ArticleScientific data2025
Reusability challenges of livestock production data to improve animal health.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Reusability challenges of livestock production data to improve animal health.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In veterinary epidemiology, using data routinely generated by stakeholders of the livestock production chains offers an opportunity for researchers to access a large amount of information that could be used to improve animal health. However, (re)using these non-scholarly data doesn't come without challenges. This study assesses the reusability for research purposes of 30 European datasets generated by the livestock sector to meet legislative or operational needs. Information about each dataset was collected through a questionnaire survey filled by the data owner or the data user (researchers). Datasets were described, and their compliance with the FAIR principles, a data-sharing standard, and the principle of accountability defined in the General Data Protection Regulation were assessed. The study highlighted major gaps in terms of compliance with data regulations and implementation of good data management practices, specifically considering the rare use of metadata and standard vocabularies. Filling these gaps is essential to reap the full benefits offered by the rapidly growing volume of heterogeneous data available in livestock production systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.