DOI: 10.5061/dryad.781pv
title: Data from: Data reuse and the open data citation advantage

description: 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.
description: manuscript source file
 (knitr)The source file for
 running the statistics and inserting the results into the manuscript text.
 The manuscript does not include final wording revisions made in the peer
 review process. See the "about this doc" section for
 instructions on running this R knitr markdown file to produce stats.md and
 stats.R files.stats_knit_.mdmanuscript compiled file, with statsThis file is the result of running stats_knitr_.md through knitr. It does not include changes made during the peer review process.stats.mdcitationsCitations used by stats_knitr_.md to build the references list.citation11k.bibhelpers.rHelper R functions used by stats_knitr_.md to run the statistics.helpers.Rpreprocess_raw_data.rHelper R functions used by stats_knitr_.md to preprocess data before running the statistics.preprocess_raw_data.Rpubmed_gse_count.csvThe number of GSE data sets added to the NCBI's GEO repository each year, 2000-2011.pubmed_pmc_ratios.csvThe fraction of PubMed in PMC that are indexed with the MeSH term “gene expression profiling”, by year of publication, 2000-2011, as measured in 2012.PLoSONE2011_rawdata.txtData from Piwowar HA (2011) Data from: Who shares? Who doesn’t? Factors associated with openly archiving raw research data. Dryad Digital Repository. doi:10.5061/dryad.mf1sd. Reproduced here to make it easy to rerun scripts.scopus_all.csvScopus citation data for publications in the cohort.GEO_dataset_attributes.csvOne row for every GEO dataset reuse detected by searching PMC for GEO accession numbers. Columns list GEO accession, gse number, gds number, related submit_pmids, identified reuse_pmcid, the reuse_pmids_for_pmc, the submission authors, the reuse authors, whether the sumission authors and the reuse authors overlap, the submission affiliation, the release date, columns to detect reuse and data creation keywords, excerpts around the accession numbers when available (the reuse paper was OA), reuse journal, year, and date_published, the medline_status, whether it was listed on the NCBI GEO reuse webpage, whether the reuse is OA, and whether it is listed as a metaanalysis by MEDLINE.Mendeley_annotated_250_of_11k.csvManual annotation of a random 250 papers from the 10,555 papers in the study. Manual examination was to determine whether the study did indeed generate gene expression microarray data. Rows with "created-microarray-data" were identified as generating microarray data in the manual review; "created-microarray-data-not" were identified as not actually generating gene expression microarray data despite being identified as such by our automated filter.tracking1k_20111008.csvManually annotated instances of citation context to papers that created publicly available datasets. This study explores the subset of the dataset related to GEO data: "dataset reused" means the citation context was determined to be in the context of data reuse.