Dataset Retrieval: Informationsverhalten von Datensuchenden und das Ökosystem von Data-Retrieval-Systemen

Verschiedene Stakeholder fordern eine bessere Verfügbarkeit von Forschungsdaten. Der Erfolg dieser Initiativen hängt wesentlich von einer guten Auffindbarkeit der publizierten Datensätze ab, weshalb Dataset Retrieval an Bedeutung gewinnt. Dataset Retrieval ist eine Sonderform von Information Retrieval, die sich mit dem Auffinden von Datensätzen befasst. Dieser Beitrag fasst aktuelle Forschungsergebnisse über das Informationsverhalten von Datensuchenden zusammen. Anschließend werden beispielhaft zwei Suchdienste verschiedener Ausrichtung vorgestellt und verglichen. Um darzulegen, wie diese Dienste ineinandergreifen, werden inhaltliche Überschneidungen von Datenbeständen genutzt, um den Metadatenaustausch zu analysieren.Various stakeholders are calling for better availability of research data. The success of these initiatives depends largely on good discoverability of published datasets, which is why Dataset Retrieval is gaining in importance. Dataset Retrieval is a special form of Information Retrieval that is concerned with finding datasets. This paper summarizes recent research on the information behavior of data users. Subsequently, two search services with different objectives are presented and compared. In order to show how these services interconnect, overlaps in content are used to analyze metadata exchange between them.Peer Reviewe

Data Quality Assurance at Research Data Repositories

This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories. The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses. The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse. The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance

Bedarfsgesteuerte Entwicklung einer Forschungsdateninfrastruktur am Beispiel des generischen Repositoriums RADAR

RADAR[1] (Research Data Repository) bietet akademischen Einrichtungen und Forschenden eine generische Infrastruktur zur langfristigen Archivierung und Publikation digitaler Forschungsdaten. Das Forschungsdatenrepositorium wird von FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur [2] betrieben. Die DINI/nestor-AG Forschungsdaten [3] lud im Januar 2019 im Rahmen der Workshop-Reihe "FDM am Standort: von der initialen Idee zum dauerhaften Service" in Kooperation mit dem Projekt UNEKE [4] der Universitätsbibliothek Duisburg-Essen und dem IT Center der RWTH Aachen [5] zum Workshop "Bedarfserhebungen - Grundlage für passgenaue Infrastrukturen?" ein. Dort wurde die bedarfsgesteuerte Entwicklung von RADAR während der Projektphase und im Produktivbetrieb vorgestellt. Darüber hinaus wurde über die grundlegenden Funktionalitäten und Dienstmerkmale der Forschungsdateninfrastruktur sowie über Pläne für deren zukünftige Weiterentwicklung informiert. [1] https//www.radar-service.eu (geprüft: 13.09.2019) [2] https://www.fiz-karlsruhe.de (geprüft: 13.09.2019) [3] https://dini.de/ag/dininestor-ag-forschungsdaten/ (geprüft: 13.09.2019) [4] https://uneke.de (geprüft: 13.09.2019) [5] http://www.itc.rwth-aachen.de (geprüft: 13.09.2019)  

Disappearing repositories -- taking an infrastructure perspective on the long-term availability of research data

Currently, there is limited research investigating the phenomenon of research data repositories being shut down, and the impact this has on the long-term availability of data. This paper takes an infrastructure perspective on the preservation of research data by using a registry to identify 191 research data repositories that have been closed and presenting information on the shutdown process. The results show that 6.2 % of research data repositories indexed in the registry were shut down. The risks resulting in repository shutdown are varied. The median age of a repository when shutting down is 12 years. Strategies to prevent data loss at the infrastructure level are pursued to varying extent. 44 % of the repositories in the sample migrated data to another repository, and 12 % maintain limited access to their data collection. However, both strategies are not permanent solutions. Finally, the general lack of information on repository shutdown events as well as the effect on the findability of data and the permanence of the scholarly record are discussed

