oliveirabruno01/openfarm-zoo-valence-arousal
OpenFARM Zoo Valence Arousal This prepared dataset derives from the Figshare collection Emotion recognition study, associated with Hiisivuori et al. (2025), Human recognition of emotional valence and arousal of zoo animals. Source: https://doi.org/10.6084/m9.figshare.c.7807931 Article: https://www.nature.com/articles/s41598-025-28646-7 Article DOI: 10.1038/s41598-025-28646-7 Source license: CC BY 4.0 according to the Figshare articles Prepared: 2026-05-18 Scope… See the full description on the dataset page: https://huggingface.co/datasets/oliveirabruno01/openfarm-zoo-valence-arousal.
OpenFARM Zoo Valence Arousal
This prepared dataset derives from the Figshare collection Emotion recognition study, associated with Hiisivuori et al. (2025), Human recognition of emotional valence and arousal of zoo animals.
Source: https://doi.org/10.6084/m9.figshare.c.7807931 Article: https://www.nature.com/articles/s41598-025-28646-7 Article DOI: 10.1038/s41598-025-28646-7 Source license: CC BY 4.0 according to the Figshare articles Prepared: 2026-05-18
Scope
This is a tiny eval-only visual affect benchmark. Given a short zoo-animal visual stimulus, predict expert-labeled emotional valence and/or arousal.
- Species counts: {'Siberian tiger': 5, 'Turkmenian markhor': 5, 'Barbary macaque': 5}
- Rows: 15 eval examples
- Split:
testonly - Valence labels: {'negative': 6, 'neutral': 3, 'positive': 6}
- Arousal labels: {'high': 9, 'low': 6}
These are expert emotional-state labels from the source study. They are not clinical pain scores or a complete welfare diagnosis.
Prepared Media
The Figshare source provides three video compilations. Each row here is clipped from the compilation using black-frame separator detection and preserves the full stimulus segment used in the human study: real-speed clip, slow-motion clip, and still-frame portion. The primary video column is muted because the paper describes the study as visual recognition from mute clips.
Each row also includes a compact 3x3 filmstrip image for image-model evaluation.
Audio audit summary: {'activeaudiosecmin': 8.02, 'activeaudiosecmedian': 15.42, 'activeaudiosecmax': 22.13, 'activeaudioratiomedian': 0.481}
Audio-bearing video exported in this dataset build: False. If present, video_with_audio should be treated as an explicit multimodal ablation, not the headline human-comparable benchmark.
Data Shape
Main fields:
filmstrip: HF Image feature with a 3x3 visual summaryvideo: HF Video feature with the muted prepared MP4 stimulusvideo_with_audio: optional HF Video feature, present only whenEXPORT_AUDIO_VARIANT=Truespecies: target animal speciesvalence: expert label, one ofnegative,neutral,positivearousal: expert label, one oflow,highvalence_arousal: combined helper labelduration_sec: prepared stimulus durationsource_url,source_article_url,source_doi,article_doi,license: source attribution fields
Leakage Notes
Public rows use opaque media filenames and intentionally omit clip codes, source filenames, segment timestamps, expert interpretation notes, pre-formatted messages, task ids, and OpenFARM-specific row ids. The environment should build prompts from the media and selected non-target fields only.
Sources
- Figshare collection: https://doi.org/10.6084/m9.figshare.c.7807931
- Article: https://www.nature.com/articles/s41598-025-28646-7
- Article DOI: 10.1038/s41598-025-28646-7
