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Auflistung nach Schlagwort "Research Data Management"

1 - 8 von 8
  • Position paper
    From Receiving to Characterizing: Improving Training Strategies for Research Data/Software Management by another domain!
    (Proceedings of DELFI 2024, 2024) Bernoth, Jan; Riedel, Christian; Wiepke, Axel; Laban, Firas Al
    Constructing learning objectives for Research Data and Software Management (RDSM) primarily focuses on the cognitive domain of Bloom’s taxonomy. However, surveys exploring why RDSM is not fully implemented across all research fields have revealed that the issue extends beyond a mere lack of knowledge. It appears that researchers also lack inner conviction and are not open to RDSM practices. To address this, we aim to initiate a discussion through this position paper on incorporating another dimension of Bloom’s Taxonomy into training competencies: the affective domain. Considering emotional factors might help persuade individuals to receive and respond to information more effectively. By integrating the affective domain, we suggest that a shift in learning strategies and activities from purely cognitive objectives in RDSM could be beneficial.
  • Workshopbeitrag
    An Interdisciplinary Perspective on Managing Process Model Simulation Data
    (Modellierung 2024 Satellite Events, 2024) Schreiber, Clemens; Schiefer, Gunther; Oberweis, Andreas
    Managing process model simulation data in a findable, accessible, interoperable, and reusable way faces several challenges. We discuss three main challenges of simulation data documen- tation, which we encountered in an interdisciplinary research project on research data management. These challenges refer to (1) the definition of a discipline-specific metadata model, (2) the development of user support to ensure metadata quality and (3) the interlinking of the simulation data across research areas. Based on the identified challenges and the reported insights gained from the research project, we provide novel ideas for the development of process model simulation tools, enabling a better documentation of simulation data in the future.
  • Zeitschriftenartikel
    Intra-consortia data sharing platforms for interdisciplinary collaborative research projects
    (it - Information Technology: Vol. 62, No. 1, 2020) Schröder, Max; LeBlanc, Hayley; Spors, Sascha; Krüger, Frank
    As the importance of data in today’s research increases, the effective management of research data is of central interest for reproducibility. Research is often conducted in large interdisciplinary consortia that collaboratively collect and analyse such data. This raises the need of intra-consortia data sharing. In this article, we propose the use of data management platforms to facilitate this exchange among research partners. Based on the experiences of a large research project, we customized the CKAN software to satisfy these needs for intra-consortia data sharing.
  • Konferenzbeitrag
    RDM4MOD: Working Workshop on Research Data Management in Modelling in Computer Science
    (Modellierung 2022 Satellite Events, 2022) Goedicke, Michael; Lucke, Ulrike
    The demand increases to substantiate the claims made in the scientific processes in the realm of modeling in all areas of computer science. Thus, publications, funding proposals etc. require more often that empirical data (if available) along with the related context of experiments and the artifacts in terms of descriptions, software and other tools will be part of the publication or proposed project as well. Infrastructure will be provided to store and make available this kind of research data according to the FAIR – principles (Findable, Accessible, Interoperable, Reusable) as part of the National Research Data Infrastructure. Within this workshop, the consortium NFIDxCS collected requirements as well as existing approaches to build such an infrastructure with a special focus on modeling issues.
  • Textdokument
    Research Data Management in Computer Science - NFDIxCS Approach
    (INFORMATIK 2022, 2022) Goedicke,Michael; Lucke,Ulrike
    This contribution discusses the challenges and architectural considerations for Research Data Management in computer science and related infrastructure for implementing the so-called FAIR principles. The main challenge is, to cover the research data management requirements of the various sub disciplines of computer science. This diversity must be managed in a uniform way which entails a common structure for this task. We outline these requirements briefly and discuss then the concept of the so-called research data management container (RDMC) which encapsulates a given research data set in conjunction with all accompanying information and support (software, execution environment etc) in order to provide a portable unit for management, distribution and access control.
  • Konferenzbeitrag
    Towards the Establishment of Data Science Centers at Higher Education Institutions
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Putzke, Johannes; Schimmer, Thomas; Richter, Manuela; Hamel, Lucas
    Numerous Higher Education Institutions (HEI) are considering the launch of data science centers, partly induced by a call from the German Federal Ministry of Education and Research (BMBF). However, data science centers at HEIs substantially differ from their counterparts in in- dustry. In this brief paper, we highlight some of these differences and identify a challenge in setting up data science centers at HEIs using the case of an initiative at eight universities of applied sciences in Germany.
  • Textdokument
    Tracing the History of the Baltic Sea Oxygen Level
    (BTW 2021, 2021) Auge, Tanja; Heuer, Andreas
    In order to guarantee the reproducibility of research results, large research communities, conferences and journals increasingly demand the provision of original research data. Since this is often not possible or desired, a certain tact and sensitivity is needed. With our method, combining provenance and evolution, we can identify the source tuples necessary for the reconstruction of a query result also in temporal databases. To avoid dirty data caused by the inverse evolution, we introduced the what-provenance, which remembers the data types of the source relation.
  • Konferenzbeitrag
    Utilizing Personas to Create Infrastructures for Research Data and Software Management
    (INFORMATIK 2024, 2024) Bernoth, Jan; Al Laban, Firas; Lucke, Ulrike
    Personas are often used in requirements engineering to analyze how people from target groups could use prospective systems or services. Recently, the personas approach also gained some popularity for demonstration and marketing purposes. While the usefulness of personas is uncontested, it is not always clear to which extent the target group is covered and which relevant perspectives are still missing. To address this problem, we present a systematic approach to gain a structured analysis of the personas used in a complex development process. The overall goal of this development is to provide an infrastructure for the management of research data and research software using a containerized approach. We applied the FAIR Ecosystem model with its components and relevant stakeholder groups as a basis of the persona construction, and we illustrate how we used this method to categorize the created personas and to identify blind spots. Not every role is currently defined at all institutions – but if they were, how would they shape research at universities in 2034?