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Towards FAIR principles for research software @ FAIR Software Session, National eScience Symposium 2019, Amsterdam, Netherlands

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Slides of a presentation about our paper "Towards FAIR Principles for Research Software" (https://doi.org/10.3233/DS-190026) that I gave at the Dutch National eScience Symposium 2019 in Amsterdam (https://www.esciencesymposium2019.nl/).

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.

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Towards FAIR principles for research software @ FAIR Software Session, National eScience Symposium 2019, Amsterdam, Netherlands

  1. 1. Faculty of Science Departmentof Informationand Computing Sciences SoftwareTechnologyGroup FAIR Principles for Research Software Anna-Lena Lamprecht(@al_lamprecht) FAIR Software@ NationaleScience Symposium, Amsterdam,21November 2019
  2. 2. Towards FAIR Principles for Research Software Anna-LenaLamprecht,Leyla Garcia,Mateusz Kuzak,Carlos Martinez, RicardoArcila,Eva Martin Del Pico,Victoria Dominguez Del Angel, Stephanie van de Sandt, Jon Ison, PaulaAndrea Martinez, PeterMcQuilton, Alfonso Valencia,Jennifer Harrow, FotisPsomopoulos,Josep Ll Gelpi, Neil Chue Hong, Carole Goble, Salvador Capella- Gutierrez DOI: 10.3233/DS-190026 IOS Press DataScience Journal, Pre-Press Published 13 November 2019
  3. 3. FAIR Findable Accessible Interoperable Reusable 2016: "The FAIRguiding principlesfor scientific data managementand stewardship" (Wilkinson et al., doi:10.1038/sdata.2016.18) 2018: "Central to the realization of FAIR are FAIR Digital Objects, which may representdata,softwareor other researchresources."(European Commission) But: Howdo the FAIR Principlesrelate to software? Mismatch between the broad intentionsof the 4 foundational FAIR principlesand how the 15 FAIR GuidingPrinciplesare communicatedand perceived.
  4. 4. FAIR and software: an ongoing discussion This session! Five recommendations for FAIR software" at NL- RSE 2019 “FAIR principles for Software” at 2019 Workshop on Sustainable Software Sustainability (WOSSS19) “FAIR Software” Birds of a Feather meeting at deRSE 2019 Top 10 FAIR Data & Software Global Sprint, including “10 easy things to make your software FAIR” “Sharing Your Software – What is FAIR?” at the 2018 American Geophysical Union (AGU) Fall Meeting “FAIRness assessment for software” at the ELIXIR 2018 BioHackathon “Making Software FAIR” at the DTL Communities@Work 2018 Conference TIB Training workshops on FAIR Data and Software “Applying FAIR Principles to Software” at the 2017 Workshop on Sustainable Software Sustainability (WOSSS17) CodeMeta Workshop 2016 on The Future of Software Metadata ...
  5. 5. Software is (not) data Data (01101010...) Information Instructions/ Software ...
  6. 6. Software is (not) data Data (01101010...) Information Instructions/ Software ... Digital Object Data Software ... VS.
  7. 7. Research software Research software is "softwarethat is used to generate, processor analyze resultsthat youintendto appear in a publication"(Hettrick et al., 2014). Many forms. Many purposes. Many distribution channels. Traditionally, often createdas Free and/or Open Source Software (FOSS).
  8. 8. FAIR and FOSS Clear overlap of objectives, but not the same. FOSS:Open source code, open licenses. FAIR:Open data not a requirement. Due to, e.g., privacy and sensitivity concerns with patients' health records. Not in the same way valid for research software. There is even a demand to make methods available! Should FAIR software require FOSS? Ongoing discussion ...
  9. 9. FAIR and software quality Software quality is a major concern in RSE. Can FAIR meet the expectations? Distinguish between formand function ofsoftware: Quality of the formof software can be coveredby FAIR (code quality, maintainability). Quality of the functionality of software goes beyond FAIR (functionalcorrectness, software security, computationalefficiency).
