Open data for open scholarship - where we are
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This document discusses open data and open scholarship. It provides examples of how data from different disciplines can be reused in new contexts, highlighting the benefits of open data for research quality, speed, and cost effectiveness. It also outlines funder policies requiring data to be shared and challenges for universities in supporting open data. Overall it advocates for making research data discoverable and reusable to further open scholarship.
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Open data for open scholarship - where we are
- 1. Open data for open scholarship: where are we? 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. 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 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 2
- 3. Before where - WHY? 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 3
- 4. Data reuse stories • The palaeontologist who saved years of work with archaeological data 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 4
- 5. What a paleontologist looks at 1m Now 100 million years ago 25m 50m 75m 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 5
- 6. What a paleontologist looks at 100,000 500,000 750,000 Now 1m 1 million Now years 100 million years ago 25m 50m 75m 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 6
- 7. What an archaeologist looks at 100,000 500,000 750,000 Now 1 million years 100,000 years ago 75,000 25,000 50,000 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 7
- 8. Data reuse stories • The palaeontologist who saved years of work with archaeological data • The 19th-century ships logs that help us model climate change 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 8
- 9. The Old weather project Data for research, not from research 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 9
- 10. 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 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 10
- 11. Data reuse - messages Often your data tells stories that your publications do not Not all data comes from other researchers Discipline-bounded data discovery doesn’t give us all we need or want One person’s noise is another person’s signal 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 11
- 12. Data reuse from Hubble 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 12
- 13. G8UK - Endorses OA Open Data Charter Policy Paper 18 June 2013 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 13
- 14. 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 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 14
- 15. Open scholarship – the wider picture • Not just about open papers, open data • Software, methods, workflows are all important • Data need not be open – but its existence must be • Data: DISCOVERABLE & REUSABLE 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 15
- 16. FUNDER POLICY UNIVERSITY RESPONSE 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 16
- 17. Funder requirements • UK http://www.epsrc.ac.uk/about/standards/researchdata/Pages/policyframework.aspx • USA – NSF, NEH, NIH • Europe • Denmark – in development • Most place burden on researcher – some on the institution 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 17
- 18. 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. 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 18
- 19. DCC Policy Summary http://www.dcc.ac.uk/resources/policy-and-legal 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 19
- 20. 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 20
- 21. Research data centres are good value! • See Jisc reports on ADS, BADC, UKDA: • Returns on investment between 400% and 1200% http://www.jisc.ac.uk/whatwedo/programmes/di_dir ections/strategicdirections/badc.aspx 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 21
- 22. Research Data Centres – the solution! MANY AREAS OF RESEARCH HAVE NO DATA CENTRE TO SERVE THEM 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 22
- 23. INSTITUTIONAL SERVICES 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 23
- 24. DCC guidance 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 24
- 25. Roles and Responsibilities What data to keep 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 25
- 26. Some example services • Storage – persistent, shareable • Permanent, citeable identifiers • Data Management Planning (DMPOnline, DMPTool) • Database as a service (e.g. Oxford ORDS) • Embed tools in Excel – Dataup, others • Workflow management - Taverna 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 26
- 27. 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 27
- 28. Make data creation easier 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 28
- 29. Make data citable • 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) # # Edwin Henneken, Alberto Accomazzi, (2011) Linking to Data - Effect on Citation Rates in Astronomy. http://arxiv.org/abs/1111.3618 * 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 † 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 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 29
- 30. http://dataintelligence.3tu.nl/en/home/ Choice of RDM training materials for librarians Up-skilling for data http://datalib.edina.ac.uk/mantra/libtraining.html 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 30
- 31. Make data discoverable • Data must be discoverable to be reused • Alone, or in conjunction with publication • Institutional catalogues, national data registries – JISC is piloting through DCC • We are copying Australian approach 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 31
- 32. Pimp your data – make it findable & reusable Gking.harvard.edu/data 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 32
- 33. Commercial services 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 33
- 34. SWEDEN DENMARK? CANADA 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 34
- 35. My message to you • Help researchers understand the benefits to them of sharing their data • Help them discover & reuse data • Give them tools that help the process • Work to ensure they get credit for data citation 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 35
- 36. My message to researchers • The credit belongs to you • The data belongs to all of us • Share, and we all reap the benefits 2014-10-07 Kevin Ashley –Confoa-2014 - CC-BY 36
Editor's Notes
- 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.
- 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.
- Many of you may be familiar with this graph from the Hubble Space telescope data archive. It tells the same story in a different way, and also tells a story about the transformation of astronomy as a discipline. In the days of photographic plates, sharing (analogue) astronomical data was difficult. Digital instruments transformed this, and some time around 2000, more research was being done with old data than with new data. I could be more specific about this if the data behind this graph was made available, incidentally!
- 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.
- 2014-10-07