Opening up data: a UK perspective – Jisc and CNI conference 10 July 2014
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This document summarizes Kevin Ashley's presentation on opening up research data from a UK perspective. The presentation discusses the policy background around open data in the UK, developments in infrastructure to support open data, and costs associated with making data openly available. It also notes that fully realizing the benefits of open data will require international cooperation across organizations like the Digital Curation Centre.
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Opening up data: a UK perspective – Jisc and CNI conference 10 July 2014
- 1. Opening up data: A UK perspective Kevin Ashley Digital Curation Centre www.dcc.ac.uk @kevingashley Kevin.ashley@ed.ac.uk Reusable with attribution: CC-BY The DCC is supported by Jisc
- 2. A summary • Policy background • The end point – why it matters • UK reaction & developments • Infrastructure • Costs • Joining up internationally • More than data… 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 2
- 3. My home – the DCC • Mission – to increase capability and capacity for research data services in UK institutions • Not just a UK problem – an international one • Training, shared services, guidance, policy, standards, futures 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 3
- 4. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 4 SWEDEN DENMARK CANADA
- 5. Data reuse stories • The palaeontologist who saved years of work with archaeological data • The 19th-century ships logs that help us model climate change • The ‘noise’ from research radar that mapped dust from Eyjafjallajökull 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 5
- 6. Data reuse - messages 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 6 Often your data tells stories that your publications do not Not all data comes from other researchers One person’s noise is another person’s signal Discipline-bounded data discovery doesn’t give us all we need or want
- 7. Why does this matter? • Research quality – How close can we get to the truth? • Research speed – How quickly can we get to the truth? • Research finance – How much does the truth cost? • Improving one or more of these is of interest to all actors: • Researchers as data creators • Researchers as data reusers • Research institutions • Funders – hence government and society 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 7
- 8. The Policy 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 8
- 9. G8UK - Endorses OA Open Data Charter Policy Paper 18 June 2013 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 9
- 10. Funder requirements • UK – RCUK (generic), NERC, STFC, ESRC, BBSRC, EPSRC, MRC • USA – NSF, NEH, NIH • Europe • Denmark, Germany, Netherlands… • Most place burden on researcher – some on the institution 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 10 http://www.epsrc.ac.uk/about/standards/researchdata/Pages/policyframework.aspx
- 11. RCUK policy - The 1-minute version • Research data are a public good – make openly available in timely & responsible way • Have policies & plans. Data with long-term value should be preserved & usable • Metadata for discovery & reuse. Link publications & data • Sometimes law, ethics get in the way. We understand. • Limited embargos OK. Recognition is important – always cite data sources • OK to use public money to do this. Do it efficiently. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 11
- 12. EPSRC policy points • Awareness of regulatory environment • Data access statement • Policies and processes • Data storage • Structured metadata descriptions • Permanent identifiers for data • Securely preserved for a minimum of 10 years from last use 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY Compliance expected by 2015
- 13. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 13 DCC Policy Summary http://www.dcc.ac.uk/resources/policy-and-legal
- 14. The Response 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 14
- 15. DCC guidance 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 15
- 16. Roles and Responsibilities What data to keep 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 16
- 18. DCC ‘institutional engagement’ Assess needs Make the case Develop support and services RDM policy development Customised Data Management Plans DAF & CARDIO assessments Guidance and training Workflow assessment DCC support team Advocacy with senior management Institutional data catalogues Pilot RDM tools …and support policy implementation 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 18
- 19. Who (in the UK) is leading on RDM ? Library IT Research Office 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 20
- 20. Survey of UK HE RDM readiness • 61 of 69 responded (> 10% funding from research) • 90% using internal funding for staff, training • 57% filling all or most roles through restructuring • Russell Group: 4.7FTE -> 9.5 FTE within a year • Others: 2.6 FTE -> 3 FTE • Lack of clarity on staff outside central services 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 21 31% 38% 14% 17% Research Support & Commercialisat ion Library or Information Service IT/ Research computing Others Data & charts from Angus Whyte, DCC
