Provenance is important throughout Library and Information Science and is particularly important for the information infrastructures which support the computational aspects of the natural sciences. This is highlighted by the prominence of provenance as a plank in the FAIR principles for data stewardship (principle R1.2). While traditionally focused on the history/lineage of physical objects, provenance is now commonly accepted to apply to digital objects such as the results of computation as well as to the recipes for computing; in the case of recipes this prospective provenance is critical for reproducibility. This dissertation begins with background in provenance pertaining to data curation and computational reproducibility. The second part describes attempts to “FAIRify” the reporting and execution of workflows within a domain of natural science for better data stewardship to support data reusability. The next chapters argue that there remains a gap in our ability to fully document provenance as there are more story-telling tenses than just the past (retrospective) and future (prospective). There is also the subjunctive (conditional) and perhaps many others. Supporting new flavors of provenance requires new modeling constructs. The thesis concludes with novel information modeling techniques which exploit reification of sub-class relationships suitable for modeling these many sub-classes of provenance, as well as other domains.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo termsThe student, Michael Gryk, accepted the attached license on 2024-11-24 at 13:30.The student, Michael Gryk, submitted this Dissertation for approval on 2024-11-24 at 15:24.This Dissertation was approved for publication on 2024-12-01 at 13:12.DSpace SAF Submission Ingestion Package generated from Vireo submission #21378 on 2025-03-28 at 14:26:0
