A virtual library for behavioral performance in standard conditions—rodent spontaneous activity in an open field during repeated testing and after treatment with drugs or brain lesions

Abstract Background Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose. Findings Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset. Conclusions Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions.

posture reconstruction and kinematics measurements? To what extent do the suggested videoenhancing and upscaling tools facilitate the tracking performance (e.g., increase the range of trackable body parts? contribute to a more elaborated reconstruction of postures? increase SNR and tracking quality?). Does the video resolution support all these? In response to my concern about the low video resolution, the authors referred to another work that used an entire 352x240 px. ROI to record head and whiskers. Clearly, this example is very different than recording a rat running in a 160x160 cm arena. It is simply irrelevant to whether the video resolution is compatible with the potential reuse the authors suggested. Indeed, as the authors replied, data compatibility is an empirical question. And one that can be easily answered. I realize that the authors cannot be expected to demonstrate the compatibility of their data with every potential data-analysis tool or resolution; however, their choices of citations and examples for reuse follow that, at this level at least, the authors should address this empirical question and provide a concrete estimation. Because such an examination is so easily achievable (for example, training with DeepLabCut, which is open source and beautifully documented), I do not understand the dismissive statement that data compatibility with these products is an empirical question. Advertising a potential involves the responsibility of demonstrating its feasibility. Behavioral repertoire-The second paragraph in the introduction explains: "Video records, by capturing the entire richness of behavior, have potential utility well beyond their immediate value in a particular experiment. They could be reused as inputs for new experiments to test different hypotheses. They can be reused to analyze aspects of behavior not of interest in the particular experiment. Importantly, they could serve as data for new types of analysis of behavior, not yet available." This paragraph inspired me to consider the behavioral richness in the dataset and led to comment iv in my remarks on the previous version of the manuscript, dealing with the convergence and reduction of behavioral repertoire due to repeated measurements in the same environment. It is true that saline-injected animals should not exhibit OCD-like behaviors, which is the reason they make a good control for the OCD-model groups (brain and pharmacological treatments). Still, repeated exposure to the same stimuli considerably reduces the suggested richness of behavior and any utility that is beyond the original context. With that, the authors cleverly chose examples in the reuse potential section that either relate to the OCD field ("to identify and measure ritual behavior of relevance to OCD …; or to classify effects of different drugs on behavioral activity."), or specifically address the longitudinal questions ("circadian, seasonal or annual rhythms…quantify across trials the range of possible responses, … and across time the change with repeated testing."). Their choice of examples indicates a full understanding of the content of comment iv, and I accept their implicit approach to this issue. Novelty-I also accept the authors' reply regarding the novelty comment. I believe that their annotation schema clearly details the properties of the database and is elegantly constructed. With the supplements to the reuse potential section, I think the authors did a good job achieving their goals. Furthermore, I support the decision to avoid loading their data to GigaDB, creating unnecessary, and expensive mirroring, but suffice with providing links to the data available in other repositories. To summarize, I find the authors' replies and revisions satisfactory, with the exception of addressing the limitations posed on the reuse potential due to low video resolution (compared with the typical currently available video cameras). The unique value of this manuscript is the reuse potential of the data, which is a function of size, the annotation schema, and the data quality and compatibility. The authors achieved the two first points with the straightforward distinction of effective size, the addition of a simple selection tool, and the impressive annotation schema. Regarding data quality and compatibility, I expect the authors to support their claim for the potential reuse with machine learning tools and extraction of additional features, providing concrete evidence for the feasibility of such analysis.

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