Assessing the Impact and Quality of Research Data Using Altmetrics and Other Indicators
Stacy Konkiel
- Vol. 2, Iss: 1
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TLDR
The research into research data metrics, these metrics’ strengths and limitations with regard to formal evaluation practices, and the possible meanings of such indicators are discussed, and heuristics for policymakers and evaluators interested in doing so are suggested.Abstract:
Research data in all its diversity—instrument readouts, observations, images, texts, video and audio files, and so on—is the basis for most advancement in the sciences. Yet the assessment of most research programmes happens at the publication level, and data has yet to be treated like a first-class research object. How can and should the research community use indicators to understand the quality and many potential impacts of research data? In this article, we discuss the research into research data metrics, these metrics’ strengths and limitations with regard to formal evaluation practices, and the possible meanings of such indicators. We acknowledge the dearth of guidance for using altmetrics and other indicators when assessing the impact and quality of research data, and suggest heuristics for policymakers and evaluators interested in doing so, in the absence of formal governmental or disciplinary policies. Policy highlights Research data is an important building block of scientific production, but efforts to develop a framework for assessing data’s impacts have had limited success to date. Indicators like citations, altmetrics, usage statistics, and reuse metrics highlight the influence of research data upon other researchers and the public, to varying degrees. In the absence of a shared definition of “quality”, varying metrics may be used to measure a dataset’s accuracy, currency, completeness, and consistency. Policymakers interested in setting standards for assessing research data using indicators should take into account indicator availability and disciplinary variations in the data when creating guidelines for explaining and interpreting research data’s impact. Quality metrics are context dependent: they may vary based upon discipline, data structure, and repository. For this reason, there is no agreed upon set of indicators that can be used to measure quality. Citations are well-suited to showcase research impact and are the most widely understood indicator. However, efforts to standardize and promote data citation practices have seen limited success, leading to varying rates of citation data availability across disciplines. Altmetrics can help illustrate public interest in research, but availability of altmetrics for research data is very limited. Usage statistics are typically understood to showcase interest in research data, but infrastructure to standardize these measures have only recently been introduced, and not all repositories report their usage metrics to centralized data brokers like DataCite. Reuse metrics vary widely in terms of what kinds of reuse they measure (e.g. educational, scholarly, etc). This category of indicator has the fewest heuristics for collection and use associated with it; think about explaining and interpreting reuse with qualitative data, wherever possible. All research data impact indicators should be interpreted in line with the Leiden Manifesto’s principles, including accounting for disciplinary variation and data availability. Assessing research data impact and quality using numeric indicators is not yet widely practiced, though there is generally support for the practice amongst researchers.read more
Citations
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The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles
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Are data repositories fettered? A survey of current practices, challenges, and future technologies
TL;DR: This study identifies the current challenges and needs for improving data repository functionalities and user experiences, and makes main recommendations for future repository systems.
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The data-index: An author-level metric that values impactful data and incentivizes data sharing
TL;DR: Data-index as mentioned in this paper is a new author-level metric, which values both dataset output (number of datasets) and impact(number of data-index citations), so promotes generating and sharing data as a result.
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Evaluación de la investigación científica: mejorando las políticas científicas en Latinoamérica
TL;DR: A revisión bibliográfica, basada en artículos sobre políticas de evaluación de la investigación científica and agendas internacionales implementadas en los últimos años (principalmente en el Reino Unido, Estados Unidos, Australia, China and Latinoamérica) as mentioned in this paper , indicate que no existe un solo método de evalueación, and that ningún indicador es absoluto.
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Istraživački podatci hrvatskih autora na platformi Web of Science
TL;DR: Istraživački podatci (engl. research data) as discussed by the authors, i.e., istraživanjem, prikupljaju and bilježe tijekom eksperimenta, promatranja, modeliranja, intervjua, and sl., are the most common types of prikopljaje.
References
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Do altmetrics work for assessing research quality
Andrea Giovanni Nuzzolese,Paolo Ciancarini,Aldo Gangemi,Silvio Peroni,Francesco Poggi,Valentina Presutti +5 more
TL;DR: This work aims at rigorously investigating if any correlation exists among indicators, either traditional ( i.e. citation count and h-index) or alternative (i.e., altmetrics) and which of them may be effective for evaluating scholars.
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Bringing Citations and Usage Metrics Together to Make Data Count
TL;DR: The outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group are described, which developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations.
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Indicators for the Data Usage Index (DUI): an incentive for publishing primary biodiversity data through global information infrastructure
Peter Ingwersen,Vishwas Chavan +1 more
TL;DR: The development of the Data Usage Index (DUI) is proposed to demonstrate to data publishers that their efforts of creating biodiversity datasets have impact by being accessed and used by a wide spectrum of user communities.
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Zenodo in the Spotlight of Traditional and New Metrics
TL;DR: The characteristics and the reception of files uploaded to Zenodo and the role the repository plays itself in generating usage are explored and a tendency towards a positive relationship between permissive access rights and altmetrics in certain data records is found.
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An introduction to the joint principles for data citation
TL;DR: The Joint Declaration of Data Citation Principles, finalized in February 2014, is a formal statement pulling together practices used in the research and publishing arenas and in common use that stress the importance and legitimacy of data, the need to give scholarly credit to contributors and the importance of data as evidence.