Scientific disciplines are generating large volumes of valuable data with the potential to drive future discoveries across various domains. However, the effective management of these research data has now become indispensable and increasingly crucial in all scientific disciplines. Research Data Management (RDM) critically addresses challenges related to data integrity and curation throughout the research data life cycle. While RDM practices and procedures often exhibit substantial variations due to the diverse nature of projects, making it challenging to find overlap between disciplines, there are still similarities regarding expectations, requirements, and familiarity with tools, techniques, and infrastructure. This is due to the evolving dynamics of data generation and analytics in every scientific discipline. In this study, we conducted two comprehensive surveys by focusing on a multidisciplinary research environment involving 15 research projects and formulated the questionnaires to evaluate the RDM prerequisites and workflows for these projects. The surveys are associated with different activities in the research data life cycle, which we illustrate in the concept of the RDM Impact Cycle. We present acquired observations and provide comprehensive insights into full-cycle RDM best practices. Our discussion then emphasizes the influence of RDM on interdisciplinary research paradigms, paving the way for exciting data-driven studies supported by robust RDM practices.

Research papers are often the primary source of scientific information dissemination, as researchers encapsulate their findings in these documents. Generally such findings are of complex types, diverse expressions and also carry rich context. The traditional approach for extracting certain scientific information from these documents is manual extraction, which is very time consuming. Due to the rapid increase in number of publications, using the full potential of these rich data sources by manual extraction is becoming infeasible. In this paper, we propose a framework for the automatic extraction of targeted (user defined) quantitative information, e.g. temperature sensor values, with its geo-spatial context from scientific documents. Given a database of scientific documents and a targeted user-defined geo-tagable measurement variables, mass accumulation rate (MAR) and sedimentation rate (SR), the problem we are addressing is to retrieve all the values together with their geo-spatial information respectively. Though there has been done a lot in information retrieval, to the best of our knowledge, this problem has not been explored, yet. We design a novel heterogeneous linking solution, that links measurements with locations, which are found by our tailored extraction pipeline. In experimental studies based on our novel dataset of Marine Geology papers, we showcase the capabilities of our linking framework using common geo-tagable Marine Geology measurements.

Quantitative information derived from scientific documents provides an important source of data for studies in almost all domains, however, manual extraction of this information is very time consuming. In this paper we will introduce a system Geo-Quantities that supports the automatic extraction of quantitative, spatial and temporal information of a given measurement entity from scientific literature using text mining techniques. The difficulty of automatic measurement recognition is mainly caused by the diverse expressions in the papers. Geo-Quantities offers an interactive interface for the visualization of extracted user-defined information, in particular spatial and temporal context. In our demonstration, we will showcase the capabilities of our system by retrieving measurements such as “mass accumulation rates” and “sedimentation rates” from scientific publications in the field of marine geology, which could have high impact in studies for building global mass accumulation rate maps. For training and evaluation of Geo-Quantities we use a corpus of domain-relevant papers.