- 21. Drivers – UK institutions 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 22 0 10 20 30 40 50 60 70 80 90 100 UK Research Council data policies Government policy on open data Governance of research integrity / academic conduct Strategy to expand support for research EU Horizon2020 policy on data management 92 57 54 54 53 % Agreeing
- 22. Least progress 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 23 0 5 10 15 20 25 Business planning & sustainability Digital preservation & continuity planning Governance of data access & reuse % indicating piloting or live
- 23. What kind of external support is needed? • Advice on retention, selection • Advice on metadata creation for discovery • Specifying tools & infrastructure • Costing • Advocacy to senior management • Developing data catalogues/registers 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 24
- 24. EPSRC asked its researchers… • 75% know of funder’s policy (25% in detail) • 55% know their institution has a policy • 70% are not aware of institutional training or services for RDM • Some contradictory responses 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 25 Thanks to Ben Ryan, EPSRC, for quotes & data
- 25. Services researchers are aware of • Help with data management planning • Help with metadata creation • Training • Backup of research data • Dedicated storage 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 26
- 26. Some selected observations 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 27 The nature of my work is such that it generates no data that doesn't end up in my papers, so I'm unlikely to know about these policies. This is irrelevant to me. I deal with no sensitive data RDM sounds like a gigantic waste of time and I intend to spend as little time on it as possible I am on the point of retiring so taking less interest in these things
- 27. Infrastructure 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 28
- 28. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 29
- 29. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 30
- 30. Data centres are good value! • See Jisc reports on ADS, BADC, UKDA: 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 31 http://www.jisc.ac.uk/whatwedo/programmes/di_directions/strategicdirections/badc.aspx
- 31. Research Data Registry & Discovery Service • Modelled on Research Data Australia • Gain visibility of small data collections • Help drive home distinction between discoverable data & open data • Get evidence on which metadata items deliver reuse potential • Idea from UKRDS report in 2010 • RDA working group coordinating international work 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 32
- 32. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 33
- 33. 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 34
- 34. Pimp your data – make it findable & reusable 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 35 Gking.harvard.edu/data
- 35. On costs • Costs of data curation relatively simple to measure: see work of 4C (4cproject.eu) • Charging and payment are more complex • Funder rules can lead to perverse, inefficient payment systems • Fundamental question is ‘who pays’. This changes the answer to ‘what does it cost’ 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 36
- 36. Commercial services 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 37
- 37. What it means • Project funding can only be spent during projects on direct project costs • Project funding comes with overheads, which universities must use for research infrastructure • Ongoing (‘QR’) money is continuous, relates to research ranking • Important to distinguish business-as-usual from exceptional requirements 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 38
- 38. A research lifecycle 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 39 Time Resources Exceptional zone Normal zone Project end point Business as usual threshold Eligible for project funding
- 39. Being clever with costs • Ongoing costs beyond project end cannot be charged to a grant, but… • ‘Pay once, store forever’ charges are acceptable. • Thus, incentive to outsource long-term curation • Yet universities are only acting as last-resort option in any case – discipline data archives preferred 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 40 Many of these are run by funders
- 40. What stops data reuse • Loss • Destruction • Pride • Gluttony • Ineptitude • Concealment • Bureaucracy • Complexity • Procrastination • Lack of potential 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 41
- 41. Kevin Ashley -Jisc/CNI 2014 - CC-BY 42 “Departments don’t have guidelines or norms for personal back-up and researcher procedure, knowledge and diligence varies tremendously. Many have experienced moderate to catastrophic data loss” Incremental Project Report, June 2010 http://www.flickr.com/photos/mattimattila/3003324844/ 2014-07-10
