Recommendations for Future Data Management Plans in Earth System Sciences

Abstract

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.

Research data infrastructures in environmental sciences—Challenges and implementation approaches focusing on the integration of software components

“…htm [guideline]), and consequently focuses on core requirements as described in the Science Europe Practical Guide to the International Alignment of Research Data Management (Science Europe, 2021). Thus, these templates need improvements regarding domain-specific information, since they lack guidance on how to structure domain-specific information in a DMP and domain-specific templates (Henzen et al, 2021;Netscher et al, 2022;RDA DMP, 2020).…”

“…Data management plans (DMP) are formal and structured documents that capture all relevant information along the data lifecycle and include for instance specifications of data products or workflows, agreements on documentation, storage, access, or roles (Science Europe, 2021). DMP are evolving and can be seen as living documents, used and improved collaboratively along the data lifecycle (Henzen et al., 2021; Miksa et al., 2019). The purpose of DMP differs due to administrative requirements specified by funding institutions and project settings, for example, on the outline or number of pages.…”

“…The structure of existing DMP templates is mostly designed for funder's requirements, for example, for the EU Framework Programme for Research and Innovation Horizon 2020 (https://ec.europa.eu/research/participants/data/ref/h2020/gm/reporting/h2020-tpl-oa-data-mgt-plan_en.docx [downloadable template], https://ec.europa.eu/programmes/horizon2020//en/what-horizon-2020, https://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cutting-issues/open-access-data-management/data-management_en.htm [guideline]), and consequently focuses on core requirements as described in the Science Europe Practical Guide to the International Alignment of Research Data Management (Science Europe, 2021). Thus, these templates need improvements regarding domain‐specific information, since they lack guidance on how to structure domain‐specific information in a DMP and domain‐specific templates (Henzen et al., 2021; Netscher et al., 2022; RDA DMP, 2020).…”

Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

“…The granularity of this quality documentation may vary -sometimes be very fine (e.g., per-observation in the case of volunteered observations) but the critical common resolution required to support FAIR data publishing is the individual dataset level. 1 Quality assessment results also need to be represented consistently, updated regularly, and should be integrable across systems, services, and tools to enable improved data sharing (Henzen et al 2021;Wagner et al 2021;Peng et al 2021a).…”