EthoWatcher OS: improving the reproducibility and quality of categorical and morphologic/kinematic data from behavioral recordings in laboratory animals

Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences.

In this article, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools is critical and, most of the time, required for behavioral studies, but they are normally overlooked because they are difficult to find or use. For example, to foster the use of concordance analysis, the reliability module can perform inference using only one repetition of the same analysis. More than just bureaucratic requirements, these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Additionally, EW-OS provides extended capabilities of temporal segmentation for categorical and spatial segmentation for morphological and kinematic data extracted from videos. Finally, this software was built to allow for continued collaborative development; is freely available through an open-source platform cooperative environment and is freely available through an open-source platform cooperative environment.