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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 2
AGILE GIScience Ser., 2, 31, 2021
https://doi.org/10.5194/agile-giss-2-31-2021
AGILE GIScience Ser., 2, 31, 2021
https://doi.org/10.5194/agile-giss-2-31-2021

  04 Jun 2021

04 Jun 2021

Recommendations for Future Data Management Plans in Earth System Sciences

Christin Henzen, Stefano Della Chiesa, and Lars Bernard Christin Henzen et al.
  • Chair of Geoinformatics, Technische Universität Dresden, Germany

Keywords: data management plans, research data infrastructures, research data management, quality information, provenance

Abstract. Most research activities in Earth System Sciences (ESS) are data-driven. There is a growing need to establish innovative, cross-cutting data management and data analysis methods in ESS to support the collaboration of interdisciplinary research building on heterogeneous sources. Data management plans (DMPs) are structured documents that outline data handling and include for instance agreements on roles, specifications of data products, and definition of workflows. However, the structure of existing DMP templates is mostly designed for funder’s requirements and consequently address only the broad and interdisciplinary research community. Thus, these templates do lack (1) guidance on how to structure domain-specific information in a DMP – by providing domain-specific profiles, e.g. to harmonize the structure and improve the comprehensibility of DMP instances and (2) (linking into) tools enabling efficient management and reuse of information / sections of DMP instances. Therefore, we provide a concept of future DMP templates and address geo-domain-specific requirements, and the integration of DMPs into research data infrastructures. We recommend integrating structured provenance and quality information, using established concepts, and define a pathway to link tools into research data infrastructures, such that they foster automation of data management workflows and data reuse.

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