The database organizes data and experiments in a structured manner, connecting notes and analysis in a central location and allows for long-term access to the data, easy data sharing, and data discovery. The database works as a digital lab notebook that can be used while collecting and analyzing data and can further provide both the platform and the tools for sharing data within a lab and further with the scientific community. It provides a data structure and underlying database-infrastructure to describe electrophysiological recordings performed in rodents. There are two powerful entry-points to access the underlying data: This web interface and a REST API, which can be accessed from Matlab, Python, or other REST-API supporting programming languages. Both entry points allow you to read and write to the database.

The database is built upon a hierarchical structure with the following levels: projects -> animals -> sessions-> cells. Sessions further relate to behavioral paradigms via epoch blocks. There are two levels of attributes: personal (private) and general attributes (public). Personal attributes include: built mazesused equipment and data repositories, and general attributes include brain regions, cell types, maze types, tasks, silicon probes, optic fibers, persons, species, strain, virus construct. Use the top menu “databank” to explore the database structure and its content.


Each of the structural hierarchical tables can be synchronized with mat files that are stored with the original datasets, and all tables can be accessed from Matlab. We further have a Matlab database toolset that you can use for convenient access.

First-time users

The first step is to create an account if you don’t have one already. As you use the database for the first time, start by defining a project, then add your animals, and then your recording sessions. For each level, there are attributes that potentially need to be added to the database in order to describe your experiments, so it is recommended to explore the personal and general attributes (described above) to make sure that suitable entries exist and to get a better sense of the database structure. It is recommended that you add data repositories that you use for storing your data if they do not already exist in the database. Likewise, it is recommended to make sure that your mazes and behavioral paradigms exist before putting in your data.

The database can be used at many levels but you gain the most when you use it extensively to describe your experiments and data.