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So, what's it all about then? Why we share research data

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So, what's it all about then? Why we share research data

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This is the Keynote talk at a Jisc Research Data Network meeting held at Cambridge University on 6 September 2016. The research data network is designed to be a people network offering participants a place to demonstrate practical research data management implementations and to discuss current issues relating to research data in institutions. This keynote discusses two of the most common excuses for not sharing data and then broadens the discussion out to the need for a move to Open Research of which open data is only a small but essential part.

This is the Keynote talk at a Jisc Research Data Network meeting held at Cambridge University on 6 September 2016. The research data network is designed to be a people network offering participants a place to demonstrate practical research data management implementations and to discuss current issues relating to research data in institutions. This keynote discusses two of the most common excuses for not sharing data and then broadens the discussion out to the need for a move to Open Research of which open data is only a small but essential part.

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So, what's it all about then? Why we share research data

  1. 1. Office of Scholarly Communication So, what’s it all about then? Why we share data Jisc Research Data Network meeting Corpus Christi College, Cambridge University 6 September 2016 Dr Danny Kingsley @dannykay68 Head of Scholarly Communication University of Cambridge
  2. 2. Why are we here? What Where When Why How
  3. 3. Sharing data What Where When Why How Making data available for other researchers Openly accessible repositories As close to publication as possible The focus of today’s meeting What this talk is about – contextualise
  4. 4. Drivers for data sharing Image by Danny Kingsley
  5. 5. Drivers for data sharing Image by Danny Kingsley Funders – return on investment + better quality data
  6. 6. Drivers for data sharing Image by Danny Kingsley Funders – return on investment + better quality data Researchers – cultural expectations the ‘right’ thing to do
  7. 7. Blockers to good research Image by Danny Kingsley
  8. 8. Data Excuse Bingo Data Excuse Bingo created by @jenny_molloy
  9. 9. Incompatible! Data Excuse Bingo created by @jenny_molloy
  10. 10. ‘My data is not very interesting’ • 2005 - Professor Simon Deakin part of a team doing research on the effects of legal reforms to shareholder, creditor and worker rights made their datasets available • To date, ~50 academic papers published re- using the datasets • Organisations include: the International Labour Organization,Asian Development Bank, andTheWorld Bank
  11. 11. ‘Someone might steal/plagiarise it’ ‘A second concern held by some is that a new class of research person will emerge — people who had nothing to do with the design and execution of the study but use another group’s data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited.There is concern among some front-line researchers that the system will be taken over by what some researchers have characterized as “research parasites.”’ EDITORIAL ‘Data Sharing’, Dan L. Longo, M.D., and Jeffrey M. Drazen, M.D. N Engl J Med 2016; 374:276-277January 21, 2016 DOI: 10.1056/NEJMe1516564
  12. 12. Case study • Cambridge is a partner institution in the Jisc Data Asset Framework (DAF) surveys – Contributed to survey question development – Organised ethical clearance – Heavily promoted the survey – 440 responses out of the total of 1185 (37% of the responses) came from Cambridge
  13. 13. Partners? We broke our own rule – we did not discuss this before-hand and assumed an arrangement that didn’t exist. A beginners’ mistake. We would have used a non-proprietary repository.
  14. 14. Risk of scooping? • And we would have waited. (To give the team time to write a couple of papers) https://figshare.com/articles/Data_asset_framework_DAF_survey_results_2016/3796305/2
  15. 15. The bigger problem This is what I am going to discuss for the rest of this presentation Researchers are in a rat race to stay ahead Image by Danny Kingsley
  16. 16. Series of blogs published during July &August https://unlockingresearch.blog.lib.cam.ac.uk/?page_id=2#OpenResearch The Case for Open Research
  17. 17. The coin in the realm of academia Image Flickr – Leo Reynolds
