FAIRmat, Guide to Writing a Research Data Management PlanIn this guide, we will provide you with comprehensive information and practical tips specific to the fields of condensed-matter physics and materials science on creating a data management plan (DMP) that meets the DFG requirements and aligns your research with the FAIR data principles, the DFG code of conduct, and the EU open science policy. We will take you through the essential components of a DMP, provide tips on data management best practices, and offer guidance on appropriate tools and technologies. By the end of this guide, you will have the knowledge and tools necessary to create a thorough and effective DMP that not only meets the requirements of the DFG but also supports the long-term success of your research project. This guide follows the sections in the DFG checklist, and the questions to be addressed as required by the DFG are listed after each section. Although this guide is based on the requirements of the DFG, the information is not exclusive to any specific funding agency and can be used as a general guide for other research areas. Before preparing a DMP for your project, make sure to check the specific requirements for your funding agency, discipline, and research institution.
Metadata Schema for the German Human Genome-Phenome ArchiveThis White Paper summarizes the development of the German Human Genome-Phenome Archive (GHGA) Metadata Model and describes the modeling framework in detail. Further, it elaborates on implemented ontologies, data privacy considerations and alignment with other (inter-) national consortia.
Combining the BIDS and ARC Directory Structures for Multimodal Research Data OrganizationInterdisciplinary collaboration and integration of large and diverse datasets are becoming increasingly important. Answering complex research questions requires combining and analysing multimodal datasets. Research data management follows the FAIR principles making data findable, accessible, interoperable, and reusable. However, there are challenges in capturing the entire research cycle and contextualizing data according, not only for the DataPLANT and NFDI4BIOIMAGE communities. To address these challenges, DataPLANT developed a data structure called Annotated Research Context (ARC). The Brain Imaging Data Structure (BIDS) originated from the neuroimaging community extended for microscopic image data. Both concepts provide standardised and file system based data storage structures for organising and sharing research data accompanied with metadata. We exemplarily compare the ARC and BIDS designs and propose structural and metadata mapping.
