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Towards Open Research

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Practices, experiences, barriers and opportunities. Presented at the Research Data Network workshop, St Andrews, 30 Nov 2016

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Towards Open Research

  1. 1. Towards Open Research practices, experiences, barriers and opportunities 3rd Research Data Network St Andrews, 30 November 2016 Veerle Van den Eynden Gareth Knight UK Data Service University of Essex London School of Hygiene & Tropical Medicine
  2. 2. Our research • 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://dx.doi.org/10.6084/m9.figshare.4055448
  3. 3. Data sharing practices • 95% of respondents generate research data • 51 / 55 % of these made research data available in last 5 years • 4 / 2 datasets on average: full dataset or subset, e.g. with paper • sharing increases with career length • sharing varies by discipline • 77% reuse existing data for: background, validation, methodology development & new analysis
  4. 4. Reasons to share data (Wellcome) 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
  5. 5. Reasons to share data (ESRC) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% My funder requires me to share my data(N=131) Journal expects data to be accessible(N=132) My research community expects data sharing(N=131) It is good research practice to share research data(N=133) Collaboration and contribution by other researchers(N=131) It has public health benefits, e.g. disease outbreaks(N=125) Ability to respond rapidly to public health emergencies(N=122) Ethical obligation/Maximize benefits for society(N=128) Contributes to academic credentials(N=128) Enables validation and /or replication of my research(N=129) Improved visibility for my research(N=128) I can get credit and more citations by sharing data(N=127) Not at all important Slightly important Moderately important Very important Extremely important Benefits from data sharing: collaborations, higher citation rates Most no direct benefits; but also no bad experiences
  6. 6. Barriers to data sharing (Wellcome) 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
  7. 7. Barriers to data sharing (ESRC) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% I may lose publication opportunities(N=231) Others may misuse or misinterpret my data(N=229) I have insufficient skills to prepare the data(N=227) It requires time/effort to prepare data for deposit(N=233) Insufficient funding to prepare data(N=232) No consent from research participants to share data(N=232) Confidential / sensitive data(N=229) Commercially sensitive/has commercial value(N=218) There are third party rights in my data(N=219) No suitable repository exists for my data(N=220) Country-specific regulations do not allow sharing(N=214) Not at all important Slightly important Moderately important Very important Extremely important
  8. 8. Motivations for more data sharing (Wellcome)
  9. 9. Significant differences in motivations MOREIMPORTANTLESSIMPORTANT 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 Knowing how other people use data early career researchers ~ LMIC researchers ~ cell, development and physical science, humanities, infection and immuno-biology, population health genetic and molecular science 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 MOREIMPORTANTLESSIMPORTANT 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 Evidence of data citation early career researchers researchers not sharing data now 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 Nothing would motivate researchers not sharing data now
  10. 10. Code sharing practices • 40% generate code – Researchers performing surveys, secondary analysis & simulations more likely to produce code • 43% of these made code available in last 5 years – Researchers performing simulations, secondary analysis and experiments share most code – Researcher applying qualitative and survey methods shared less • 37% reuse existing code – Obtained from colleagues/collaborators & community repository – Good documentation, originate from a reputable source, and openly available are key factors in code reuse
  11. 11. Reasons to share code (Wellcome) 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
  12. 12. Code sharing benefits (Wellcome) 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
  13. 13. Code sharing barriers (Wellcome) 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
  14. 14. Motivations for more code sharing (Wellcome) 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
  15. 15. PROPOSED ACTIONS
  16. 16. Data Sharing & Reuse Policy development • Provide guidelines on how to share 'difficult' data types, e.g. sensitive and large data • Consider how contradictions between government and funder data sharing policy can addressed Rewards • Ensure data sharing recognised in career progress evaluation • Facilitate opportunities for data creators to become co-authors on new publications based upon their data Promotion • Monitor use and showcase examples of best practice • Provide networking/training opportunities for data creators and re-users Infrastructure development • Build repository that offers free storage, supports granular access controls, and resource-specific features (e.g. imaging data, large datasets) Funding • Consider a dedicated funding stream to cover data/code preparation for projects, and additional staff within institution/project/support network to help with data preparation
  17. 17. Code Sharing & Reuse Policy development • Consider code sharing mandate • Include processing scripts such as stata.do files and batch files in interpretation Rewards • recognise in funding decisions • encourage authors to cite code in research outputs Promotion • monitor code reuse and showcase examples of code sharing best practice • Provide networking/training opportunities for code developers and code re-users Infrastructure development • Invest in creation of deposit tools • Consider setup of a long-term repository for research code (e.g. Wellcome GitLab), or offer guidance on platforms to use Funding • Consider additional funding for code sharing preparation during project life and ongoing maintenance over time
  18. 18. Further developments • Wellcome Open Research platform • Wellcome Open Research Pilot Project (Cambridge) • Series of reports and reviews
  19. 19. Wellcome Trust, David Carr, Robert Kiley Anca Vlad, UK Data Service All researchers contributing wisdom via surveys and focus group discussions Expert advisors: Barry Radler (University of Wisconsin), Carol Tenopir (University of Tennessee), David Leon (LSHTM), Frank Manista (Jisc), Jimmy Whitworth (LSHTM) and Louise Corti (UK Data Service)

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