  18. 18. Journal Impact Factor Impact Factor for 2015 is – Number of citations in 2014 of articles published in 2012-2013 divided by: – Number of articles published in the journal in 2012-2013 Image by Danny Kingsley
  19. 19. Backlash http://www.sciencemag.org/news/2016/07/hate-journal-impact- factors-new-study-gives-you-one-more-reason
  20. 20. http://www.sciencemag.org/news/2016/07/hate-journal-impact- factors-new-study-gives-you-one-more-reason
  21. 21. San Francisco Declaration on ResearchAssessment • Themes – The need to eliminate the use of journal-based metrics, such as Journal Impact Factors, in funding, appointment, and promotion considerations; – The need to assess research on its own merits rather than on the basis of the journal in which the research is published; and – The need to capitalize on the opportunities provided by online publication (such as relaxing unnecessary limits on the number of words, figures, and references in articles, and exploring new indicators of significance and impact). • http://www.ascb.org/dora/ • >12,000 individuals & >800 organisations
  22. 22. This is one of the big problems Image by Danny Kingsley The insistence on the need to publish novel results in high impact journals is creating a multitude of problems with the scientific endeavour
  23. 23. Hyperauthorship http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.191803 24 of the 33 pages of this paper listed the over 5,000 authors (nine pages are the paper itself)
  24. 24. Storm of protest http://www.nature.com/news/physics-paper-sets-record-with-more-than-5-000-authors- 1.17567
  25. 25. Storm of protest http://www.independent.co.uk/news/science/long-author-lists-on-research-papers-are- threatening-the-academic-work-system-10279748.html
  26. 26. Storm of protest https://theconversation.com/long-lists-are-eroding-the-value-of- being-a-scientific-author-42094
  27. 27. Storm of protest https://www.timeshighereducation.com/news/mass-authorship- destroying-credibility-papers
  28. 28. Speaking of other ways of measuring… http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.191803 This Altmetrics score of 579 is “in the top 5% of all research outputs scored by Altmetric”
  29. 29. Blogged because of author list! https://aps.altmetric.com/details/3997327/blogs
  30. 30. Reproducibility Scientists are very rarely rewarded for being right, they are rewarded for publishing in certain journals and for getting grants. Image by Danny Kingsley
  31. 31. The nine circles of scientific hell (with apologies to Dante and xkcd) Neuroskeptic Perspectives on Psychological Science 2012;7:643-644 Copyright © by Association for Psychological Science
  32. 32. Crisis? Nature, 533, 452–454 (26 May 2016) doi:10.1038/533452a http://www.nature.com/news/1-500-scientists-lift-the-lid-on- reproducibility-1.19970
  33. 33. Oh dear http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124
  34. 34. Retraction • According to RetractionWatch http://retractionwatch.com/ there are 500- 600 retractions a year • Only 5% of publicly available versions (non- publisher websites) of retracted works have a retraction statement attached http://www.ncbi.nlm.nih.gov/pmc/articles/PM C3411255/
  35. 35. Correlation between impact factor and retraction index. Ferric C. Fang, and Arturo Casadevall Infect. Immun. 2011;79:3855-3859
  36. 36. Time for a change ‘Richard Smith: Another step towards the post-journal world’ BMJ blog, 12 Jul, 16 Image by Danny Kingsley
  37. 37. Distribute the load Photo from Flickr – by Andy
  38. 38. Register trials https://clinicaltrials.gov/
  39. 39. Peer review of methodology http://neurochambers.blogspot.co.uk/2012/10/changing-culture-of-scientific.html
  40. 40. Increased transparency Cell Press - redesigned methods section to help authors clearly communicate how experiments are conducted. http://www.eurekalert.org/pub_releases/2016-08/cp- cpt082516.php
  41. 41. Open data • “Publicly funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner.” • RCUK Common Principles on Data Policyhttp://www.rcuk.ac.uk/research/datapol icy/
  42. 42. Science Matters https://www.sciencematters.io/
  43. 43. Putting money where their mouth is • Wellcome Open Research wellcomeopenresearch.org/ • Authors can “rapidly publish all outputs from their research – everything from standard research articles and data sets to case reports, protocols, and null and negative results.”
  44. 44. It is all connected • Increasing access to data is part of a much bigger agenda to overhaul how research is shared, assessed and ultimately practiced. • You are part of a revolution. Image by Danny Kingsley
  45. 45. Questions/Discussion • Thanks! Dr Danny Kingsley Head of Scholarly Communication University of Cambridge @dannykay68

