Data, data use, and scientific inquiry

Wynholds, Laura; Wallis, Jillian C.; Borgman, Christine L.; Sands, Ashley E.; Traweek, Sharon

doi:10.1145/2232817.2232822

Cited by 28 publications

(22 citation statements)

References 12 publications

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“…Repeating and extending previous studies will help develop an understanding of the potential of data reuse, areas of progress, and remaining challenges (e.g., Zimmerman, 2003; Wan & Pavlidis, 2007; Wynholds et al, 2012; Rolland & Lee, 2013). Usage statistics from primary data repositories and value-added repositories are also useful sources of insight into reuse patterns (Rung & Brazma, 2013).…”

Section: Discussionmentioning

confidence: 94%

Data reuse and the open data citation advantage

Piwowar

2013

PeerJ

445

271

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Background. Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the “citation benefit”. Furthermore, little is known about patterns in data reuse over time and across datasets.Method and Results. Here, we look at citation rates while controlling for many known citation predictors and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties.Conclusion. After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered. We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.

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Section: Discussionmentioning

confidence: 94%

Data reuse and the open data citation advantage

Piwowar

2013

PeerJ

445

271

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“…The few studies on data reuse indicate that scientifically important uses of data may not be mentioned or cited in publications (Palmer, Weber, & Cragin, 2011;Wallis et al, 2013;Wynholds et al, 2012). The reasons for lack of citation are many.…”

Section: Discussionmentioning

confidence: 99%

“…Data in repositories are most easily cited, as these institutions usually offer suggested citation formats that include unique and persistent identifiers. However, if external data were obtained to calibrate instruments or to "ground truth" a field site, they may not be cited because they were considered background to the research or implicit in the methods Wynholds, Wallis, Borgman, Sands, & Traweek, 2012). In other cases, authors might cite a "data paper" associated with a data release, as in astronomy (Ahn et al, 2012), an entire archive (e.g., Sloan…”

Section: Theoretical Problems Of Data Citationmentioning

confidence: 99%

Data Citation as a Bibliometric Oxymoron

Borgman¹

2016

Theories of Informetrics and Scholarly Communication

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“…The steps of data discovery, location, retrieval, and storage all take place before data use or reuse takes place (Wynholds, Wallis, Borgman, Sands, & Traweek, 2012). The decisions required in these initial stages can impact the scale, method and other aspects of the scientific project, in turn affecting the outcome of the research.…”

Section: Retrieve and Storementioning

confidence: 99%

“…Astronomers draw ever more heavily on repositories of astronomical observations. These resources may supplement or supplant direct observation from telescopes (Wynholds, Wallis, Borgman, Sands, & Traweek, 2012). We analyze the people and infrastructure involved in the building, sustaining, and curation of large sky surveys; these generate massive amounts of data that serve multiple scientific purposes (Fearon, Borgman, Traweek, & Wynholds, 2010).…”

Section: Introductionmentioning

confidence: 99%

Follow the data: How astronomers use and reuse data

Sands

Borgman

Wynholds

et al. 2012

Proc of Assoc for Info

Self Cite

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We analyze the people and infrastructure involved in the building, sustaining, and curation of large astronomy sky surveys. Our research assesses what new infrastructures, divisions of labor, knowledge, and expertise are necessary for the proper care of data. Between May 2011-February 2012, we conducted fourteen interviews employing Sloan Digital Sky Survey (SDSS) data use as the focus. SDSS is a multi-faceted, multi-phased data-driven telescope project with hundreds of collaborators and thousands of users of the open data. The Follow the Data interview protocol identifies a single publication authored by each interviewee and uses it as a lens looking backward and forward to identify data uses leading into and out of the publication.The interviews revealed the ways these astronomers discover, locate, retrieve, and store external data for their research. Any given astronomy research project may employ multiple methods to discover, locate, retrieve, and store multiple datasets. Our research finds that informal and formal methods are used to discover and locate data, including person-to-person contact. Data retrieval and storage methods are often determined by the size of the dataset and the amount of infrastructure available to the researcher. Astronomy research practices are evolving rapidly with access to more data and better tools. The poster presentation will report further on how those data are used and reused in astronomy.

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Data, data use, and scientific inquiry

Cited by 28 publications

References 12 publications

Data reuse and the open data citation advantage

Data reuse and the open data citation advantage

Data Citation as a Bibliometric Oxymoron

Follow the data: How astronomers use and reuse data

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