STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications
Our text mining tool, STEED, can extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. This enables the tool's deployment for probing a field in a research improvement context or replacing one human reader during data extraction, resulting in substantial time savings and contributing towards the automation of systematic reviews.
Authors
- Julia Bugajska
- Benjamin Victor Ineichen (ORCID: https://orcid.org/0000-0003-1362-4819)
- Daniel S. Reich (ORCID: https://orcid.org/0000-0002-2628-4334)
- Bernard Friedrich Hild
- Marianna Rosso (ORCID: https://orcid.org/0000-0002-1236-4085)
- David Brüschweiler (ORCID: https://orcid.org/0009-0009-1476-3619)
- Wolfgang Emanuel Zürrer
- Ewoud Ewing
- Amelia Elaine Cannon
Institutions
- National Institutes of Health (US)
- Karolinska University Hospital (SE)
- University of Zurich (CH)
- Karolinska Institutet (SE)
- National Institute of Neurological Disorders and Stroke (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1371/journal.pone.0311358
- Primary Topic
- Viral Infectious Diseases and Gene Expression in Insects
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Universität Zürich
- National Institute of Neurological Disorders and Stroke