Data for: Coordination and persistence of aggressive visual communication in Siamese fighting fish

Animals coordinate their behavior with each other during both cooperative and agonistic social interactions. Such coordination often adopts the form of “turn-taking”, in which the interactive partners alternate the performance of a behavior. Apart from acoustic communication, how turn-taking between animals is coordinated is not well understood. Furthermore, the neural substrates that regulate persistence in engaging in social interactions are poorly studied. Here, we use Siamese fighting fish (Betta splendens) to study visually driven turn-taking aggressive behavior. Using encounters with conspecifics and with animations, we characterize the dynamic visual features of an opponent and the behavioral sequences that drive turn-taking. Through a brain-wide screen of neuronal activity during coordinated and persistent aggressive behavior, followed by targeted brain lesions, we find that the caudal portion of the dorsomedial telencephalon, an amygdala-like region, promotes persistent partici..., Videos were taken at 2 angles (top, side) with Raspberry Pi cameras in .h264 format at 40 fps and converted to .mp4 and cropped to 500 x 500 pixel dimensions. Videos were then fed either into an automated scoring pipeline (DeepLabCut/OpenCV/feature extraction/tcn) or manually scored. Keypoints or binary arrays (1=flare, 0 = noflare) were then used to extract features of shape and motion and to measure flaring synchrony or overall proportion of time flaring. Data and scripts for analysis and visualization are categorized by their corresponding figure(s) in Everett et al., , # Data for: Coordination and persistence of aggressive visual communication in Siamese fighting fish

Dryad dataset DOI: https://doi.org/10.5061/dryad.7wm37pw2w

Primary article DOI: 10.1016/j.celrep.2024.115208

## Description of the data and file structure

The data and code used to generate the main Figures 1-6 of the Everett et al. are included in the Dryad repository. Behavioral data (Figures 1-6) were collected using Raspberry Pi cameras, and both manual scoring and semi-supervised behavior segmentation were employed to quantify aggression and identify the visual cues driving aggressive behavior. pS6 experiments were conducted to identify areas of differential neural activity following aggressive encounters, with the results presented in Figure 6.

## File types and how to open (Usage Notes):

*TIP: all .h5, .npy, and .csv files can be viewed and variables summarized by dragging the file into ChatGPT ([https://chatgpt.com/](https://chatgpt.com/)) prompt

Zip files: compressed dire...,