How to Design a Research Data Management Platform? Technical, Organizational and Individual Perspectives and Their Relations

Hofeditz, Lennart; Roß, Björn; Wilms, Konstantin; Rother, Marius; Rehwald, Stephanie; Brenger, Bela; López, Ania; Vogl, Raimund; Rudolph, Dominik

doi:10.1007/978-3-030-50017-7_23

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…Buys and Shaw, 2015; Jones, 2014; Joo and Peters, 2020; Pinfield et al, 2014; Yu et al, 2017). The study sample underlined that these services need to be user-friendly and should be oriented on the needs (Eifert et al, 2016; Hofeditz et al, 2020; Palumbo et al, 2015) which emphasizes the strong interrelation between the components “structure” and “people” of Leavitt’s model.…”

Section: Resultsmentioning

confidence: 99%

“…Due to a limited budget, it is necessary to consider the needs of the scientists (Renwick et al, 2017; as well as to identify existing resources and collaborations (Coates, 2014). RDM-services can increase the acceptance of the RDMS within the HEI (Plomp et al, 2019) but they need to be user-friendly (Hofeditz et al, 2020;Palumbo et al, 2015).…”

Section: Policies/guidelinesmentioning

confidence: 99%

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Research data management systems and the organization of universities and research institutes: A systematic literature review

Donner

2022

Journal of Librarianship and Information Science

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New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research.

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Section: Resultsmentioning

confidence: 99%

Section: Policies/guidelinesmentioning

confidence: 99%

Research data management systems and the organization of universities and research institutes: A systematic literature review

Donner

2022

Journal of Librarianship and Information Science

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“…Relevant research on government data openness has been carried out for many years, and the research contents show a diversified trend, including concept discrimination [13], data opening practices [14,15], policies and regulations [16], institutional mechanisms [17], standards and norms [18], privacy protection [19], collaborative research between data openness and e-government [20], and smart cities, etc. There are few studies focusing on the impact of data openness.…”

Section: Literature Reviewmentioning

confidence: 99%

Could Government Data Openness Enhance Urban Innovation Capability? An Evaluation Based on Multistage DID Method

2021

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The wave of government data opening has gradually swept the world since it rose from the United States in 2009. The purpose is not to open government data, but to release data value and drive economic and social development through data accessibility. At present, the impact of academic circles on government open data mostly stays in theoretical discussion, especially due to the lack of empirical tests. Using the multistage difference-in-difference (DID) model, this paper analyzes the panel data from 2009 to 2016 by taking two batches of Chinese cities with open data released in 2014 and 2105 as samples to test the impact of government data opening on urban innovation ability. The results show that the opening of government data significantly improves urban innovation abilities. After considering the heterogeneity and fixed effects of urban characteristics, the opening of government data still significantly improves urban innovation ability and shows a greater innovation driving role in cities with high levels of economic development, human capital, and infrastructure. Based on this, this paper believes that we should continue to promote the opening of government data, release the value of data, and pay attention to the Matthew effect between cities that may appear in the era of big data.

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How to Design a Research Data Management Platform? Technical, Organizational and Individual Perspectives and Their Relations

Cited by 2 publications

References 25 publications

Research data management systems and the organization of universities and research institutes: A systematic literature review

Research data management systems and the organization of universities and research institutes: A systematic literature review

Could Government Data Openness Enhance Urban Innovation Capability? An Evaluation Based on Multistage DID Method

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