  The way in which data are shared can affect their utility and reusability.
Here, we demonstrate how data that we had previously shared in bulk can be
mobilized further through a knowledge graph that allows for much more granular
exploration and interrogation. The original dataset is about the computational
reproducibility of GitHub-hosted Jupyter notebooks associated with biomedical
publications. It contains rich metadata about the publications, associated
GitHub repositories and Jupyter notebooks, and the notebooks' reproducibility.
We took this dataset, converted it into semantic triples and loaded these into
a triple store to create a knowledge graph, FAIR Jupyter, that we made
accessible via a web service. This enables granular data exploration and
analysis through queries that can be tailored to specific use cases. Such
queries may provide details about any of the variables from the original
dataset, highlight relationships between them or combine some of the graph's
content with materials from corresponding external resources. We provide a
collection of example queries addressing a range of use cases in research and
education. We also outline how sets of such queries can be used to profile
specific content types, either individually or by class. We conclude by
discussing how such a semantically enhanced sharing of complex datasets can
both enhance their FAIRness, i.e., their findability, accessibility,
interoperability, and reusability, and help identify and communicate best
practices, particularly with regards to data quality, standardization,
automation and reproducibility.
