Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Data reuse and scholarly reward: understanding practice and building infrastructure


Published on

authors: Todd Vision and Heather Piwowar
presented at: iEvoBio, Ottawa, July 11, 2012

Published in: Technology, Education
  • Be the first to comment

Data reuse and scholarly reward: understanding practice and building infrastructure

  1. 1. Data reuse and scholarly reward:understanding practice andbuilding infrastructureTodd Visionab, Heather Piwowaraca NationalEvolutionary Synthesis CenterbUniversity of North Carolina at Chapel HillcDuke University
  2. 2. OutlineArticle citation boost from open data focusing on gene expression dataPatterns of data reuse across repositories 1000 datasets from 10 repositoriesBeyond traditional metrics
  3. 3. The open data citation boost I Citation boost (95% CI) pImpact factor (increase 2X) 84% (54-109%) <0.001Date of publication (1 month earlier) 3% (2-5%) <0.001Nationality (US corresponding author) 38% (1-89%) 0.049Data publicly available 69% (18-143%) 0.006 Piwowar et al. (2007) PLoS ONE 2, e308
  4. 4. The open data citation boost II
  5. 5. Reuse by self vs others
  6. 6. Extrapolated reuse by others
  7. 7. Why?1. Data Reuse2. Credibility Signalling3. Increased Visibility4. Early View5. Selection Bias
  8. 8. Data citation by repository
  9. 9. data repositoriesreference managers blog citations social bookmarks social networks
  10. 10. Escaping the article citation deathtrap
  11. 11. Analysis software and data: CCZero for data where possible, MIT for codeDryad: new BSD license: Apache license MIT license: from: Jonathan Carlson, Sarah Judson, Jason Priem, EstephanieSta Maria, Nick Weber, Mike WhitlockFunding: NSF (to DataONE, Dryad, and NESCent), Sloan Foundation, OpenSociety FoundationFollow us: Todd: @tjvision Heather: @researchremix, Dryad: @datadryad, @totalimpactorg,