{"data":{"id":"10.5061/dryad.781pv","type":"dois","attributes":{"doi":"10.5061/dryad.781pv","prefix":"10.5061","suffix":"dryad.781pv","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Piwowar, Heather A.","nameType":"Personal","givenName":"Heather A.","familyName":"Piwowar","affiliation":["National Evolutionary Synthesis Center","Duke University"],"nameIdentifiers":[]},{"name":"Vision, Todd J.","nameType":"Personal","givenName":"Todd J.","familyName":"Vision","affiliation":["National Evolutionary Synthesis Center","Duke University","University of North Carolina at Chapel Hill"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Data reuse and the open data citation advantage"}],"publisher":"Dryad","container":{},"publicationYear":2013,"subjects":[{"subject":"Information science"},{"subject":"Incentives"},{"subject":"Bibliometrics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Data reuse"},{"subject":"Data repositories"},{"subject":"Bioinformatics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Data archiving"},{"subject":"Open data","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Science policy","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2013-09-19T23:40:57Z","dateType":"Submitted"},{"date":"2013-10-01T14:45:26Z","dateType":"Issued"},{"date":"2013-10-01T14:45:26Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.7717/peerj.175","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["175461156 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Background: Attribution to the original contributor upon reuse of\n published data is important both as a reward for data creators and to\n document the provenance of research findings. Previous studies have found\n that papers with publicly available datasets receive a higher number of\n citations than similar studies without available data. However, few\n previous analyses have had the statistical power to control for the many\n variables known to predict citation rate, which has led to uncertain\n estimates of the \"citation benefit\". Furthermore, little is\n known about patterns in data reuse over time and across datasets. Method\n and Results: Here, we look at citation rates while controlling for many\n known citation predictors, and investigate the variability of data reuse.\n In a multivariate regression on 10,555 studies that created gene\n expression microarray data, we found that studies that made data available\n in a public repository received 9% (95% confidence interval: 5% to 13%)\n more citations than similar studies for which the data was not made\n available. Date of publication, journal impact factor, open access status,\n number of authors, first and last author publication history,\n corresponding author country, institution citation history, and study\n topic were included as covariates. The citation benefit varied with date\n of dataset deposition: a citation benefit was most clear for papers\n published in 2004 and 2005, at about 30%. Authors published most papers\n using their own datasets within two years of their first publication on\n the dataset, whereas data reuse papers published by third-party\n investigators continued to accumulate for at least six years. To study\n patterns of data reuse directly, we compiled 9,724 instances of third\n party data reuse via mention of GEO or ArrayExpress accession numbers in\n the full text of papers. The level of third-party data use was high: for\n 100 datasets deposited in year 0, we estimated that 40 papers in PubMed\n reused a dataset by year 2, 100 by year 4, and more than 150 data reuse\n papers had been published by year 5. Data reuse was distributed across a\n broad base of datasets: a very conservative estimate found that 20% of the\n datasets deposited between 2003 and 2007 had been reused at least once by\n third parties. Conclusion: After accounting for other factors affecting\n citation rate, we find a robust citation benefit from open data, although\n a smaller one than previously reported. We conclude there is a direct\n effect of third-party data reuse that persists for years beyond the time\n when researchers have published most of the papers reusing their own data.\n Other factors that may also contribute to the citation benefit are\n considered.We further conclude that, at least for gene expression\n microarray data, a substantial fraction of archived datasets are reused,\n and that the intensity of dataset reuse has been steadily increasing since\n 2003.","descriptionType":"Abstract"},{"description":"manuscript source file\n (knitr)The source file for\n running the statistics and inserting the results into the manuscript text.\n The manuscript does not include final wording revisions made in the peer\n review process. See the \"about this doc\" section for\n instructions on running this R knitr markdown file to produce stats.md and\n 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.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.781PV</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Piwowar, Heather A.</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/001ykb961" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Evolutionary Synthesis Center</affiliation>
      <affiliation affiliationIdentifier="https://ror.org/00py81415" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Duke University</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Vision, Todd J.</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/001ykb961" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Evolutionary Synthesis Center</affiliation>
      <affiliation affiliationIdentifier="https://ror.org/00py81415" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Duke University</affiliation>
      <affiliation affiliationIdentifier="https://ror.org/0130frc33" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">University of North Carolina at Chapel Hill</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data from: Data reuse and the open data citation advantage</title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2013</publicationYear>
  <subjects>
    <subject>Information science</subject>
    <subject>Incentives</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Bibliometrics</subject>
    <subject>Data reuse</subject>
    <subject>Data repositories</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Bioinformatics</subject>
    <subject>Data archiving</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Open data</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Science policy</subject>
  </subjects>
  <dates>
    <date dateType="Submitted">2013-09-19T23:40:57Z</date>
    <date dateType="Issued">2013-10-01T14:45:26Z</date>
    <date dateType="Available">2013-10-01T14:45:26Z</date>
  </dates>
  <language>en</language>
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  <descriptions>
    <description descriptionType="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 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>
    <description descriptionType="Other">
      &lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;manuscript source file
      (knitr)&lt;/h4&gt;&lt;div
      class="o-metadata__file-description"&gt;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.&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;stats_knit_.md&lt;/br&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;manuscript compiled file, with stats&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This file is the result of running stats_knitr_.md through knitr. It does not include changes made during the peer review process.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;stats.md&lt;/br&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;citations&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Citations used by stats_knitr_.md to build the references list.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;citation11k.bib&lt;/br&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;helpers.r&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Helper R functions used by stats_knitr_.md to run the statistics.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;helpers.R&lt;/br&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;preprocess_raw_data.r&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Helper R functions used by stats_knitr_.md to preprocess data before running the statistics.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;preprocess_raw_data.R&lt;/br&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;pubmed_gse_count.csv&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;The number of GSE data sets added to the NCBI's GEO repository each year, 2000-2011.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;pubmed_pmc_ratios.csv&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;The 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;PLoSONE2011_rawdata.txt&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Data 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;scopus_all.csv&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Scopus citation data for publications in the cohort.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;GEO_dataset_attributes.csv&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;One 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Mendeley_annotated_250_of_11k.csv&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Manual 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;tracking1k_20111008.csv&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Manually 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;
    </description>
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