ArticleScientific data2026
A dataset of fine-grained zebrafish interactions in health and disease.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A dataset of fine-grained zebrafish interactions in health and disease.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Zebrafish (Danio rerio) has emerged as a valuable vertebrate model organism for studies of social interactions. While previous multiple-animal research has focused on gross movement, here we present a machine vision workflow to capture and analyze fine-grained social interactions through the tracking of three anatomical landmarks (at the head, pectoral fins, and tail) as well as the identity of the fish in 3D. We release a dataset of N = 173 five-hour recordings of adult zebrafish dyads, including male/male and female/female wild-type pairs and disease-model mutants, sampling complex behaviors such as dominance contests and aggressive/submissive motifs. The recordings are of high temporal resolution (fs = 140 Hz) from a large imaging volume ~ 10 body lengths per linear dimension, and include both square and cylindrical arenas. This dataset offers a critical resource for biologists seeking to understand the neural basis of social behavior, for machine learning researchers working to improve posture tracking, and for the broader quantitative understanding of natural behavior.
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.