One of the most important steps in managing research data is the creation of metadata. To save researchers noticeable routine work in creating metadata in the medium term, the INF project of the collaborative research centre SFB 1280 “Extinction Learning” developed the MetaDataApp. The MetaDataApp is a Java application with a graphical user interface that allows researchers to easily enter metadata, as well as store metadata in local json files along with research data using an inheritance strategy.
The DatabaseApp was developed by the INF project of the collaborative research centre SFB 1280 "Extinction Learning" to improve the accessibility and findability of internally shared metadata and research data. The app provides a graphical user interface (GUI) for faceted search of available metadata records stored in a connected database (.db): Its core functionality is to provide access to the collected metadata, search for research data of interest in the accessible metadata and retrieve the data associated with the search result.
The PigeonSuperModel is an open source repository of multiple pre-trained deep-learning models for markerless pose tracking in pigeons. With this PigeonSuperModel we provide multiple pre-trained neural networks for out-of-the-box video analysis of pigeon behavior. Using a shared PigeonSuperModel across labs, we advocate for a standardized set of markers for pigeon tracking and generalizable models across experiments, animals, and camera views.
SyncFLIR is a repository with code and instructions to build a synchronized multiview video recording setup using computer vision cameras from FLIR on Windows. Instead of using independent action cameras to record behavioral experiments and painfully trying to synchronize the different video tracks by e.g. hand-clapping cues or blinking LEDs, make your life easier using triggered cameras. Such an array of wired cameras is scalable without considerable effort, allowing for synchronized multiview video recording and 3D triangulation.
A free, open source Matlab Toolbox for the control of behavioral experiments. The major aim of the project is to provide set of basic tools that allow programming novices to control basic hadware used for behavioral experimentation without limiting the power and flexibility of the underlying programming language. The modular design of the toolbox allows portation of parts as well as entire paradigms between different types of hardware.
The EDA-Analysis App is a MATLAB App that computes EDA results from raw data acquired with Biopac and Brainvision systems using graphical tools. The app has a graphical user interface that allows the user to view and verify the raw data and the results. Furthermore, there are several export options to save the results in different file formats. To facilitate data exchange with other workgroups, the app offers the possibility to export the EDA data in BIDS format.
The OTBR toolbox helps scientists to take care of their experiments without worrying too much about the technical implementation of the tools. In addition to the help provided by our tools, our toolbox also offers the possibility of graphical representation, which we have realized with the well-known Psychophysics Toolbox. This has the advantage that experienced users are able to use their code for the psychophysics toolbox whereas novices could use our user-friendly functions for graphical requirements.
The Subject Generator helps investigators and subjects to generate SFB 1280 compliant subject codes. The official web version of the Subject Code Generator can be found on the SFB1280 website and on the RUB GitLab. This project was developed to enable a faster and more reliable way to generate the code than the paper-pencil version. The subject code represents a pseudonymization and not an anonymization. Diers, E., Pacharra, M., Merz, C. J., Ernst, T. M., & Otto, T. (2023). Subject Code Generator v1.1 (v1.1).