# Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data

arXiv: https://arxiv.org/abs/1809.06532 · HTML: https://mlsanity.com/paper/1809.06532/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks](https://mlsanity.com/paper/2110.01434/) (2021)
- [Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts](https://mlsanity.com/paper/2402.05536/) (2024)
- [Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge](https://mlsanity.com/paper/2605.10370/) (2026)
- [SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions](https://mlsanity.com/paper/2607.27955/) (2026)

## 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.