- 42. Excuses – and responses • “People will ask questions” – So use a data centre or repository • “It will be misinterpreted” – Stuff happens. Also, openness encourages correction • “It’s not interesting” – Let others be the judge – your noise is my signal • “I might get another paper out of it” – Up to a point. We might get more research out of it • “I don’t have permission” – A real problem. But solvable at senior level • “It’s too bad/complicated” –see above • “It’s not a priority” – Unfortunately, funders are making it so. But if you looked at the evidence, it would be your priority as well 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 43 See e.g. Carly Strasser’s blog: http://datapub.cdlib.org/2013/04/24/closed-data-excuses-excuses/
- 43. Citability • Making data available increases citations • Everyone – academic, funder, institution – loves citations • Want evidence? – Alter, Pienta, Lyle – 240%, social sciences * – Piwowar, Vision – 9% (microarray data)† – Henneken, Accomazzi – 20% (astronomy) # 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 44 † Piwowar H, Vision TJ. (2013) Data reuse & the open data citation advantage. PeerJ PrePrints 1:e1v1 http://dx.doi.org/10.7287/peerj.preprints.1v1 * Amy Pienta, George Alter, Jared Lyle, (2010) The Enduring Value of Social Science Research: The Use and Reuse of Primary Research Data. http://hdl.handle.net/2027.42/78307 # Edwin Henneken, Alberto Accomazzi, (2011) Linking to Data - Effect on Citation Rates in Astronomy. http://arxiv.org/abs/1111.3618
- 44. Open scholarly communication • It’s not just publications and/or data • Software, methods, workflows, instruments… • Need to resist the urge to make everything look like a publication 2014-07-10 Kevin Ashley -Jisc/CNI 2014 - CC-BY 45
Notes de l'éditeur
- There are many such stories of unexpected data reuse; these are a few examples. The last, exemplified in the Old Weather project, is seeing the original data being reused for at least the third time and in doing so is helping both climatologist and family historians through a single piece of transcription work. An impressive result.
- There are many such stories of unexpected data reuse; these are a few examples. The last, exemplified in the Old Weather project, is seeing the original data being reused for at least the third time and in doing so is helping both climatologist and family historians through a single piece of transcription work. An impressive result.
- For an audience such as this, I shouldn’t have to explain why data reuse is important. But just in case, and to explain why some things have happened the way they have, I’ll describe some of the drivers. Ensuring that all research data is discoverable and reusable increases the quality of the research that we do. It can add to the data we collect ourselves and can improve the statistical rigour of our results. Exposing data to scrutiny makes it more straightforward to validate or challenge the findings of others. Making data available also improves the speed with which we can do research. If someone else has already gathered the data we need (perhaps for a different end use), we can move directly to the analysis stage of our work, saving both time and money. And saving money increases the efficiency of research. We hope that the money saved lets us do more research, but even if it doesn’t society as a whole will gain. There’s evidence behind this that I’ll come to later, but it is an effective counter to those in some universities who feel that increasing funder requirements for data management simply leads to additional costs with no gain. There is a gain in all these areas, and hence every one of the actors – researchers, their employers, their funders, and society, should be motivated to make this happen.
- An interesting trend to emerge is who is addressing RDM within the unis. The library is leading in most cases and is involved regardless of who’s championing the cause. Research offices are often the lead partner – seemingly for strategic reasons of senior buy-in and financial commitment. IT are only leading in 2 out of the 20 cases and are disengaged / absent in a few others.
- 2014-07-10
- Yet some researchers still aren’t convinced by the rhetoric. Carly Strasser at CDL has listed some of the reasons for not sharing data that she’s encountered – and here are some of my one-line responses. I’m not saying that the concerns aren’t sincere or reasonable but they can all be dealt with and some are positively misguided. The purpose of data centres, for instance, is to make data independently reusable (as stated in the OAIS standard) which relieves researchers of the burden of dealing with questions about it, at the same time as increasing the likelihood that their data will be cited.
- Did I mention that making data available increases citations? This is a win all round. If you don’t believe me, here are three studies from three different areas that all show robust, positive correlations. The effect size varies with discipline, but we have enough evidence now that anyone who says that their area is different needs to come up with evidence to show why.