Research Data Management


The professional handling of research data and the associated establishment of adequate research data management for the collaborative research center is one of the main tasks of CRC 1436.

We are developing an overall RDM strategy to manage the data from the different disciplines and structures within the CRC consortium, as well as to provide data sharing solutions that meet data protection requirements while ensuring cross-project collaboration. The final goal is the integration of the
of the CRC1436 into the existing RDM structures and networks.

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Research Data Management (RDM) is vital in collaborative neuroscience research centers across partnering institutions in Germany to ensure accurate data, seamless collaboration, compliance with regulations, and the potential for data-driven discoveries. It enables efficient research processes, supports transparent and reproducible findings, and aligns with funding requirements and open science practices. 

Within the intricate framework of SFB 1436, involving multiple participating institutions, substantial data volume, and intricate data sharing needs, Research Data Management (RDM) serves as a pivotal tool. It streamlines the research pipeline, untangling complexities, and fostering seamless collaboration by ensuring efficient data handling, structured sharing, and organized work flows.

Descriptive meta data in Research Data Management (RDM) for the neuroscience research center (SFB 1436) involves creating detailed information about the research data, such as its content, context, structure, and format. This meta data enhances data discoverability, supports collaboration among researchers, and ensures that the complex and intricate data sets in neuroscience are effectively organized and understood by both current and future stakeholders.

Data management plans (DMPs) in Research Data Management (RDM) for the neuroscience research center (SFB 1436) outline strategies for handling, organizing, and sharing research data. These plans ensure that the data is collected, documented, stored, and preserved effectively, promoting data integrity, collaboration, compliance with ethical standards, and the long-term value of research findings.

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