Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Abstract

A comprehensive record of research data provenance is essential for the successful curation, management, and reuse of data over time. However, creating such detailed metadata can be onerous, and there are few structured methods for doing so. In this case study of data curation in support of geobiology research conducted at Yellowstone National Park, we describe a method of "Research Process Modeling" for documenting noncomputational data provenance in a structured yet flexible way. The method combines systems analysis techniques to model research activities, the World Wide Web Consortium Provenance (PROV) ontology to illustrate relationships between data products, and simple inventory methods to account for research processes and data products. It also supports collaborative data curation between information professionals and researchers, and is therefore a significant step toward producing more useable and interpretable research data. We demonstrate how this method describes data provenance more robustly than "flat" metadata alone and fills a critical gap in the documentation of provenance for field-based and noncomputational workflows. We discuss potential applications of this approach to other research domains.

Physiology, Metabolism, and Fossilization of Hot-Spring Filamentous Microbial Mats

“…Detailed description of all protocols and techniques used for field collection, biomolecule extractions, and meta-omic analyses are presented in the Supplementary Information and briefly summarized here. Detailed descriptions of the experimental design and metadata curation strategies adopted for all aspects of the field and laboratory analyses in the present study are presented in the works of Palmer et al (2017) and Thomer et al (2018).…”

Softcite dataset: A dataset of software mentions in biomedical and economic research publications

“…Further details of annotation, training, alignment, and curation are documented later in this paper in detail sufficient to understand the provenance of the dataset (Gebru et al, 2018; Thomer, Wickett, Baker, Fouke, & Palmer, 2018).…”

Adapting research process models for the design of knowledge engineering applications

“…In applying RPM, we made adaptations to the information collection and description tools. Thomer et al (2018) note that their intention was to focus on process flow, while ours is to highlight information flow, particularly for processes involving data interpretation. Thus, we chose to add many more artifacts to our activity diagram, to emphasize the role of outside information in the analysis and interpretation processes.…”