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- ItemPersistent identification of instruments(London : Ubiquity Press, 2020) Stocker, M.; Darroch, L.; Krahl, R.; Habermann, T.; Devaraju, A.; Schwardmann, U.; D’onofrio, C.; Häggström, I.Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
- ItemMathematical models as research data via flexiformal theory graphs(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2017) Kohlhase, Michael; Koprucki, Thomas; Müller, Dennis; Tabelow, KarstenMathematical modeling and simulation (MMS) has now been established as an essential part of the scientific work in many disciplines. It is common to categorize the involved numerical data and to some extent the corresponding scientific software as research data. But both have their origin in mathematical models, therefore any holistic approach to research data in MMS should cover all three aspects: data, software, and models. While the problems of classifying, archiving and making accessible are largely solved for data and first frameworks and systems are emerging for software, the question of how to deal with mathematical models is completely open. In this paper we propose a solution to cover all aspects of mathematical models: the underlying mathematical knowledge, the equations, boundary conditions, numeric approximations, and documents in a flexiformal framework, which has enough structure to support the various uses of models in scientific and technology workflows. Concretely we propose to use the OMDoc/MMT framework to formalize mathematical models and show the adequacy of this approach by modeling a simple, but non-trivial model: van Roosbroecks drift-diffusion model for one-dimensional devices. This formalization and future extensions allows us to support the modeler by e.g. flexibly composing models, visualizing Model Pathway Diagrams, and annotating model equations in documents as induced from the formalized documents by flattening. This directly solves some of the problems in treating MMS as research data and opens the way towards more MKM services for models.
- ItemMathematical models: A research data category?(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2016) Koprucki, Thomas; Tabelow, KarstenMathematical modeling and simulation (MMS) has now been established as an essential part of the scientific work in many disciplines and application areas. It is common to categorize the involved numerical data and to some extend the corresponding scientific software as research data. Both have their origin in mathematical models. In this contribution we propose a holistic approach to research data in MMS by including the mathematical models and discuss the initial requirements for a conceptual data model for this field.
- ItemEvolution statt Revolution bei der Forschungsdatenpublikation! - Das Projekt "FoHop!" an der TIB(Reutlingen : Berufsverband Information Bibliothek e. V., 2024) Engelhardt, Olga; Gugat, Petra; Renziehausen, Anna-KarinaAn der Leibniz Universität Hannover (LUH) werden jährlich ca. 390 Dissertationen eingereicht, aber nur ca. 5 % der Promovierenden veröffentlichen auch zugehörige Forschungsdaten - und diese erfüllen in den seltensten Fällen die FAIR-Kriterien. Da dies jedoch Element Guter Wissenschaftlicher Praxis ist und zunehmend von Forschungsförderung und Hochschulen gefordert wird, startete die Technische Informationsbibliothek (TIB) 2022 das Projekt "Forschungsdaten von Hochschulschriften publizieren (FoHop!)". Ziel des Projekts ist die Verbesserung der Auffindbarkeit und Nachnutzung von Forschungsdaten, die zu Dissertationen gehören. In unserem Beitrag teilen wir mit Ihnen unsere "lessons learned" - und berichten von ursprünglichen Plänen, pragmatischen Entscheidungen und erarbeiteten Workflows, um Ihnen Anregungen für ähnliche Projekte an anderen Universitäten/Bibliotheken zu geben oder bereits laufenden Projekten den einen oder anderen Stolperstein zu ersparen.
- ItemReproducible research through persistently linked and visualized data(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2017) Drees, Bastian; Kraft, Angelina; Koprucki, ThomasThe demand of reproducible results in the numerical simulation of opto-electronic devices or more general in mathematical modeling and simulation requires the (long-term) accessibility of data and software that were used to generate those results. Moreover, to present those results in a comprehensible manner data visualizations such as videos are useful. Persistent identifier can be used to ensure the permanent connection of these different digital objects thereby preserving all information in the right context. Here we give an overview over the state-of-the art of data preservation, data and software citation and illustrate the benefits and opportunities of enhancing publications with visual simulation data by showing a use case from opto-electronics.