Fusion of Multiple Open Source Applications for Data Management Workflows in Psychology and Neuroscience

Finding a compromise between researchers’ needs, their skills in data management, data access restrictions, and limited funding for RDM is a complex but highly relevant and timely challenge. At the University of Marburg this challenge is taken on by the team of the “Data Hub”. The team consists of several people with different responsibilities, backgrounds, and affiliations such as project management staff, scientific staff, data stewards, data scientists, technical administrative staff, located in Marburg and Gießen. The Data Hub is funded by The Adaptive Mind (TAM) and supported by the information infrastructure project (NOWA) of the SFB135, which are consortia in the fields of psychology and neuroscience, with over 50 involved PIs, based in several locations in the federal state of Hesse, Germany. Although the research data in the two consortia are restricted to the fields of psychology and neuroscience, a major challenge is the need to harmonize heterogeneous data. The data encompass research data from different modalities such as behavior, eye tracking, EEG and neuroimaging as well as code for experiments and analysis in various programming languages. Therefore, the data management workflow needs to be applicable to heterogeneous in- and output data, different project sizes, and numbers of researchers involved. Furthermore, tools need to be able to integrate those heterogeneous data by utilizing a harmonizing standard in the field (here: BIDS). To increase the reproducibility of research findings, an integration of version control and provenance tracking (here: DataLad) should be available. For this, the team must have an understanding at which point to include the researchers: How much background knowledge about software do they have and how much do they really need? Which functions of the software are necessary and which ones can be skipped because they’ll never apply to the researchers’ work? Do they need a lot of hands-on practice or is the concept enough? In our presentation, we will first introduce the Data Hub of the University of Marburg and its technical architecture. We will then present the data management tools utilized in the Data Hub, i.e., DataLad, GIN, GitLab, JupyterHub, and BIDS. We will specifically focus on how these tools are interconnected, i.e., the research data management workflow of the Data Hub. Then, we will outline the challenges for both the researchers as well as the Data Stewards regarding training, support and maintenance of the services.

(This talk was not recorded.)