data reuse and scholarly reward: understanding practice and building infrastructure
DESCRIPTION
authors: Todd Vision and Heather Piwowar presented at: iEvoBio, Ottawa, July 11, 2012TRANSCRIPT
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Data reuse and scholarly reward:understanding practice and building infrastructure
Todd Visionab, Heather Piwowarac
a National Evolutionary Synthesis CenterbUniversity of North Carolina at Chapel HillcDuke University
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Outline
Article citation boost from open datafocusing on gene expression data
Patterns of data reuse across repositories
1000 datasets from 10 repositories Beyond traditional metrics
total-impact.org
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The open data citation boost I
Citation boost (95% CI) p
Impact factor (increase 2X) 84% (54-109%) <0.001
Date of publication (1 month earlier) 3% (2-5%) <0.001
Nationality (US corresponding author) 38% (1-89%) 0.049
Data publicly available 69% (18-143%) 0.006
Piwowar et al. (2007) PLoS ONE 2, e308
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The open data citation boost II
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Reuse by self vs others
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Extrapolated reuse by others
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Why?
1. Data Reuse2. Credibility Signalling3. Increased Visibility4. Early View5. Selection Bias
http://researchremix.wordpress.com/2012/07/02/open-data-citation-advantage
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Data citation by repository
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data repositories
reference managers
blog citations
social bookmarks
social networks
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Escaping the article citation deathtrap
http://total-impact.org/collection/qdp779
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Analysis software and data: https://github.com/hpiwowar License: CCZero for data where possible, MIT for code
Dryad: new BSD license: http://code.google.com/p/dryad/DataONE: Apache licensehttp://www.dataone.org/developer-resourcesTotal-impact: MIT license: https://github.com/total-impact/
Contributions from: Jonathan Carlson, Sarah Judson, Jason Priem, Estephanie Sta Maria, Nick Weber, Mike Whitlock
Funding: NSF (to DataONE, Dryad, and NESCent), Sloan Foundation, Open Society Foundation
Follow us:Todd: @tjvisionHeather: @researchremix, researchremix.wordpress.orgDryad: @datadryad, blog.datadryad.org@totalimpactorg, total-impact.tumblr.com