Editor's Notes

  • In 2005 Professor Deakin and the CBR research team began creating a set of linked datasets to estimate the effects of legal reforms to shareholder, creditor and worker rights. The project has evolved, broadened and resulted in far-reaching impact thanks in large part to the willingness of Deakin and his research colleagues to make their data openly available on the web. Around 50 academic papers re-using the datasets have appeared from various scholars and organisations around the world, including high profile entities such as the International Labour Organization (ILO), Asian Development Bank, and The World Bank. As of July 2016, there are three principal datasets coding for labour laws in 117 countries between 1970 and 2013 (the CBR Labour Regulation Index), shareholder protection in 30 countries between 1990 and 2013 (the CBR Extended Shareholder Protection Index), and creditor protection in 30 countries between 1990 and 2013 (the CBR Extended Creditor Protection Index). The datasets are valued by individuals and organisations alike, as they enable researchers to track changes in labour, company and insolvency law over long periods of time for many countries. The latest version of the Law, Finance & Development project datasets can be viewed at http://dx.doi.org/10.17863/CAM.506
     
    Deakin and the Law, Finance & Development team use an innovative research method called leximetrics, which essentially involves applying the principles of econometrics to law and while using an extensive coding methodology. In addition to making their data freely available, the team also supplies codebooks to provide explanations and legal sources for all of their coding, something no other researchers had done before for this type of analysis. The codebooks make the data transparent and the study replicable, and lend more credibility to the research results. Additionally, they are continuing to further develop the datasets on shareholder and creditor rights, so that they match the labour regulation index in terms of years and countries covered
  • Unsurprisingly this position raised a small storm of protest (an example is here). This was so sustained that four days later a clarification was issued, which did not include the word ‘parasites’.
  • The nine circles of scientific hell (with apologies to Dante and xkcd)‏
  • More than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature's survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research.

    The data reveal sometimes-contradictory attitudes towards reproducibility. Although 52% of those surveyed agree that there is a significant 'crisis' of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature.
  • Correlation between impact factor and retraction index. The 2010 journal impact factor (37) is plotted against the retraction index as a measure of the frequency of retracted articles from 2001 to 2010 (see text for details). Journals analyzed were Cell, EMBO Journal, FEMS Microbiology Letters, Infection and Immunity, Journal of Bacteriology, Journal of Biological Chemistry, Journal of Experimental Medicine, Journal of Immunology, Journal of Infectious Diseases, Journal of Virology, Lancet, Microbial Pathogenesis, Molecular Microbiology, Nature, New England Journal of Medicine, PNAS, and Science.
  • https://www.flickr.com/photos/veggiesosage/8129414104/in/photolist-donnA9-6sfvqZ-8rWsjR-roCJtM-dRMUe7-5JE3DX-5bY9jD-5gCtmQ-Gzywtf-fANgPy-6LGtL-6AdqKh-8qZicQ-dJdJE8-48RRrG-5fe5e3-8jCedo-8JFhMX-dRMUrS-7zHQEm-axsb6-aRDZtV-4F6gVx-cyfdM7-9hY5Qz-61tJsZ-2iJ7VY-5frH4J-axsdg-5q8Lko-h942Yo-6YomAZ-2EygiV-tEsAP-3UN759-dCCU93-6YW14K-apE2wJ-4SuG87-3vppp-d95MMw-845Gx-6LGte-8uqdXH-jYQBF6-dRMTZu-4xMeSv-q3xvwc-4mb63q-aqdnhZ
  • Registered Reports will help eliminate these bad incentives by making the results almost irrelevant in reaching editorial decisions. The philosophy of this approach is as old as the scientific method itself: If our aim is to advance knowledge then editorial decisions must be based on the rigour of the experimental design and likely replicability of the findings – andnever on how the results looked in the end.


    Cortex (Neurobiological journal)

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