO - Data Science

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Towards FAIR principles for research software

Abstract / Description of output

Access to Document

Fingerprint

Projects

The UK Software Sustainability Institute: Phase 3

Research output

FAIR Principles for Research Software (FAIR4RS Principles) 1.0

The FAIR4RS team: Working together to make research software FAIR

FAIR Research Software and Science Gateways

Cite this