Research data management: a conceptual framework

Patel, Dimple

doi:10.1108/lr-01-2016-0001

Cited by 47 publications

(61 citation statements)

References 9 publications

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“…This paper addressed several issues related to research data services, providing a patchy picture that intended to give insight into several essential and intriguing questions. None of the answers to these is final, but -even if slowly -researchers gradually will have to get accustomed to publishing not only manuscripts, but making research data open (Patel, 2016). Academic libraries may take the lead in this process, or -at least -it is necessary for them to reflect on new developments by filling the niches that appear in services, provided for researchers by other academic units.…”

Section: Resultsmentioning

confidence: 99%

“…Examining the quality of research data also should mean considering relevance to the given research question and format that comprises the fields and units used, naming conventions, dates of creation and update (Zilinski & Nelson, 2014). Issues, related to copyright, data licensing, data security, data privacy and to the danger of the erroneous interpretation of data also are tied to quality (Patel, 2016).…”

Section: Rds Conceptsmentioning

confidence: 99%

“…The authenticity and trustworthiness of the data is reaffirmed by usage. Data sharing also saves time because the researchers who reuse data do not have to "reinvent" many of the results already attained (Patel, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Beyond Literacies: The Evolving Landscape of Library Support to Research 2.0

Koltay

2017

ZIN

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PURPOSE/THESIS: This paper identifies some of the tasks and roles that academic libraries have to perform in order to react to the emergence of Research 2.0. APPROACH/METHODS: The argument is based on a non-exhaustive review of the recent literature. RESULTS AND CONCLUSIONS: Academic libraries should respond to the emergence of Research 2.0 by filling niches in services provided by other academic units. RESEARCH LIMITATIONS: As a rule, only the literature of the second half of the 2010s was taken into consideration. PRACTICAL IMPLICATIONS: The tasks identified in this paper may not seem urgent today, but the likelihood that they will become an imperative in the future is high. ORIGINALITY/VALUE: The issues identified in this paper are already a part of everyday best practices in several countries.

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Section: Resultsmentioning

confidence: 99%

Section: Rds Conceptsmentioning

confidence: 99%

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Beyond Literacies: The Evolving Landscape of Library Support to Research 2.0

Koltay

2017

ZIN

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“…• The ownership of the copyright and other intellectual property rights in OS data is a critical issue [17].…”

Section: Open Science Data Characteristics and Categoriesmentioning

confidence: 99%

The Uptake of Open Science: Mapping the Results of a Systematic Literature Review

et al. 2020

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This paper contributes to “Open Science” theory, with a specific focus on Open Science data generated by scholars. To this end, a mixed- method systematic literature review, including science mapping techniques, was conducted. Our preliminary results reveal the potential of Open Science as a domain for interdisciplinary research. A keyword co-occurrence network analysis using the VOSviewer visualisation tool identified five clusters of interrelated sub-concepts within Open Science research. The key distinctive characteristics and the various categories of Open Science data have been identified. The relevant data platforms have been provided to exemplify each category of Open Science data. Finally, a distinction between Open Science data and Open Government data was explored and the convergence point between them was presented.

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“…In order to implement RDM, the university needs to classify the data to ensure what data can be shared widely. For instance, Duke University classifies data based on the risk level: sensitive (high), restricted (medium), and public (low) [15], while Western University uses the following group: confidential, sensitive, and public [16,17]. These classifications are based on the existing law and regulation.…”

Section: Fig 3 the Barriers Of Data Sharing From Indonesia's Point Omentioning

confidence: 99%

How to Extend your Data Lifetime: Research Data Management in Indonesia’s Context

Irawan¹,

Darujati²,

Soebandhi³

et al. 2019

Proceedings of the 1st International Conference on Life, Innovation, Change and Knowledge (ICLICK 2018)

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Data is the basis of research. On the other side, the world has a problem of replication. The first problem is we don't really know how to manage our own data to able to reanalyze it at some point after the research has been finished. The lifetime of data is very short, in only one or two fiscal years. In this article we will describe on how to write a research data management in order to extend the lifetime of data. There are seven basic components to remember before writing a proper research data management: (1) Data storage and software, (2) Metadata, (3) Structure, (4) Persistent link, (5) Licensing, (6) Data maintainer, (7) Indexing. In several fields, including medicine, an anomyzation strategy will be needed. We also need to put into account the Intellectual Property Rights and data ownership in to the equation, as Indonesian scientists are not properly exposed to those subjects.

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Research data management: a conceptual framework

Cited by 47 publications

References 9 publications

Beyond Literacies: The Evolving Landscape of Library Support to Research 2.0

Beyond Literacies: The Evolving Landscape of Library Support to Research 2.0

The Uptake of Open Science: Mapping the Results of a Systematic Literature Review

How to Extend your Data Lifetime: Research Data Management in Indonesia’s Context

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