Research data management in academic institutions: A scoping review

Perrier, Laure; Blondal, Erik; Ayala, Ana Patricia; Dearborn, Dylanne; Kenny, Tim; Lightfoot, David; Reka, Roger; Thuna, Mindy; Trimble, Leanne; MacDonald, Heather

doi:10.1371/journal.pone.0178261

Cited by 54 publications

(49 citation statements)

References 23 publications

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“…The articles referenced above and many others share a method of grouping stakeholders by professional or institutional identity. Perrier et al (2017), for instance, divide stakeholders into professional groups (researchers, administrators, participants, and funders) in their survey of research data management (p. 9). Another formulation of stakeholders is possible, in that those who work in scholarly communications could define themselves by the metadata roles they have in common.…”

Section: Methodsmentioning

confidence: 99%

A literature review of scholarly communications metadata

Gregg¹,

Erdmann²,

Paglione³

et al. 2019

RIO

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The purpose of this literature review is to identify the challenges, opportunities, and gaps in knowledge with regard to the use of metadata in scholarly communications. This paper compiles and interprets literature in sections based on the professional groups, or stakeholders, within scholarly communications metadata: researchers, funders, publishers, librarians, service providers, and data curators. It then ends with a 'bird's eye view' of the metadata supply chain which presents the network of relationships and interdependencies between stakeholders. This paper seeks to lay the groundwork for new approaches to present problems in scholarly communications metadata. KeywordsScholarly communication, metadata, metadata supply chain, research data management, Metadata 2020 ‡ § | ¶ # This review assumes at least a basic knowledge of how metadata records are created, shared, and used. Nevertheless, two core terms are defined below to frame the discussion.Scholarly communication is the process by which research is conducted, transformed into content, and distributed to a wider audience. The majority of the resources featured in this literature review are concerned with the physical and life sciences. The implications of this review, however, can be extended in many cases to the social sciences and humanities which have the same need to describe and share scholarly works. The majority of resources also treat the published journal article as the primary form of scholarly output, though emerging literature increasingly focuses on research data and the research process as opposed to finalized articles.Metadata in this context is the information that accompanies the various stages and outputs of research. Common to most scholarly research are metadata elements such as author, date, title, subject, language, and standard identifier. In the case of research data, metadata describes specialized aspects such as the geographic location where the data was collected, the name and identifier of the research funder, the institutional affiliation of the researchers, contributors such as editors and data curators, or the number of the grant award funding the research (DataCite Metadata Working Group 2016). Metadata for journal article includes identifiers such as the International Standard Serial Number (ISSN, the numeric identifier assigned to journals), volume, issue, page numbers, or epub identifier. In the case of monographs (books), specialized metadata includes the International Standard Book Number (ISBN), title, page number, and author of individual chapters. These and other metadata elements are essential for disseminating research outputs. Metadata gives proper credit to research funders, ensures that research can be reproduced, facilitates others in finding research, allows authors to build a portfolio, and measures the scholarly impact of an article. StakeholdersPublishers, service providers, researchers, funders, librarians, and data curators are the stakeholders of metadata in scholarly communication. The relationships ...

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Section: Methodsmentioning

confidence: 99%

A literature review of scholarly communications metadata

Gregg¹,

Erdmann²,

Paglione³

et al. 2019

RIO

View full text Add to dashboard Cite

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“…The collection method does not guarantee completeness, which means there might be research data lifecycle models not captured by our analysis. Since [2] already provides a scoping review of the relevant literature this is an acceptable Figure 1: Publication types of found research data management models/lifecycles defect. Our approach was focused on finding criteria to compare data lifecycle models to each other and to evaluate the fitness of lifecycle models in general for certain purposes, which does not necessitate completeness.…”

Section: Threats To Validitymentioning

confidence: 99%

“…Most of these models break down the phenomena of research data management into a series of tasks or states of data and relate them to different roles or actors. As [2] indicates, these models are often not evaluated in a manner that allows to reproducibly derive the same model for a certain purpose (explaining, educating, etc.). A model un-evaluated is, scientifically speaking, of doubtful quality.…”

mentioning

confidence: 99%

Methods to Evaluate Lifecycle Models for Research Data Management

Weber

Kranzlmüller

2019

Bibliothek Forschung Und Praxis

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Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.Lebenszyklus-Modelle für Forschungsdaten sind oft abstrakt und einfach. Hierin liegt die Gefahr, ein zu einfaches Bild der komplexen Forschungsdaten-Landschaft zu zeichnen. Die Analyse von 90 dieser Modelle führt zu zwei Ansätzen, die Qualität dieser Modelle zu bewerten. Die Uneinheitlichkeit in der Terminologie erschwert einen direkten Vergleich zwischen den Modellen, wohingegen eine empirische Evaluierung der Modelle in Reichweite liegt.

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“…While useful tools for assessing needs for institutional research data services, they lack a mechanism to collect feedback on researchers' current practices for these treatments and assessment of their satisfaction for these treatments. A scoping review of 310 articles by Perrier et al (2017) found that most research data management studies performed by academic institutions do not include direct interaction with data producers but instead rely on indirect methods such as self-reporting surveys and case studies by a third-party observer. Jahnke, Asher, and Keralis's 2012 CLIR study, however, does approach researcher attitudes directly via their method of ethnographic interviews with social sciences researchers at five institutions.…”

Section: Literature Reviewmentioning

confidence: 99%