We have a new preprint out: Piwowar H, Vision TJ (2013) Data reuse and the open data citation advantage. PeerJ PrePrints 1:e1

It’s currently under review at PeerJ, but we still welcome feedback, and will be happy to acknowledge anyone who whose tips improve the final version of the MS.

Incidentally, thanks to Heather’s quick mouse clicking, it’s the first submission on PeerJ’s new PrePrint site. We’re hopeful that this platform will help Biology warm to the idea of preprints the way Physics has to arXiv!

Some other aspects of the process of writing this manuscript are fun and worthy of discussion, but I’ll let Heather take the first crack at that topic on her blog when she gets the time. In the meantime, there’s a nice interview with Heather on the Dryad blog that discusses some of the background to the paper.

For another very cool experiment with preprints and open peer review in Biology, see Haldane’s Seive. One of the founders is our own Vision lab undergrad alumnus Joe Pickrell.

But, back to the new paper, here’s the abstract:

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 boost”. 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 boost varied with date of dataset deposition: a citation boost 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 boost 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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