Enabling research data management for non IT-professionals

Abstract

In almost all academic fields, results are derived from found evidence such as objects to be digitized, case studies, observations, experiments, or research data. Ideally, results are linked to its evidence to ease data governance and reproducibility of results, and publicly stored in a research data repository to be themselves linked as evidence for new results. This linking has created a huge mesh of data over the years. Searching for information, deciding whether found information is relevant, and then using relevant information for producing results costs a lot of time in such a mesh of data. Due to the fact that a high investment of time is associated with high costs, funding agencies such as the German Research Foundation (Deutsche Forschungsgemeinschaft; DFG) or the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung; BMBF) demand a data management plan (DMP). A DMP is designed to reduce the costs of projects submitted to a funding agency and to avoid future costs when data repositories are to be reused. Nevertheless, a DMP is often not fully implemented because it is too costly, which in the long run leads to a mesh of data. In this thesis, we identify problems and present solutions usable by non-IT-experts to spend less time on solving problems that arise at implementing a DMP at each project’s repository and coping with a huge mesh of data across many repositories. According to our observations, humanities scholars produce research data that are meant to be printed later or uploaded at a repository. Potential problems that arise at a repository, independent of other repositories, to be solved are manifold. Data to be printed is encoded with a markup language for illustration purposes and not machine interpretable formatted. We not only show that such formatted data can be structured with a parser to be interpreted by machines, but also what possibilities open up from the structured data. Structured data is automatically combined, linked, transformed into other formats, and visualized on the web. Visualized data can be cited and annotated to help others assess relevance. Once, problems are solved at each repository, we show how we cope with data linked across repositories. This is achieved by designing a human-aware information retrieval (IR) agent, that can search in a mesh of data for relevant information. We discuss in what way the interaction of a user with such an IR agent can be optimized with human-aware collaborative planning strategies.