Data reuse and scholarly reward: understanding practice and building infrastructure

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

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

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

The open data citation boost II

Reuse by self vs others

Extrapolated reuse by others

Why?

1. Data Reuse2. Credibility Signalling3. Increased Visibility4. Early View5. Selection Bias

http://researchremix.wordpress.com/2012/07/02/open-data-citation-advantage

Data citation by repository

data repositories

reference managers

blog citations

social bookmarks

social networks

Escaping the article citation deathtrap

http://total-impact.org/collection/qdp779

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