Bridging the Scientific Knowledge Gap and Reproducibility: A Survey of Provenance, Assertion and Evidence Ontologies
The rapid growth of scientific publications and evolving experimental paradigms create significant challenges in staying up-to-date with current advances. Assertions are often unstructured and have limited provenance, which hinders reproducibility. Ontologies and knowledge graphs (KGs) offer structured solutions by capturing assertions, evidence, and provenance to support reproducibility. This paper reviews 23 ontologies - 13 focused on assertions and evidence and 10 on provenance - providing an overview of the current landscape while highlighting key challenges and opportunities for improvement.
Authors
- Dorota Jarecka (ORCID: https://orcid.org/0000-0001-8282-2988)
- Puja Trivedi
- Satrajit Ghosh (ORCID: https://orcid.org/0000-0002-5312-6729)
- Tek Raj Chhetri (ORCID: https://orcid.org/0000-0002-3905-7878)
- Yaroslav O. Halchenko (ORCID: https://orcid.org/0000-0003-3456-2493)
- Lydia Ng (ORCID: https://orcid.org/0000-0002-7499-3514)
- Patrick L. Ray (ORCID: https://orcid.org/0000-0002-5260-9315)
Institutions
- Dartmouth College (US)
- Allen Institute for Brain Science (US)
- McGovern Institute for Brain Research (US)
- Allen Institute (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1145/3701716.3715483
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universitas Brawijaya
- National Institutes of Health