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<p>Zebrafish (<i>Danio rerio</i>) 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 <i>N</i>&#xa0;=&#xa0;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 (<i>f</i><i>s</i>&#xa0;=&#xa0;140 Hz) from a large imaging volume &#xa0;~&#xa0;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.</p>

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A dataset of fine-grained zebrafish interactions in health and disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Kosmas Deligkaris,
  • Radmila Neiman,
  • Makoto Hiroi,
  • Tatsuo Izawa,
  • Liam O’Shaughnessy,
  • Luis Carretero Rodriguez,
  • Ichiro Masai,
  • Greg J. Stephens

摘要

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.