This document discusses a study analyzing data reuse across scientific repositories. It finds that open gene expression data receives 69% more citations on average than closed data. It also finds patterns of data reuse vary across repositories, with references managers, blogs, bookmarks, and social networks all being venues for citations beyond traditional publications. The document advocates moving beyond traditional metrics like citations and journal impact factors to more fully capture the impact of open data sharing.

1. Data reuse and scholarly reward:
understanding practice and
building infrastructure
Todd Visionab, Heather Piwowarac
a NationalEvolutionary Synthesis Center
bUniversity of North Carolina at Chapel Hill
cDuke University

2. Outline
Article citation boost from open data
focusing on gene expression data
Patterns of data reuse across repositories
1000 datasets from 10 repositories
Beyond traditional metrics
total-impact.org

3. 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

4. The open data citation boost II

5. Reuse by self vs others

6. Extrapolated reuse by others

7. Why?
1. Data Reuse
2. Credibility Signalling
3. Increased Visibility
4. Early View
5. Selection Bias
http://researchremix.wordpress.com/2012
/07/02/open-data-citation-advantage

9. Data citation by repository

11. data repositories
reference managers
blog citations
social bookmarks
social networks

12. Escaping the article citation deathtrap
http://total-impact.org/collection/qdp779

13. 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-resources
Total-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: @tjvision
Heather: @researchremix, researchremix.wordpress.org
Dryad: @datadryad, blog.datadryad.org
@totalimpactorg, total-impact.tumblr.com