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Video recordings of 11 rats were digitized at five frames per second, and parameters describing the shape and the position of the rat were calculated. The behavior displayed by the rats was observed by an experienced observer. Separate neural networks were trained and validated, using the data for each individual rat. The neural networks correctly classified an average of 76.53% of the frames in the validation set and 98.18% of the frames in the training set. A single neural network was trained with 6 rats and validated with 5 rats. The neural network correctly classified 63.74% of the frames in the validation set and 82.85%of the frames in the training set.To quantify and qualify the effects of experimental manipulations, observers or researchers often measure the overt, postural behavior of a subject or the activity a subject performs. To reduce the complexity of the registration, a selection of behaviors is made from the total number ofbehaviors that a subject can perform. This list of behavioral categories is known as the ethogram. The measurements of these categories, often expressed in units of time (duration) or in frequency of occurrence, are extensively used in multiple research areas, such as pharmacology, agricultural science, and biomedical research. The postures, transition of postures, or series of postures that constitute a behavioral element have been described for several species. These publications are used as a standard within a field to form a consensus on the definition of a behavioral category. Previous publications have described the typical postures that constitute the behavioral categories of the rat (Rattus norvegicus;

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The following article is reblogged with permission from the Netherlands eScience Center. Please find the original post at this link.Software is hugely important for research, but it's not very visible as a key research output. We present the Research Software Directory (RSD), an online service designed to put research software in the spotlight. The RSD showcases software to the research community and promotes its reuse and impact. The platform is the result of a collaboration between the eScience Center,