Towards FAIR principles for research software

Anna-lena Lamprecht; Leyla Garcia; Mateusz Kuzak; Carlos Martinez; Ricardo Arcila; Eva Martin Del Pico; Victoria Dominguez Del 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; Paul Groth; Paul Groth; Michel Dumontier

doi:10.3233/DS-190026

Towards FAIR principles for research software

Anna-lena Lamprecht, Leyla Garcia, Mateusz Kuzak, Carlos Martinez, Ricardo Arcila, Eva Martin Del Pico, Victoria Dominguez Del 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, Paul Groth, Paul GrothMichel Dumontier

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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 basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we 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. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.

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

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Lamprecht, A., Garcia, L., Kuzak, M., Martinez, C., Arcila, R., Martin Del Pico, E., Dominguez Del 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., Groth, P., ... Dumontier, M. (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 basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we 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. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.",

author = "Anna-lena Lamprecht and Leyla Garcia and Mateusz Kuzak and Carlos Martinez and Ricardo Arcila and {Martin Del Pico}, Eva and {Dominguez Del 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 and Paul Groth and Paul Groth and Michel Dumontier",

year = "2020",

month = jun,

day = "12",

doi = "10.3233/DS-190026",

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Lamprecht, A, Garcia, L, Kuzak, M, Martinez, C, Arcila, R, Martin Del Pico, E, Dominguez Del 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, Groth, P, Groth, P & Dumontier, M 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 Del Pico, Eva

AU - Dominguez Del 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

AU - Groth, Paul

AU - Dumontier, Michel

PY - 2020/6/12

Y1 - 2020/6/12

N2 - 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 basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we 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. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.

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 basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we 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. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.

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M3 - Article

SN - 2451-8484

VL - 3

SP - 37

EP - 59

JO - Data Science

JF - Data Science

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ER -

Towards FAIR principles for research software

Abstract

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