How does a scientist know whether an available data set can support their work? This study examines how researchers from different disciplines and practical contexts (e.g., graduate students, faculty researchers, federal research scientists) use information about the sources and analysis of data, also known as provenance, when presented with indicators in an online system, addressing the research question: Can coupling climate-related indicators with data provenance support scientific innovation and science translation?
This study draws on web credibility research as well as boundary object theory, which focuses on the role of artifacts (such as images) in translation and communication across the boundaries of social groups, as a theoretical lens to inform and direct our inquiry.
In this pilot study, we are examining the way that such artifacts can support innovation and translation in the National Climate Indicator System (NCIS). Through a multi-stage research design, we hope to discover principles for optimizing the presentation of data for scientific advancements and translation. Packaging an integrated data product (indicators) with its provenance seems a valuable strategy for improving the ability of researchers to creatively consider the utility of data and information from other domains for use in their own work.
This research is a collaboration with Dr. Melissa Kenney of the Earth System Science Interdisciplinary Center, supported by a seed grant from the UMD ADVANCE Program for Inclusive Excellence (NSF award HRD 1008117).