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Towards Open Research: practices, experiences, barriers and opportunities

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Report on key findings of a Wellcome-commissioned study to investigate current practices for paper, data & code sharing among Wellcome & ESRC funded researchers and any barriers that are encountered. Presented by Gareth Knight at a CPD25 Open Access workshop at the Foundling museum in London on 26 April 2017.

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Towards Open Research: practices, experiences, barriers and opportunities

  1. 1. Towards Open Research practices, experiences, barriers and opportunities CPD25: Open Access and Repositories 26 April 2017 Veerle Van den Eynden Gareth Knight (Presenter) Anca Vlad UK Data Service University of Essex London School of Hygiene & Tropical Medicine UK Data Service University of Essex
  2. 2. Open Research study • Researchers funded by Wellcome Trust and ESRC: biomedical, clinical, population health, humanities, social sciences  Current attitudes and practices related to sharing of: • Publications • Data • Code  Barriers that inhibit or prevent researchers from sharing  Identification of action that funders can take to encourage good practice and mitigate issues • Survey (N=583 + 259), focus groups (N=22) Van den Eynden, Veerle et al. (2016) Towards Open Research: Practices, experiences, barriers and Opportunities. Wellcome Trust. https://doi.org/10.6084/m9.figshare.4055448
  3. 3. Article publishing • Respondents published average of 18-peer reviewed papers during past 5 years – 30% published all papers as OA • Factors that affect ability to publish OA: – Journal lacks OA option (31%) – Lack of funds to cover APCs (30%) – Papers uploaded to social network (8%) – Lead author decided against OA (4%) • 50% of respondents use WT funds for APCs: – Humanities & social scientists less likely than Biomedical & clinical scientists – Early-career less likely than more established researchers Open access cookie (CC BY-NC-SA 2.0) https://www.flickr.com/photos/biblioteekje/6325328112/
  4. 4. Data sharing 95% of respondents generate research data, of which 52% made it available in last 5 years
  5. 5. Data sharing methods 414 respondents share data: • Full dataset (51%) • Data subset linked to paper (38%) • Other subset of data (37%) Via: • Community repositories (42%) • Institutional repositories (37%) • Project/private repositories (15%) • General purpose repositories (13%) • Journal supplementary (10%)
  6. 6. Reasons to share data 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% My funder requires me to share my data(N=273) Journal expects data underpinning findings to be accessible(N=273) My research community expects data sharing(N=274) It is good research practice to share research data(N=277) It enables collaboration and contribution by other researchers(N=274) It has public health benefits, e.g. disease outbreaks(N=265) Ability to respond rapidly to public health emergencies(N=263) Ethical obligation towards research participants to maximize benefits for society(N=266) Contributes to academic credentials(N=273) Enables validation and /or replication of my research(N=275) Improved visibility for my research(N=273) I can get credit and more citations by sharing data(N=267) Not at all important Slightly important Moderately important Very important Extremely important Source: Wellcome survey results
  7. 7. Barriers to data sharing 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% I may lose publication opportunities if I share data(N=517) Others may misuse or misinterpret my data(N=519) I have insufficient skills to prepare the data(N=505) It requires time/effort to prepare my data for deposit(N=520) I do not have sufficient funding to prepare data for sharing(N=509) I do not have permission (consent) from my research participants to share data(N=510) Data contain confidential / sensitive information and cannot be de-identified(N=504) My data are commercially sensitive or has commercial value(N=501) There are third party rights in my data(N=499) No suitable repository exists for my data(N=502) Country-specific regulations do not allow sharing(N=486) Not at all important Slightly important Moderately important Very important Extremely important Source: Wellcome survey results
  8. 8. Motivations for more data sharing Source: Wellcome survey results
