Data Management Plan for Use Cases of the GeoKur Project on suitability of global land use data to assess relationships between land use, degradation, pollination and human migration

The BMBF project GeoKur aims to support the curation and quality assurance of Earth System Science (ESS) data sets, focusing on the suitability of geospatial time-series of global land use data by analysing human-environment relations such as land degradation, biodiversity, human migration and ecosystem services. This DMP describes two use cases of the GeoKur project. The use cases use various existing and publicly available datasets of land-use, net-migration, crop yield, etc., to showcase best practices to determine their fitness for use. The data will be used to 1) identify spatial patterns of land degradation processes and in-migration in Sub-Saharan Africa between 2000 and 2015 and to 2) investigate the effects of agricultural management and pollination-related variables on crop-specific yields. Within the project two partners collaborate on the two use cases: a team of researchers collects, analyses and provides data, scripts and related publications and a team of data stewards and software engineers provides discipline-specific guidance, adapted tools, and develops specific methods and tools based on the researcher needs. Thus, this data management plan (DMP) strongly focusses metadata, software, and technical aspects for ESS projects, instead of describing common RDM practice and methods. Moreover, it serves as example to develop ESS discipline-specific guidance and tools for data management. This DMP follows the Science Europe Template. The two use cases are compliant with the Principles for the Responsible Handling of Research Data at the UFZ. The present principles are based on the Guidelines of the Helmholtz Association on the Management of Research Data, on the Guidance of the European Commission on Data Management according to the FAIR Principles and the Deutsche Forschungsgemeinschaft (DFG) Research Data Guidelines and Guidelines for Safeguarding Good Research Practice. The project uses publicly available geospatial datasets and provides produced datasets as open data, compliant with the FAIR (Findability, Accessibility, Interoperability, Re-usability) principles. During the project, datasets will be managed data management system (DMS) implemented as open source catalogue CKAN with spatial extensions facilitating direct metadata and data access via an Application Programming Interface (API). For long-term storage, selected results will be stored in the institutional data management system, called UFZ Data Management Platform including raw data after the project ends, resp. published on the Earth & Environmental Science data repository PANGAEA without raw data. The researchers develop data preparation and analysis scripts using the language R. Scripts will be managed on GitHub and published via Zenodo following reproducible research approaches by including links to the well-documented open-source GitHub repository and used datasets to ensure reproducibility of the applied approaches. The data management of the project focuses on discipline-specific provenance and quality tracking for produced and collected datasets and documentation. Therefore, all datasets will be described using a project-specific geospatial extension for the Data Catalog Vocabulary (GeoDCAT) metadata profile with linked provenance ontology (PROV-O) and Data Quality Vocabulary (DQV). Metadata in the GeoDCAT format will therefore be automatically extracted or tracked by GeoKur-specific tools or extended by manual processes, when extraction or tracking is not possible. A specific quality register facilitates the curated management of quality measure descriptions by linking from dataset metadata to the descriptions. Thus, a specific quality assurance workflow is developed, e.g. managing use case-specific quality measures and activities