Empirical research based on time series is a data intensive activity that needs a data base management system (DBMS). We investigate the special properties a time series management system (TSMS) should have. We then show that currently available solutions and related research directions are not well suited to handle the existing problems. Therefore, we propose the development of a special purpose TSMS, which will offer particular modeling, retrieval, and computation capabilities. It will be suitable for end users, offer direct manipulation interfaces, and allow data exchange with a variety of data sources, including other databases and application packages. We intend to build such a system on top of an off-theshelf object-oriented DBMS.

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To improve animal welfare and data quality and reproducibility during research conducted under anaesthesia, anaesthetic depth in laboratory animals must be precisely monitored and controlled. While a variety of methods have been developed to estimate the depth of anaesthesia in humans, such tools for monitoring anaesthetic depth in laboratory animals remain limited. Here we propose an epidural electrocorticogram-based monitoring system that accurately tracks the depth of anesthesia in mice receiving inhalable isoflurane anaesthesia. Several features of the electrocorticogram signals exhibit robust modulation by the concentration of the administered anesthetic, notably, corticocortical coherence serves as an excellent indicator of anaesthetic depth. We developed a gradient boosting regressor framework that utilizes the extracted features to accurately estimate the depth of anaesthesia. Our method for feature extraction and estimation is conducted with a latency of only ten seconds, establishing a system for the real-time tracking of anaesthetic depth in mice.