Das Versprechen der Vernetzung der NFDI

Das Versprechen der Vernetzung der NFDI ist ein Veranstaltungsbeitrag. Er stellt wesentliche Ergebnisse aus dem Workshop Visualisierung von Netzwerken auf dem 2. Community Meeting des Konsortiums NFDI4Ing vor und zeichnet nach, wie die Visualisierung des Versprechens der Vernetzung durch die Diskussionen mit den Teilnehmenden des Workshops zu neuen Erkenntnissen und Fragestellungen führte

re3data – Indexing the Global Research Data Repository Landscape Since 2012

For more than ten years, re3data, a global registry of research data repositories (RDRs), has been helping scientists, funding agencies, libraries, and data centers with finding, identifying, and referencing RDRs. As the world’s largest directory of RDRs, re3data currently describes over 3,000 RDRs on the basis of a comprehensive metadata schema. The service allows searching for RDRs of any type and from all disciplines, and users can filter results based on a wide range of characteristics. The re3data RDR descriptions are available as Open Data accessible through an API and are utilized by numerous Open Science services. re3data is engaged in various initiatives and projects concerning data management and is mentioned in the policies of many scientific institutions, funding organizations, and publishers. This article reflects on the ten-year experience of running re3data and discusses ten key issues related to the management of an Open Science service that caters to RDRs worldwide

re3data – Indexing the Global Research Data Repository Landscape Since 2012

The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.For more than ten years, re3data, a global registry of research data repositories (RDRs), has been helping scientists, funding agencies, libraries, and data centers with finding, identifying, and referencing RDRs. As the world’s largest directory of RDRs, re3data currently describes over 3,000 RDRs on the basis of a comprehensive metadata schema. The service allows searching for RDRs of any type and from all disciplines, and users can filter results based on a wide range of characteristics. The re3data RDR descriptions are available as Open Data accessible through an API and are utilized by numerous Open Science services. re3data is engaged in various initiatives and projects concerning data management and is mentioned in the policies of many scientific institutions, funding organizations, and publishers. This article reflects on the ten-year experience of running re3data and discusses ten key issues related to the management of an Open Science service that caters to RDRs worldwide.Peer Reviewe

Report on re3data COREF / CoreTrustSeal Workshop on Quality Management at Research Data Repositories

On October 5, 2022, the “Workshop on Quality Management at Research Data Repositories” – jointly organized by re3data COREF and CoreTrustSeal – was held online with more than 70 participants attending. The objective of the workshop was to discuss activities research data repositories perform to assure, assess, and improve data quality.The workshop started with input from the workshop organizers: re3data COREF presented results of a recent suvey on quality management at repositories, and CoreTrustSeal shared the perspective of a certification organization. Then, repositories from different disciplinary backgrounds presented their approaches to quality management. The workshop concluded with a breakout session and a plenary discussion on options for making information on data quality assurance more visible

Quantitative assessment of metadata collections of research data repositories

Structured metadata are of particular importance in the context of facilitating research data (re-)use. Although research data repositories create and manage metadata records, existing research offers limited insights into the relationship between repositories and metadata for research data. Therefore, in conducting a quantitative assessment informed by metadata quality requirements, this thesis aims at making distinctive features of metadata for research data visible, specifying the potential influence of repository characteristics on metadata, and exploring changes to metadata records. The analysis showed variations in metadata completeness across repositories. Within repositories, metadata descriptions are relatively homogenous. These findings suggest that repositories have developed distinctive and consistent practices for describing data. On average, descriptions comprise 487.3 characters, and 5.52 years passed between the year a dataset was published and the metadata record was registered. Differences in the completeness of metadata records, description length and timeliness were significant across repository types and certification status, whereas differences in collection homogeneity were not significant. Overall, most metadata records in the sample were changed, which conforms with the conceptualization of metadata for research data as dynamic and changeable objects. Differences in the number of changes are significant across repository types

Nutzung der Schattenbibliothek Sci-Hub in Deutschland

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