Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields

Park, Hyoungjoo; You, Sukjin; Wolfram, Dietmar

doi:10.1002/asi.24049

Cited by 45 publications

(41 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Individuals involved in research, across disciplines, who seek and reuse secondary data comprised the population of interest. This is a challenging population to target, as it is difficult to trace instances of data reuse, particularly given the fact that data citation, and other forms of indexing, are still in their infancy 22 . The data reuse practices of individuals in certain disciplines have been better studied than others 23 , in part because of the existence of established data repositories within these disciplines 24 .…”

Section: Methodsmentioning

confidence: 99%

A dataset describing data discovery and reuse practices in research

Gregory

2020

Sci Data

View full text Add to dashboard Cite

This paper presents a dataset produced from the largest known survey examining how researchers and support professionals discover, make sense of and reuse secondary research data. 1677 respondents in 105 countries representing a variety of disciplinary domains, professional roles and stages in their academic careers completed the survey. The results represent the data needs, sources and strategies used to locate data, and the criteria employed in data evaluation of these respondents. The data detailed in this paper have the potential to be reused to inform the development of data discovery systems, data repositories, training activities and policies for a variety of general and specific user communities.

show abstract

Section: Methodsmentioning

confidence: 99%

A dataset describing data discovery and reuse practices in research

Gregory

2020

Sci Data

View full text Add to dashboard Cite

show abstract

“…However, data citations should be interpreted carefully, due to the lack of coverage across disciplines (Mongeon et al, 2017). A deeply engrained authorship and citation culture affect the rates at which research data is cited: some journals and data repositories lack standardized guidelines for citing data (Mooney & Newton, 2012), informal data citation is more common in certain fields (Park et al, 2018;Zhao et al, 2018), and authors may not be aware of the need to cite the data they base their studies upon (Mooney & Newton, 2012). There are also known differences between the types of datasets and their citation rates (Peters et al, 2016).…”

Section: Citation-based Indicatorsmentioning

confidence: 99%

Assessing the Impact and Quality of Research Data Using Altmetrics and Other Indicators

Konkiel

2020

SAR

View full text Add to dashboard Cite

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...

show abstract

“…Policy initiatives, field cultures and data infrastructure all help to encourage data sharing [6] and researchers seem increasingly willing to publish their data [7]. This may generate citations to the data, originating paper or authors to recognise this effort [8][9][10][11][12][13], which is a useful incentive [14].…”

Section: Introductionmentioning

confidence: 99%

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

et al. 2020

View full text Add to dashboard Cite

Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.

show abstract

Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields

Cited by 45 publications

References 37 publications

A dataset describing data discovery and reuse practices in research

A dataset describing data discovery and reuse practices in research

Assessing the Impact and Quality of Research Data Using Altmetrics and Other Indicators

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

Contact Info

Product

Resources

About