Readcube papers bibliography full#

Readcube papers bibliography full#

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. 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. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 20, at about 30%. 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. 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.

Here, we look at citation rates while controlling for many known citation predictors and investigate the variability of data reuse. Furthermore, little is known about patterns in data reuse over time and across datasets. 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”. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. 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.