Data from: Fish communicate with water flow to enhance a school's social network

McHenry, Matthew1

Published Sep 04, 2024 on Dryad. https://doi.org/10.5061/dryad.ngf1vhj2g

Data files

Sep 04, 2024 version files 4.02 GB

Abstract

Code for running schooling experiments and analyzing the data. Here we have developed python code for controlling devices during the running of experiments and the acquisition of kinematic data from the video recordings. In addition, MATLAB code was developed for the analysis of schooling kinematics.

README: Data from: Fish communicate with water flow to enhance a school's social network

See the README.md file in the "running_experiment" archive for details on the acquisition of data.

The analysis was coded in matlab. The m-file wk_main.m controls the workflow and describes the data analysis. All other m-files supplement that analysis.

General notes

Some fields have entries of NaN, which means "not a number" and serves as a placeholder when values are not available.
Some fields have entries of "n/a", which means "not applicable". For example, were performed recordings of spatial calibrations that have no fish and thus entered "n/a" for fish_num, the number of fish in the recording.

data

holds the datasets used in the study.

Files

compiled_data.csv: provides the mean and variance values for a variety of measurements that served as dependent variables in our statistical analysis.
experiment_log.csv: itemizes parameters required for the kinematic analysis of each experiment. The rows where sch_num==999 corresponded to the calibration sequences and thus some of the columns are entered as n/a if those measurements did not apply.
recording_log: Generated by our python code for all video recordings performed. Here too sch_num==999 corresponded to the calibration sequences.

Folders

masks: Contains the images used to mask video files, along with data (in .mat and .npy formats) that detail the positions of masking pixels.
matlab: holds all kinematic data for schooling analysis. Requires MATLAB to load.
mean_images: images created by temporal averges of pixel intensity across recordings. Used to subtrack background features from video frames.

data_acquisition_code

Holds all software used for running experiments and acquiring kinematics. This consists primarily of Python scripts that are explained in the README.md file in this folder. The code for running experiments is organized by running scripts from run_acq_kinematics.py and the analysis code in organized in run_analysis.py.

data_analysis_code

Holds all MATLAB code for performing the kinematic analysis. The code is organized by running scripts from wk_main.m in MATLAB. The major components of the analysis are described in the README.md file in this folder.