DOI: 10.5061/dryad.pc866t1vk
title: Gerbil family video and pose tracking files with metadata

description: Understanding animal behavior requires the capability of monitoring and
 quantifying behaviors across many time points and increasingly ethological
 behaviors.  In the past several years, machine vision researchers
 have made significant advances in building animal tracking software
 relying on the development of convolutional neural networks (CNNs) and
 deep learning. However, the lack of longitudinal, curated video datasets
 for machine learning and behavioral science remains a key bottleneck in
 advancing both fields. Here, we present 3 curated multi-animal, multi-day
 rodent behavior datasets containing the behavior of gerbil families (2
 adults and 4 pups) during critical periods of development (P15 to P30).
 Each cohort contains videos acquired at 24 fps for 15 days continuously,
 yielding approximately 30 million frames per cohort. We provide ground
 truth annotations for all animals in 1000 to 6000 frames across the
 cohorts using a standardized Ultralytics pose data format.  We
 additionally provide predictions from using Sleap (Pereira et al, 2022)
 machine vision tracking as a dataset to be used by behavioral scientists
 for analysis and machine vision researchers as a benchmark.
description: The datasets were acquired at New York University between 2019
 and 2020.  The original videos were compressed to lower resolution and
 saved as compressed files. For machine vision output, Sleap (Pereira et al
 2022; https://github.com/talmolab/sleap) was used for annotating, training, and predicting the identity and feature location of each animal. 
description: # Gerbil family video and pose tracking files with metadata Dataset DOI:
 [10.5061/dryad.pc866t1vk](about:blank) **Description of the data and file
 structure** **Files and variables** There are two types of compressed data
 folders, video and tracking. They are each named according to the family
 cohort and postnatal day (P) from from which they were obtained. Each
 compressed video file contains individual files in mp4 format that can be
 viewed with Quicktime or other standard video players. Each compressed
 tracking file contains individual SLEAP (Pereira et al., 2022) files in
 slp format that can be viewed with the sleap-label command-line tool
 ([https://sleap.ai/develop/index.html](https://sleap.ai/develop/index.html)). **File: cohort4_P19_videos.zip** **Description:**  P19 raw video **File: cohort4_P20_videos.zip** **Description:**  P20 raw video **File: cohort4_P21_videos.zip** **Description:**  P21 raw video **File: cohort4_P22_videos.zip** **Description:**  P22 raw video **File: cohort4_P24_videos.zip** **Description:** P24 raw video **File: cohort4_P23_videos.zip** **Description:** P25 raw video **File: cohort4_P25_videos.zip** **Description:** P25 raw video **File: cohort4_P26_videos.zip** **Description:** P26 raw video **File: cohort4_P27_videos.zip** **Description:** P27 raw video **File: cohort4_P30_videos.zip** **Description:** P30 raw video **File: cohort4_P29_videos.zip** **Description:** P29 raw video **File: cohort4_P28_videos.zip** **Description:**  P28 raw video **File: cohort2_P30_tracking.zip** **Description:** P30 SLEAP tracks **File: cohort2_P15_tracking.zip** **Description:**  P15 SLEAP tracks **File: cohort2_P16_tracking.zip** **Description:**  P16 SLEAP tracks **File: cohort2_P18_tracking.zip** **Description:** P18 SLEAP tracks **File: cohort2_P24_tracking.zip** **Description:**  P24 SLEAP tracks **File: cohort2_P19_tracking.zip** **Description:**  P19 SLEAP tracks **File: cohort2_P27_tracking.zip** **Description:**  P27 SLEAP tracks **File: cohort2_P25_tracking.zip** **Description:**  P25 SLEAP tracks **File: cohort2_P26_tracking.zip** **Description:** P26 SLEAP tracks **File: cohort2_P21_tracking.zip** **Description:**  P21 SLEAP tracks **File: cohort2_P20_tracking.zip** **Description:**  P20 SLEAP tracks **File: cohort2_P22_tracking.zip** **Description:**  P22 SLEAP tracks **File: cohort2_P23_tracking.zip** **Description:**  P23 SLEAP tracks **File: cohort2_P28_tracking.zip** **Description:**  P28 SLEAP tracks **File: cohort2_P29_tracking.zip** **Description:**  P29 SLEAP tracks **File: cohort2_P17_tracking.zip** **Description:**  P17 SLEAP tracks **File: cohort3_P15_tracking.zip** **Description:**  P15 SLEAP tracks **File: cohort3_P16_tracking.zip** **Description:**  P16 SLEAP tracks **File: cohort3_P17_tracking.zip** **Description:**  P17 SLEAP tracks **File: cohort3_P18_tracking.zip** **Description:**  P18 SLEAP tracks **File: cohort3_P19_tracking.zip** **Description:**  P19 SLEAP tracks **File: cohort3_P20_tracking.zip** **Description:**  P20 SLEAP tracks **File: cohort3_P21_tracking.zip** **Description:**  P21 SLEAP tracks **File: cohort3_P20_tracking.zip** **Description:**  P20 SLEAP tracks **File: cohort3_P22_tracking.zip** **Description:**  P22 SLEAP tracks **File: cohort3_P23_tracking.zip** **Description:**  P23 SLEAP tracks **File: cohort3_P24_tracking.zip** **Description:**  P24 SLEAP tracks **File: cohort3_P25_tracking.zip** **Description:**  P25 SLEAP tracks **File: cohort3_P26_tracking.zip** **Description:**  P26 SLEAP tracks **File: cohort3_P27_tracking.zip** **Description:**  P27 SLEAP tracks **File: cohort3_P28_tracking.zip** **Description:**  P28 SLEAP tracks **File: cohort3_P29_tracking.zip** **Description:**  P29 SLEAP tracks **File: cohort3_P30_tracking.zip** **Description:**  P30 SLEAP tracks **File: cohort4_P15_tracking.zip** **Description:**  P15 SLEAP tracks **File: cohort4_P16_tracking.zip** **Description:**  P16 SLEAP tracks **File: cohort4_P17_tracking.zip** **Description:**  P17 SLEAP tracks **File: cohort4_P18_tracking.zip** **Description:**  P18 SLEAP tracks **File: cohort4_P19_tracking.zip** **Description:**  P19 SLEAP tracks **File: cohort4_P20_tracking.zip** **Description:**  P20 SLEAP tracks **File: cohort4_P21_tracking.zip** **Description:**  P21 SLEAP tracks **File: cohort4_P20_tracking.zip** **Description:**  P20 SLEAP tracks **File: cohort4_P22_tracking.zip** **Description:**  P22 SLEAP tracks **File: cohort4_P23_tracking.zip** **Description:**  P23 SLEAP tracks **File: cohort4_P24_tracking.zip** **Description:**  P24 SLEAP tracks **File: cohort4_P25_tracking.zip** **Description:**  P25 SLEAP tracks **File: cohort4_P26_tracking.zip** **Description:**  P26 SLEAP tracks **File: cohort4_P27_tracking.zip** **Description:**  P27 SLEAP tracks **File: cohort4_P28_tracking.zip** **Description:**  P28 SLEAP tracks **File: cohort4_P29_tracking.zip** **Description:**  P29 SLEAP tracks **File: cohort4_P30_tracking.zip** **Description:**  P30 SLEAP tracks ### FIgure_metadata_csv.zip #### File: 1D_n_frames.csv Description: Number of video frames analyzed per cohort. Variables: * cohort: Cohort identifier (categorical) * n_frames: Number of frames (frames) #### File: 1D_pup_sex.csv Description: Sex counts of pups per cohort. Variables: * cohort: Cohort identifier (categorical) * n_female: Number of females (count) * n_male: Number of males (count) #### File: 2B_distance.csv Description: Locomotor distance trajectories across development. Variables: * time_index: Post nest-leaving day (days) * dev_day: Day post birth (days) * mean: Mean distance traveled (cm) * sem: Standard error of the mean distance (cm) * significant_change: Statistical significance indicator (boolean/unitless) #### File: 2B_exploration.csv Description: Exploration trajectories across development. Variables: * time_index: Post nest-leaving day (days) * dev_day: Day post birth (days) * mean: Mean exploration index (unitless) * sem: Standard error of the mean (unitless) * significant_change: Statistical significance indicator (boolean/unitless) #### File: 2B_food.csv Description: Food-seeking behavior across development. Variables: * time_index: Post nest-leaving day (days) * dev_day: Day post birth (days) * mean: Mean interaction with food hopper (count) * sem: Standard error of the mean (count) * significant_change: Statistical significance indicator (boolean/unitless) #### File: 2B_water.csv Description: Water-seeking behavior across development. Variables: * time_index: Post nest-leaving day (days) * dev_day: Day post birth (days) * mean: Mean interaction with water spout (count) * sem: Standard error of the mean (count) * significant_change: Statistical significance indicator (boolean/unitless) #### File: 2C_3DPCA.csv Description: Three-dimensional PCA of behavioral trajectories. Variables: * PC1, PC2, PC3: Principal components (unitless) * behavior_id: Numeric behavior index (integer) * behavior_label: Behavior name (categorical) #### File: 2D.csv Description: PCA overlap analysis of behaviors. Variables: * behavior: Behavior name (categorical) * overlap_ratio: Ratio of overlap in PCA space (unitless) * volume_or_area: PCA space volume/area (unitless) * n_dimensions: Number of PCA dimensions (count) #### File: 3B.csv Description: Huddling and nest-exit behavior across development. Variables: * day: Day post birth (days) * mean_huddling: Mean time spent huddling (seconds) * sem_huddling: Standard error of mean huddling (seconds) * mean_exits: Mean nest exits (count) * sem_exits: Standard error of nest exits (count) #### File: 3D.csv Description: Pup vs. adult interaction dynamics across days. Variables: * day: Postnatal day (days) * mean_pup_pup: Mean pup-pup approaches (count) * sem_pup_pup: SEM of pup-pup approaches (count) * mean_adult_pup: Mean adult-pup approaches (count) * sem_adult_pup: SEM of adult-pup approaches (count) #### File: 3F.csv Description: Comparison of pup-pup and pup-adult interactions. Variables: * day: Postnatal day (days) * mean_pup_pup: Mean pup-pup approaches (count) * sem_pup_pup: SEM of pup-pup approaches (count) * mean_pup_adult: Mean pup-to-adult approaches (count) * sem_pup_adult: SEM of pup-to-adult