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Toward Long-Term and Archivable Reproducibility.

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Title: Toward Long-Term and Archivable Reproducibility.
Authors: Akhlaghi, Mohammad1 (AUTHOR) mohammad@akhlaghi.org, Infante-Sainz, Raul2 (AUTHOR) infantesainz@gmail.com, Roukema, Boudewijn F.3 (AUTHOR) boud@astro.uni.torun.pl, Khellat, Mohammadreza4 (AUTHOR) mkhellat@ideal-information.com, Valls-Gabaud, David5 (AUTHOR) david.valls-gabaud@observatoiredeparis.psl.eu, Baena-Galle, Roberto6 (AUTHOR) roberto.baena@unir.net, A. Barba, Lorena (AUTHOR) labarba@gwu.edu, Gesing, Sandra (AUTHOR) sandra.gesing@nd.edu
Source: Computing in Science & Engineering. May/Jun2021, Vol. 23 Issue 3, p82-91. 10p.
Subjects: IEEE Computer Society, Freeware (Computer software), Digital libraries, Modular design, Computer systems, Minimal design
Abstract: Analysis pipelines commonly use high-level technologies that are popular when created, but are unlikely to be readable, executable, or sustainable in the long term. A set of criteria is introduced to address this problem: completeness (no execution requirement beyond a minimal Unix-like operating system, no administrator privileges, no network connection, and storage primarily in plain text); modular design; minimal complexity; scalability; verifiable inputs and outputs; version control; linking analysis with narrative; and free and open-source software. As a proof of concept, we introduce "Maneage" (managing data lineage), enabling cheap archiving, provenance extraction, and peer verification that has been tested in several research publications. We show that longevity is a realistic requirement that does not sacrifice immediate or short-term reproducibility. The caveats (with proposed solutions) are then discussed and we conclude with the benefits for the various stakeholders. This article is itself a Maneage'd project (project commit 313db0b). Appendices—Two comprehensive appendices that review the longevity of existing solutions are available as supplementary "Web extras," which are available in the IEEE Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/MCSE.2021.3072860. Reproducibility—All products available in zenodo.4913277, the Git history of this paper's source is at git.maneage.org/paper-concept.git, which is also archived in Software Heritage Heritage: swh:1:dir:33fea87068c1612daf011f161b97787b9a0df39f. Clicking on the SWHIDs in the digital format will provide more "context" for same content. [ABSTRACT FROM AUTHOR]
Copyright of Computing in Science & Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Toward Long-Term and Archivable Reproducibility.
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  Data: <searchLink fieldCode="AR" term="%22Akhlaghi%2C+Mohammad%22">Akhlaghi, Mohammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohammad@akhlaghi.org</i><br /><searchLink fieldCode="AR" term="%22Infante-Sainz%2C+Raul%22">Infante-Sainz, Raul</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> infantesainz@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Roukema%2C+Boudewijn+F%2E%22">Roukema, Boudewijn F.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> boud@astro.uni.torun.pl</i><br /><searchLink fieldCode="AR" term="%22Khellat%2C+Mohammadreza%22">Khellat, Mohammadreza</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> mkhellat@ideal-information.com</i><br /><searchLink fieldCode="AR" term="%22Valls-Gabaud%2C+David%22">Valls-Gabaud, David</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> david.valls-gabaud@observatoiredeparis.psl.eu</i><br /><searchLink fieldCode="AR" term="%22Baena-Galle%2C+Roberto%22">Baena-Galle, Roberto</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> roberto.baena@unir.net</i><br /><searchLink fieldCode="AR" term="%22A%2E+Barba%2C+Lorena%22">A. Barba, Lorena</searchLink> (AUTHOR)<i> labarba@gwu.edu</i><br /><searchLink fieldCode="AR" term="%22Gesing%2C+Sandra%22">Gesing, Sandra</searchLink> (AUTHOR)<i> sandra.gesing@nd.edu</i>
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  Data: Analysis pipelines commonly use high-level technologies that are popular when created, but are unlikely to be readable, executable, or sustainable in the long term. A set of criteria is introduced to address this problem: completeness (no execution requirement beyond a minimal Unix-like operating system, no administrator privileges, no network connection, and storage primarily in plain text); modular design; minimal complexity; scalability; verifiable inputs and outputs; version control; linking analysis with narrative; and free and open-source software. As a proof of concept, we introduce "Maneage" (managing data lineage), enabling cheap archiving, provenance extraction, and peer verification that has been tested in several research publications. We show that longevity is a realistic requirement that does not sacrifice immediate or short-term reproducibility. The caveats (with proposed solutions) are then discussed and we conclude with the benefits for the various stakeholders. This article is itself a Maneage'd project (project commit 313db0b). Appendices—Two comprehensive appendices that review the longevity of existing solutions are available as supplementary "Web extras," which are available in the IEEE Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/MCSE.2021.3072860. Reproducibility—All products available in zenodo.4913277, the Git history of this paper's source is at git.maneage.org/paper-concept.git, which is also archived in Software Heritage Heritage: swh:1:dir:33fea87068c1612daf011f161b97787b9a0df39f. Clicking on the SWHIDs in the digital format will provide more "context" for same content. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computing in Science & Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May/Jun2021
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