Start of Teaching Series on Research Data Management in Bioinformatics - Workshop on Data Management Plans in Freiburg

Research processes produce an increasing amount of digital data. They are often very discipline-specific and exist in different forms. They can be the basis as well as the result of research. Preserving, managing and curating research data thus becomes a central task for every scientist and research institution - from the preparation of a research proposal to everyday research work. This process must be structured and organised. An increasingly established solution is the use of data management plans (DMP). They can primarily be understood as an abstract concept that helps to define data management through the planned course of the research project and its subsequent long-term availability.

A DMP structures the handling of research data over their life cycle. In the process, findings on required or generated data sets are to be considered as well as their licensing, enrichment with metadata, necessary processing steps and software, or ownership over time. The aim of the event is to explore the manifold questions surrounding data management and to enable the participants to create such a plan themselves. The course will cover the following topics:

  • Introduction to research data management
  • Presentation of the individual components of a data management plan: Collection of project metadata, description of the data genesis or data stock, data management, consolidation and archiving, exchange and standardization
  • Development and design of a data management plan
  • Digital data management in the research proposal
  • Presentation of online help tools (e.g. RDMO) and example DMPs (BMBF, DFG)
  • Institutional support

The course is part of the university's professional qualification programme. Application is possible via the Campus Management System.

Next step in the NFDI building process: Grant application submitted

On Tuesday the 15th October the DataPLANT NFDI consortium submitted it's proposal to the DFG. The consortium in Fundamental Plant Research consists of roughly 30 participants including universities and large research institutions distributed over the country. A significant proportion of the participants originate from Baden-Württemberg and the BioDATEN Science Data Center. Further co-applicants are the Technical University of Kaiserslautern and the Forschungszentrum Jülich.

The central aim of the DataPLANT consortium is to advance research data management in it's designated community and generate added value in the field of basic plant research. Successful collaboration and use of data of different modalities – from many sources and experiments, pre-processed or analysed with a variety of algorithms – requires contextualization of the data. The FAIR Data 1 and Linked Open Data Principles provide critical guidelines for research data management. Various consortia have therefore made proposals for best practice and compliance with these principles, but it is almost always the initiative of individual researchers to implement them. Therefore, comprehensive information on the required quality for use by third parties is rarely available. Researchers have been shown to require practical assistance in exploiting the fragmented and complex resource landscape. This increases the need for a tailor-made (infra)structure for research data management. By combining technical expertise in the fields of fundamental plant research, information and computer sciences and infrastructure specialists, DataPLANT will support plant scientists in every RDM concerns. DataPLANT will create a service environment to contextualize research data according to the FAIR principles with minimal additional effort and to support the entire research cycle in modern plant biology. The tailor-made service landscape in DataPLANT will consist of technical-digital assistance as well as on-site personnel assistance. DataPLANT thus creates a central entry point and a valuable subject-specific data and knowledge resource. In combination with teaching and training concepts, data literacy is strengthened and a long-term motivation for the creation of well-indicated data objects is generated. By integrating plant science into the NFDI network as a whole, DataPLANT is driving the digital transformation and democratization of research data in the field.

Science Data Center BioDATEN as part of the NFDI process

Together with colleagues from Tübingen, Konstanz, Freiburg, Heidelberg, ... parts of the BioDATEN community joined forces with the DaPLUS+ consortium from Kaiserlautern, Jülich and Düsseldorf to paticipate in the process to create a National Research Data Infrastructure. The newly formed consortium centers around plant data in bioinformatics and handed in a binding "Letter of Interest".

In modern hypothesis-driven science, researchers increasingly rely on effective research data management services and infrastructures that facilitate the acquisition, processing, exchange and archival of research data sets, to enable the linking of interdisciplinary expertise and the combination of different analytical results. The immense additional insight obtained through comparative and integrative analyses provides additional value in the examination of research questions that goes far beyond individual experiments. Specifically, in the research area of fundamental plant research that this consortium focuses on, modern approaches need to integrate analyses across different system levels (such as genomics, transcriptomics, proteomics, metabolomics, phenomics). This is necessary to understand system-wide molecular physiological responses as a complex dynamic adjustment of the interplay between genes, proteins and metabolites. As a consequence, a wide range of different technologies as well as experimental and computational methods are employed to pursue state-of-the-art research questions, rendering the research objective a team effort across disciplines. The overall goal of DataPLANT is to provide the research data management practices, tools, and infrastructure to enable such collaborative research in plant biology. In this context, common standards, software, and infrastructure can ensure availability, quality, and interoperability of data, metadata, and data-centric workflows and are thus a key success factor and crucial precondition in barrier-free, high-impact collaborative plant biology research. Toward this, the key objectives pursued by this consortium are:

  1. A specific community standard for fundamental plant research (meta)data and workflow annotation, based on generic, existing and emerging standards (e.g., ISA model, MIAPPE) and ontologies in plant science.
  2. Assistive mechanisms and services to build, link and maintain the complete research context during data acquisition, curation, analysis, and publication.
  3. Mechanisms for collaborative research based on enrichment and automatized crosslinking of plant-research specific (meta)data to facilitate research context management.
  4. A cloud-based open reference implementation of these mechanisms and services, and a central hosted instance thereof.
  5. A robust, federated infrastructure both for data computation and management covering the complete data lifecycle.
  6. Comprehensive training of community members through workshops and summer schools and providing open training material.

The final grant application is due to the 15th October.

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