Revealing the Detailed Lineage of Script Outputs Using Hybrid Provenance

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Bibliographische Detailangaben
Beteiligte: Zhang, Qian, Cao, Yang, Wang, Qiwen, Vu, Duc, Thavasimani, Priyaa, McPhillips, Timothy, Missier, Paolo, Slaughter, Peter, Jones, Christopher, Jones, Matthew B., Ludäscher, Bertram
In: International Journal of Digital Curation, 12, 2018, 2, S. 390-408
veröffentlicht:
Edinburgh University Library
Medientyp: Artikel, E-Artikel

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Umfang: 390-408
ISSN: 1746-8256
DOI: 10.2218/ijdc.v12i2.585
veröffentlicht in: International Journal of Digital Curation
Sprache: Unbestimmt
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Kollektion: Edinburgh University Library (CrossRef)
Inhaltsangabe

<jats:p>We illustrate how combining retrospective and prospectiveprovenance can yield scientifically meaningful hybrid provenancerepresentations of the computational histories of data produced during a script run. We use scripts from multiple disciplines (astrophysics, climate science, biodiversity data curation, and social network analysis), implemented in Python, R, and MATLAB, to highlight the usefulness of diverse forms of retrospectiveprovenance when coupled with prospectiveprovenance. Users provide prospective provenance, i.e., the conceptual workflows latent in scripts, via simple YesWorkflow annotations, embedded as script comments. Runtime observables can be linked to prospective provenance via relational views and queries. These observables could be found hidden in filenames or folder structures, be recorded in log files, or they can be automatically captured using tools such as noWorkflow or the DataONE RunManagers. The YesWorkflow toolkit, example scripts, and demonstration code are available via an open source repository.</jats:p>