  9. 9. Significant differences in motivationMOREIMPORTANTLESSIMPORTANT Extra funding to cover costs established researchers ~ cell, development and physical science, genetic and molecular science, neuroscience and mental health, population health infection and immunobiology Enhanced academic reputation early career researchers ~ researchers not sharing data now Co-authorship on reuse papers early career researchers clinical, population health, social science researchers cell, devel and physical science, neuroscience and mental health biomedical and humanities researchers, genetic and molecular science, infection and immunobiology Case study that showcase data LMIC researchers ~ humanities, Infection and immuno-biology, population health cell, development and physical science, genetic and molecular science, neuroscience and mental health Data deposit leads to data paper publication early career researchers; LMIC researchers ~ cell, development and physical science, infection and immuno- biology, neuroscience and mental health genetic and molecular science, humanities and social sciences Considered favourably in funding and promotion decisions UK-based researchers ~ cell, development and physical science, genetic and molecular science, neuroscience and mental health Population health Ability to limit data access to specific purposes or individuals LMIC researchers ~ clinical, population health and social science researchers biomedical researchers Assistance from institution or funder to prepare data clinical, population health and social science researchers biomedical and humanities researchers
  10. 10. Code sharing 40% of respondents generate code: • Researchers performing surveys, observations, experiments, secondary analysis & simulations more likely to produce code 43% of these shared code in last 5 years: • Researchers performing simulations and secondary analysis more likely to share code • Researchers applying qualitative methods less likely to share code 37% reuse existing code: • Obtain from colleagues, collaborators & community repositories • Influencing factors in code reuse: good documentation, reputable source, and open availability Shared via institutional, community & journal services
  11. 11. Reasons to share code 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% My funder requires me to share my code(N=97) Journal expects code to be accessible(N=97) My research community expects code sharing(N=97) It is good research practice to share code(N=101) To enable collaboration and contribution (N=98) Contributes to my academic credentials(N=95) Enables validation of my research(N=97) Enables replication of my research(N=96) Improved visibility for my research(N=95) I can get credit and more citations by sharing code(N=91) Not at all important Slightly important Moderately important Very important Extremely important Source: Wellcome survey results
  12. 12. Code sharing benefits 0 5 10 15 20 25 30 35 40 Career benefits More publications Higher citation rate New collaborations More funding opportunities Financial benefit New patents Improvements to public health Use in health emergencies None Other Source: Wellcome survey results
  13. 13. Code sharing barriers 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Desire to patent (N=210) Protecting intellectual property (N=213) Software and systems dependencies (N=213) I may lose publication opportunities if I share code (N=210) Others may misuse or misinterpret my code (N=211) Insufficient skills to prepare the code for public use (N=213) It requires time/effort to prepare my code for deposit (N=217) Insufficient funding to prepare code for public use (N=211) My code has commercial value (N=207) There are third party rights in my code (N=206) No suitable repository exists for my code (N=197) Not at all important Slightly important Moderately important Very important Extremely important Source: Wellcome survey results
  14. 14. Motivations for more code sharing 0 10 20 30 40 50 60 Financial incentive from my institution Extra funding to cover the costs Enhanced academic reputation Code access and metrics Knowing how others use my code Co-authorship on papers resulting from reuse Case study that showcases my code It is looked on more favourably in funding and promotion decisions Evidence of code citation Assistance from institution/funder staff to prepare code Nothing motivates me Source: Wellcome survey results
  15. 15. Recommendations Funding: • Dedicated funding streams for data/code preparation • Guidelines for describing code development & sharing in funding bid (Software Management Plan?) • Demand for investment in support staff to help with data/code preparation Rewards: • Recognise data & code sharing in career progress evaluation • Citations and co-authorship for new publications based upon shared data/code • Build evidence of good practice – case studies. Infrastructure: • Utilise existing infrastructure where possible, e.g. GitHub, SourceForge, CRAN for R code, etc. • Enhance functionality - granular access controls, big data, enhanced citation and reuse metrics Support: • Enhance networking / support opportunities for data/code creators and re-users • Develop training – software carpentry, Software Sustainability Institute
  16. 16. Further Developments https://wellcome.figshare.com/ https://wellcomeopenresearch.org/
  17. 17. Thanks to: All researchers who contributed to the surveys and focus groups Wellcome Trust: David Carr Robert Kiley Expert advisors: Barry Radler (University of Wisconsin), Carol Tenopir (University of Tennessee), David Leon, Jimmy Whitworth (LSHTM) Frank Manista (Jisc) Louise Corti (UK Data Service)

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