  10. 10. FAIR applied to research software FAIRfordata FAIRforsoftware Operation F1 (Meta)dataare assigned a globally unique and persistent identifier. Softwareand its associated metadata havea global, unique and persistent identifier for each released version. Rephrased F2 Data are described with rich metadata. Softwareis described with rich metadata. Rephrased F3 Metadataclearly and explicitly include the identifier of the data it describes. Metadataclearly and explicitly include identifiers for all the versions of the software it describes. Rephrased and extended F4 (Meta)dataare registered or indexed in a searchable resource. Softwareand its associated metadata are included in a searchable software registry. Rephrased
  11. 11. FAIRfordata FAIRforsoftware Operation A1 (Meta)dataare retrievableby their identifier using a standardized communications protocol. Softwareand its associated metadata are accessible by their identifier using a standardizedcommunicationsprotocol. Rephrased A1.1 The protocol is open, free, and universally implementable. The protocol is open, free, and universally implementable. Remain the same A1.2 The protocol allows for an authenticationandauthorization procedure, where necessary. The protocol allows for an authenticationand authorizationprocedure, where necessary. Remain the same A2 Metadataare accessible, even when the data are no longer available. Softwaremetadata are accessible, even when the software is no longer available. Rephrased
  12. 12. FAIR applied to research software: I FAIRfordata FAIRforsoftware Operation I1 (Meta)datause a formal, accessible, shared, and broadly applicablelanguagefor knowledge representation. Softwareand its associated metadata use a formal, accessible, shared and broadly applicablelanguageto facilitatemachine readability and data exchange. Rephrased and extended I2 (Meta)datause vocabulariesthat follow FAIR principles. – Reinterpreted, extended and split I2S.1 – Softwareand its associated metadata are formally described using controlled vocabulariesthat follow the FAIR principles. Reinterpreted, extended and split I2S.2 – Softwareuse and produce data in types and formats that are formally described using controlled vocabularies that follow the FAIR principles. Reinterpreted, extended and split I3 (Meta)datainclude qualified references to other (meta)data. – Discarded I4S – Softwaredependencies are documented and mechanisms to access them exist. Newly proposed
  13. 13. FAIRfordata FAIRforsoftware Operation R1 (Meta)dataare richly described with a plurality of accurate and relevant attributes. Softwareand its associated metadata arerichly described with a plurality of accurate and relevantattributes. Rephrased R1.1 (Meta)dataare released with a clear and accessible data usage license. Softwareand its associated metadata have independent, clear and accessible usage licenses compatiblewith the software dependencies. Rephrased and extended R1.2 (Meta)dataare associated with detailedprovenance. Softwaremetadata include detailed provenance, detaillevel should be community agreed. Rephrased R1.3 (Meta)datameet domain-relevant community standards. Softwaremetadata and documentationmeet domain-relevantcommunity standards. Rephrased
  14. 14. More in the paper Examples of tooling that enables F, A, I, R software. Detailed discussion of the challenges around interoperability. Exemplary FAIRness assessment of two bioinformaticstools (Fastme and ChIPseeker). ...
  15. 15. What's next? Further discussion! Agreement on definiteFAIR software principles. Governance model for the FAIR principles. Metrics and maturity models for FAIR software. Agreements on the expected degrees of FAIRness.
  16. 16. Acknowledgments We are grateful to the numerous people who contributedto the discussions aroundFAIR researchsoftwareat differentoccasions preceding the work on this paper. Making no claims to completeness, these include Peter Doorn, Michel Dumontier, Chris Erdmann, José María Fernández, Rafael C. Jimenez, Katrin Leinweber, Jason Maassen, Mustapha Mokrane, Jurriaan Spaaks, Mark Wilkinson and Amrapali Zaveri. We also thank the Japan BioHackathon for sponsoring a FAIR software related project for the 2018 edition. Furthermore, we would like to thank Stian Soiland-Reyes for his valuable comments on earlier versions of this manuscript. NCH and CAG were supported by EP/N006410/1 and EP/S021779/1 for the UK Software Sustainability Institute. AV, JLG, SCG and CAG were supported by ELIXIR-EXCELERATE 676559. EM, AV, JLG and SCG has been additionally supported by PT17/0009/0001. EM and SCG has been additionally supported by IMI2 FAIRplus 802750.
  17. 17. Thank you!

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