approaches (count) #### File: 4F.csv Description: Network size (number of states) across development. Variables: * Day: Postnatal day (P15 = 0) (days) * Category: Animal group (categorical) * Mean: Mean network size (count) * Std: Standard deviation (count) #### File: 4H.csv Description: Number of cycles in behavioral state-transition graphs. Variables: * Day: Postnatal day (P15 = 0) (days) * Category: Animal group (categorical) * Mean: Mean number of cycles (count) * Std: Standard deviation (count) #### File: 4J.csv Description: Transitivity (clustering coefficient) of state-transition graphs. Variables: * Day: Postnatal day (P15 = 0) (days) * Category: Animal group (categorical) * Mean: Mean transitivity (unitless) * Std: Standard deviation (unitless) #### Files: 5B_Pups.csv, 5B_Adults.csv Description: Circadian foraging behavior by developmental period. Variables: * chunk_label: Developmental period (categorical) * mean_foraging: Mean foraging trace (unitless/normalized counts) * sem_foraging: SEM (unitless) * std_foraging: Standard deviation (unitless) #### Files: 5C_Adults.csv, 5C_Pups.csv Description: Circadian locomotor activity. Variables: * chunk_label: Developmental period (categorical) * mean_distance: Mean distance traveled (cm) * sem_distance: SEM (cm) * std_distance: Standard deviation (cm) #### Files: 5D_Adults.csv, 5D_Pups.csv Description: Circadian food hopper interactions. Variables: * chunk_label: Developmental period (categorical) * mean_food: Mean food interactions (count) * sem_food: SEM (count) * std_food: Standard deviation (count) #### Files: 5E_Adults.csv, 5E_Pups.csv Description: Circadian water spout interactions. Variables: * chunk_label: Developmental period (categorical) * mean_water: Mean water interactions (count) * sem_water: SEM (count) * std_water: Standard deviation (count) #### Files: 5F_adults_w_adults.csv, 5F_pups_w_pups.csv Description: Circadian pairwise proximity of social partners. Variables: * chunk_label: Developmental period (categorical) * mean_pairwise: Mean pairwise distance (cm) * sem_pairwise: SEM (cm) * std_pairwise: Standard deviation (cm) #### File: 5H.csv Description: Bhattacharyya distances for developmental segmentation. Variables: * t1: First developmental boundary (days) * t2: Second developmental boundary (days) * bhattacharyya_distance: Distance value (unitless) #### File: S1.csv Description: Cross-validation comparing real vs shuffled trajectories. Variables: * day: Postnatal day (days) * mean_real: Mean of real data (unitless) * sem_real: SEM of real data (unitless) * mean_shuffled: Mean of shuffled control (unitless) * sem_shuffled: SEM of shuffled control (unitless) #### File: S2_water.csv Description: Water-seeking trajectories with shuffled control. Variables: * day: Postnatal day (days) * mean_real: Mean of real data (unitless) * sem_real: SEM (unitless) * mean_shuffled: Mean of shuffled control (unitless) * sem_shuffled: SEM of shuffled control (unitless) #### File: S2_food.csv Description: Food-seeking trajectories with shuffled control. Variables: * day: Postnatal day (days) * mean_real: Mean of real data (unitless) * sem_real: SEM (unitless) * mean_shuffled: Mean of shuffled control (unitless) * sem_shuffled: SEM of shuffled control (unitless) #### File: S3.csv Description: PCA of water and food developmental trajectories (original vs shuffled). Variables: * label: Behavior and condition (categorical) * type: Original or shuffled (categorical) * PC1, PC2: Principal components (unitless) #### File: S4.csv Description: Pearson correlation values for condition comparisons. Variables: * condition: Condition label (categorical) * value: Correlation coefficient (unitless) #### File: S5.csv Description: Inter-behavior correlations. Variables: * source_behavior: Source behavior name (categorical) * target_behavior: Target behavior name (categorical) * correlation: Pearson r (unitless) #### File: S6.csv Description: Full PCA output with metadata. Variables: * PC1, PC2: PCA components (unitless) * behavior_id: Numeric behavior index (integer) * behavior_label: Behavior name (categorical) * cohort_sample_id: Unique sample ID (categorical) Please contact the authors for questions about variable definitions or data structure. ## Code/software All data can be loaded / run in python with .csv loading packages The videos are in .mp4 format and do not require any additional software for access. The ground truth tracks were saved in Ultralytics data format for behavior annotation. The format is described in detail here: https://docs.ultralytics.com/datasets/pose/ The Sleap predictions were saved in .slp file format and can be accessed using the Sleap I/O interface provided here: https://github.com/talmolab/sleap-io. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * Mitelut et al, [https://www.biorxiv.org/content/10.1101/2023.11.10.566632v2](https://www.biorxiv.org/content/10.1101/2023.11.10.566632v2)