Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data
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Abstract (arXiv)
Nanopublications are a Linked Data format for scholarly data publishing that has received considerable uptake in the last few years. In contrast to the common Linked Data publishing practice, nanopublications work at the granular level of atomic information snippets and provide a consistent container format to attach provenance and metadata at this atomic level. While the nanopublications format is domain-independent, the datasets that have become available in this format are mostly from Life Science domains, including data about diseases, genes, proteins, drugs, biological pathways, and biotic interactions. More than 10 million such nanopublications have been published, which now form a valuable resource for studies on the domain level of the given Life Science domains as well as on the more technical levels of provenance modeling and heterogeneous Linked Data. We provide here an overview of this combined nanopublication dataset, show the results of some overarching analyses, and describe how it can be accessed and queried.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 2.
4 papers.
- 2021A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarkscited by 11
- 2024Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media postscited by 1
- 2026Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledgecited by 1
- 2026SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptionscited by 0
- 2026SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions30 Jul 2026
- 2026Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge11 May 2026
- 2024Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts8 Feb 2024
- 2021A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks4 Oct 2021