Kompetenzzentrum Forschungsdaten an der Universität Bielefeld - Poster

Vompras J. Kompetenzzentrum Forschungsdaten an der Universität Bielefeld - Poster. Presented at the Data Science Day (23.05.2018), Universität Bielefeld.Dieses Poster wurde an dem Bielefelder Data Science Day (23.05.2018) präsentiert. Es diente vor allem als Blickfang zum Thema Forschungsdatenmanagement an der Universität Bielefeld und zur Bekanntmachung des neu gegründeten Kompetenzzentrums. Das Poster kann/soll beliebig zu Werbe- und Informationszwecken, bei denen es darauf ankommt, die Supportdienstleistungen des Kompetenzzentrums prominent ins Licht zu rücken (z.B. bei Begehungen der Sonderforschungsbereiche oder FDM-relevanten Fachtagungen), nachgenutzt werden. Das Poster kann/soll es im Sinne von Openness an der Universität nachgenutzt und verbreitet werden. Die digitale Version ist unter der Creative Commons Namensnennung 4.0 International Lizenz (CC-BY 4.0) über PUB veröffentlicht (Zitationsempfehlung s. unten, Stil: APA)

Using DKAN a - an Open Source Portal Solution for Publishing Social Science Data: Lessons learnt at DSZ-BO

Vompras J. Using DKAN a - an Open Source Portal Solution for Publishing Social Science Data: Lessons learnt at DSZ-BO. Presented at the EDDI15 – 7th Annual European DDI User Conference, Copenhagen

Repository workflow for interlinking research data with grey literature

Vompras J, Schirrwagen J. Repository workflow for interlinking research data with grey literature. In: 8th Conference on Grey Literature and Repositories. 8th Conference on Grey Literature and Repositories. Vol 12. Prague: National Library of Technology; 2015: 21-28.Publishing data is more and more considered as part of the research process. While funder mandates and journal policies demand the disclosure of research data at the time of article publication there is still a lack of guidelines and workflows to reference data from grey literature. Based on multidisciplinary examples found in our repository “PUB” we present a user friendly generalized framework for interlinking research data with grey literature. This way, we are not only increasing the number of “grey” non-textual research outputs – including data publications – but also foster awareness of its sharing and re-use in scientific communities

Using RDF to Describe and Link Social Science Data to Related Resources on the Web

Kramer S, Leahey A, Southall H, Vompras J, Wackerow J. Using RDF to Describe and Link Social Science Data to Related Resources on the Web. DDI Working Paper Series. Dagstuhl, Germany: DDI Alliance; 2012.This document focuses on how best to relate Resource Description Framework (RDF)-described datasets to other related resources and objects (publications, geographies, organizations, people, etc.) in the Semantic Web. This includes a description of what would be needed to make these types of relationships most useful, including which RDF vocabularies should be used, potential link predicates, and possible data sources. RDF provides a good model for describing social science data because it supports formal semantics that provide a dependable basis for reasoning about the meaning of an RDF expression. In particular, it supports defined notions of entailment which provide a basis for defining reliable rules of inference in RDF data. Our findings are discussed in the context of social science data and more specifically, how to leverage existing metadata models to use alongside linked data. We provide a case for leveraging the Data Documentation Initiative (DDI) to enable semantic linking of social science data to other data and related resources on the Web. This document is organized into five use cases, which we consider in turn. Use cases include: linking related publications to data, linking data about people and organizations to research data, linking geography, linking to related studies, and linking data to licenses. We briefly discuss emerging or known issues surrounding the potential use of linked data within each of the defined use cases. Following these, we list more topics that could develop into additional use cases. Appendix A lists elements from the DDI-Codebook and DDI-Lifecycle specifications that are relevant to each use case

Forschungsdatenmanagement – Uniweite Angebote für Wissenschaft und Forschung

Forschungsdatenmanagement (FDM) bedeutet, die im Rahmen eines Forschungsprojektes anfallenden Daten methodisch-systematisch zu erheben, zu organisieren, zu bewahren, zu dokumentieren und nachnutzbar zu machen. Die Universität Bielefeld hat mit dem Kompetenzzentrum Forschungsdaten verbindliche Unterstützungsdienstleistungen zu allen Bereichen des „Data Life Cycle“ etabliert. In diesem Vortrag werden sowohl Einblicke in die lokale Arbeit rund um das Forschungsdatenmanagement gegeben, aber auch bestehende Vernetzungsaktivitäten mit relevanten Stakeholdern präsentiert. Des Weiteren wird das „Servicezentrum Medical Data Science“ der Medizinischen Fakultät seine fachspezifischen FDM-Supportstrukturen und deren Einbettung in die hochschulweite Infrastruktur vorstellen

Replicability and comprehensibility of social research and its technical implementation

"This paper is a contribution to the methodological and technical discussion of social research infrastructure. The main question is how to store and manage data in a way that meets the increasing demand for secondary data analysis in both quantitative and qualitative social science research. The first two sections focus mainly on aspects of data documentation, in particular on the unification of various documentation requirements that have arisen across ongoing projects of the SFB 882. While the aim of documenting quantitative research processes is to ensure replicability, the aim of documenting qualitative projects is to maintain the understandability and informative value of research data. In the third section a virtual research environment (VRE) is presented that provides both a generic work platform and a project-specific research platform. The work platform bundles IT resources by bringing together various tools for administration, project management, and time- and location-independent collaboration in a single environment adapted to researchers' specific work processes. The research component combines data management with further developments in social science methodologies. It provides services for the archiving and reuse of data and enables the infrastructural and methodological coordination of data documentation. We also introduce a documentation scheme for qualitative and quantitative social research within the SFB 882. This scheme considers the specific requirements of research projects within the SFB, such as different methods (e.g. panel analysis, experimental approaches, ethnography, and interview research), project work, and requirements of longterm research." (author's abstract

Expanding the research data management service portfolio at Bielefeld University according to the three-pillar principle towards data FAIRness

Schirrwagen J, Cimiano P, Ayer V, et al. Expanding the research data management service portfolio at Bielefeld University according to the three-pillar principle towards data FAIRness. Presented at the Göttingen-CODATA RDM Symposium 2018, Göttingen

Expanding the Research Data Management Service Portfolio at Bielefeld University According to the Three-pillar Principle Towards Data FAIRness

Schirrwagen J, Cimiano P, Ayer V, et al. Expanding the Research Data Management Service Portfolio at Bielefeld University According to the Three-pillar Principle Towards Data FAIRness. Data Science Journal. 2019;18(1): 6.Research Data Management at Bielefeld University is considered as a cross-cutting task among central facilities and research groups at the faculties. While initially started as project “Bielefeld Data Informium” lasting over seven years (2010–2015), it is now being expanded by setting up a Competence Center for Research Data. The evolution of the institutional RDM is based on the three-pillar principle: 1. Policies, 2. Technical infrastructure and 3. Support structures. The problem of data quality and the issues with reproducibility of research data is addressed in the project Conquaire. It is creating an infrastructure for the processing and versioning of research data which will finally allow publishing of research data in the institutional repository. Conquaire extends the existing RDM infrastructure in three ways: with a Collaborative Platform, Data Quality Checking, and Reproducible Research