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Abstract

The increasing demand of researchers to make the underlying research data openly accessible, in addition to the classic publication forms, can improve the reproducibility of scientific findings, whether voluntarily or due to the institution’s or research funders’ requirements. As a result, researchers depend on expressive descriptions of research data for reusability. These descriptions are in the form of comprehensive metadata stored in heterogeneous formats in research data repositories. However, finding the appropriate data is arduous, as there is a growing amount of research data stored in various places and only a few repositories offer the function of displaying a preview of the data. Research work efficiency can benefit from data previews whenever researchers can explore portions of a dataset before deciding on the relevance of the data for accessing and downloading the whole dataset. The Leibniz Data Manager (LDM) is a research data management system that resorts to Semantic Web technologies to empower FAIR principles. LDM supports searching and exploring research data across various repositories. LDM provides an additional (meta-)data management layer for data collected from existing research data repositories based on the webbased data catalog software CKAN (Comprehensive Knowledge Archive Network). The primary purpose of LDM is to preview research data, e.g., tables, audio-visual material like AutoCAD files or 2D and 3D data, or live programming code via Jupyter Notebook(s) so that their potential for reuse can be easily evaluated. Since LDM is available as a Docker container, anyone can install a local LDM distribution to assist research data management in different phases of the data lifecycle. LDM is accessible at https://service.tib.eu/ldmservice/. LDM empowers researchers by supporting them in preserving their research data as open and FAIR as possible. With LDM, researchers can check whether their data is displayed correctly and whether it is available in suitable and preferably open data formats before publication. In addition, humans and computational programs can access machine-readable metadata, which can be exported in various schemas (DCAT, DataCite, and DublinCore) and RDF serializations. This enables automated searching and processing by various data bases and tools. More importantly, DataCite DOIs and ORCIDs ensure the persistence and findability of LDM (meta-)data. At the poster session we will demonstrate how scientists can be supported in searching for datasets and preserving their research data. We are also interested in collecting ideas about future requirements to be implemented in upcoming versions of the LDM.