Revealing the Detailed Lineage of Script Outputs Using Hybrid Provenance

Qian Zhang, Yang Cao, Qiwen Wang, Duc Vu, Priyaa Thavasimani, Timothy McPhillips, Paolo Missier, Peter Slaughter, Christopher Jones, Mathew B. Jones, Bertram Ludäscher
2018 International Journal of Digital Curation  
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
more » ... e 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.
doi:10.2218/ijdc.v12i2.585 fatcat:a77rphjemjfi5di4broibpe2pu