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FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset

Authors Sheeba Samuel , Daniel Mietchen

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Author Details

Sheeba Samuel
  • Distributed and Self-organizing Systems, Chemnitz University of Technology, Chemnitz, Germany
Daniel Mietchen
  • FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany
  • Institute for Globally Distributed Open Research and Education (IGDORE)

Funding

  • Samuel, Sheeba: German Research Foundation (DFG), TRR-386, project number 514664767
  • Mietchen, Daniel: MaRDI: DFG 460135501, BASE4NFDI/KGI4NFDI: DFG 521453681

Acknowledgements

We thank the providers of infrastructure, data, and code that we used in this study. These include the PubMed Central repository at the U.S. National Center for Biotechnology Information and the Ara Cluster at the University of Jena as well as the Jupyter, Python and Conda communities and their respective dependencies. The authors gratefully acknowledge the computing time made available to them on the high-performance computer at the NHR Center of TU Dresden. This center is jointly supported by the Federal Ministry of Education and Research and the state governments participating in the NHR (www.nhr-verein.de/unsere-partner). Special thanks go to JupyterCon, which provided the nucleus for our collaboration. We also thank Ramy-Badr Ahmed and Moritz Schubotz for help with registering the GitHub repositories from our corpus in the Software Heritage archive.

Cite As Get BibTex

Sheeba Samuel and Daniel Mietchen. FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 4:1-4:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/TGDK.2.2.4

Abstract

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.

Subject Classification

ACM Subject Classification
  • Information systems → Entity relationship models
  • Information systems → Information extraction
Keywords
  • Knowledge Graph
  • Computational reproducibility
  • Jupyter notebooks
  • FAIR data
  • PubMed Central
  • GitHub
  • Python
  • SPARQL

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