Research Data Management
Research Data Management (RDM) involves organizing, storing, and preserving data generated in research projects, encompassing raw experiment data, simulations, processed results, source code, and publications. The ideal standard is adherence to the FAIR principles, ensuring data is Findable, Accessible, Interoperable, and Reusable. Despite challenges, ML4Q acknowledges the importance of RDM and is actively addressing it. By embracing FAIR standards, ML4Q aims to overcome the issues of lost or inaccessible data in the physics field, recognizing the need for collaboration and a step-by-step transition. ML4Q is committed to managing research data effectively and elevating it to FAIR standards in the cluster’s work.
RDM in ML4Q
ML4Q has initiated a comprehensive transition towards effective RDM, including the recruitment of a research data manager, implementing measures such as making publications fully reproducible using data repositories like Zenodo, adopting unique identifiers for data accessibility, and aligning with DFG guidelines for a cluster-wide data management policy.
ML4Q Publishing Guide
RDM practices are becoming community standards and are increasingly demanded by publishers.
You can follow the ML4Q Publishing Guide when preparing your research data for publication.
Contact
You have any inquiries regarding RDM, data management plans and open data or specific software solutions.?
If so, you are very welcome to contact ML4Q’s Research Data Manager.
RDM in ML4Q
In ML4Q, efforts to enhance research data management (RDM) have been underway since its start. The Research Data Manager position was filled in February 2021 initializing a series of activities, first headed by Daniel Grothe until January 2024 and subsequently continued by Simon Humpohl starting February 2024, both at RWTH Aachen.
The cluster aims for all publications to be fully reproducible, utilizing community standard data repositories like Zenodo. To achieve this, a Publishing Guide has been developed, replacing ambiguous data availability statements with unique identifiers. This transition facilitates seamless access to published data. Starting January 2024, all members are mandated to comply with the specified Must-haves and additional recommendations outlined in the guideline.
The DFG’s application guidelines emphasize RDM, advocating for a cluster-wide data management policy and the adoption of data management plans across research projects. The National Research Data Infrastructure (NFDI), a nation-wide consortium, further supports RDM initiatives, with ML4Q members actively engaging in communication and participation. Further communication is supported through a loose German network of data stewards in Collaborative Research Centers and Clusters of Excellence. This ExIni-Network meets irregularly to discuss existing software solutions, RDM strategies and more.
Software projects
To address administrative overhead in adding metadata to raw measurement data, a new measurement framework, QuMADA, was developed by Daniel Grothe and Till Huckemann for the use with QCoDeS. QuMADA automatically collects metadata about measurements, streamlining the process and abstracting measurement routines from specific instruments.
ML4Q is also exploring the integration of Electronic Lab Notebooks (ELN) and Scientific Data Management Systems (SDMS) to enhance the organization of research data and metadata.