FAIR and Interactive Data Graphics from a Scientific Knowledge Graph
AuthorDeagen, Michael E.; McCusker, Jamie; Fateye, Tolulomo; Stouffer, Samuel; Brinson, L. Cate; McGuinness, Deborah L.; Schadler, Linda S.
Full CitationDeagen, Michael E., Jamie P. McCusker, Tolulomo Fateye, Samuel Stouffer, L. Cate Brinson, Deborah L. McGuinness, and Linda S. Schadler. "FAIR and Interactive Data Graphics from a Scientific Knowledge Graph." Scientific Data 9, no.1, May 2022: 1-11.
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URIhttps://www.nature.com/articles/s41597-022-01352-z; https://doi.org/10.1038/s41597-022-01352-z; https://hdl.handle.net/20.500.13015/6436
AbstractGraph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.;
PublisherNature Scientific Data
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