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Towards Reproducible Research in a Biomedical Collaboration Platform following the FAIR Guiding Principles

Published: 05 December 2017 Publication History

Abstract

Replication of computational experiments is essential for verifiable research. However, it requires a comprehensive and unambiguous description of all employed digital artifacts, in particular data, code and the computational environment. Recently, the FAIR Guiding Principles have been published to support reproducible research. In this paper, a cloud-based biomedical collaboration platform has been evaluated regarding FAIR principles and has been extended to support reproducibility. The FAICE suite is presented, encompassing tools to thoroughly describe and reproduce a computational experiment within the original execution environment as well as within a dynamically configured VM.

References

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Peter Amstutz, Michael R. Crusoe, Nebojusa Tijaniç, Brad Chapman, John Chilton, Michael Heuer, Andrey Kartashov, Dan Leehr, Hervé Ménager, Maya Nedeljkovich, Matt Scales, Stian Soiland-Reyes, and Luka Stojanovic. 2016. Common Workflow Language, v1.0. (July. 2016).
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Nick Barnes, D Jones, P Norvig, C Neylon, R Pollock, J Jackson, V Stodden, and P Suber. 2013. Science code manifesto. (2013). https://web.archive.org/web/20160218093215/http://sciencecodemanifesto.org
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Amir Bashan, Ronny P. Bartsch, Jan W. Kantelhardt, Shlomo Havlin, and Plamen Ch Ivanov. 2012. Network physiology reveals relations between network topology and physiological function. Nature Communications Vol. 3 (2012), 702.

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  • (2024)FAIRe Gesundheitsdaten im nationalen und internationalen DatenraumFAIR health data in the national and international data spaceBundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz10.1007/s00103-024-03884-867:6(710-720)Online publication date: 15-May-2024
  • (2020)Towards a More Reproducible Biomedical Research Environment: Endorsement and Adoption of the FAIR PrinciplesBiomedical Engineering Systems and Technologies10.1007/978-3-030-46970-2_22(453-470)Online publication date: 6-May-2020
  • (2019)Reproducibility and Performance of Deep Learning Applications for Cancer Detection in Pathological Images2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)10.1109/CCGRID.2019.00080(621-630)Online publication date: May-2019

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  1. Towards Reproducible Research in a Biomedical Collaboration Platform following the FAIR Guiding Principles

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    cover image ACM Conferences
    UCC '17 Companion: Companion Proceedings of the10th International Conference on Utility and Cloud Computing
    December 2017
    252 pages
    ISBN:9781450351959
    DOI:10.1145/3147234
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 05 December 2017

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    Author Tags

    1. cloud computing
    2. docker
    3. medical data
    4. repeatability
    5. reproducibility
    6. xnat

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    • Bundesministerium für Bildung und Forschung
    • Bundesministerium für Wirtschaft und Energie
    • Deutsche Forschungsgemeinschaft

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    UCC '17
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    Overall Acceptance Rate 38 of 125 submissions, 30%

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    • (2024)FAIRe Gesundheitsdaten im nationalen und internationalen DatenraumFAIR health data in the national and international data spaceBundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz10.1007/s00103-024-03884-867:6(710-720)Online publication date: 15-May-2024
    • (2020)Towards a More Reproducible Biomedical Research Environment: Endorsement and Adoption of the FAIR PrinciplesBiomedical Engineering Systems and Technologies10.1007/978-3-030-46970-2_22(453-470)Online publication date: 6-May-2020
    • (2019)Reproducibility and Performance of Deep Learning Applications for Cancer Detection in Pathological Images2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)10.1109/CCGRID.2019.00080(621-630)Online publication date: May-2019

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