Managing and sharing data
Download as PPTX, PDF1 like828 views
The document discusses the importance of managing and sharing research data, highlighting a commitment to open science by the European Research Council. It outlines benefits such as increased citations and institutional returns from shared data, while also addressing barriers like data ownership and sharing restrictions. Additionally, it emphasizes the need for improved systems and training for researchers to develop good practices in data management and sharing.
Managing and sharing data
- 1. Managing and sharing data Sarah Jones DCC, University of Glasgow sarah.jones@glasgow.ac.uk Twitter: @sjDCC ERC Workshop on Research Data Management and Sharing 18-19 September 2014 , Brussels Funded by:
- 2. European Research Council policy Commitment to open science from the start: "it is the firm intention of the ERC Scientific Council to issue specific guidelines for the mandatory deposit in open access repositories of research results – that is, publications, data and primary materials – obtained thanks to ERC grants, as soon as pertinent repositories become operational." Statement on Open Access, December 2006 Image CC BY-SA 3.0 by Greg Emmerich www.flickr.com/photos/gemmerich/6365692655
- 3. Why make data available?
- 4. Sharing leads to breakthroughs www.nytimes.com/2010/08/13/health/research /13alzheimer.html?pagewanted=all&_r=0 “It was unbelievable. Its not science the way most of us have practiced in our careers. But we all realised that we would never get biomarkers unless all of us parked our egos and intellectual property noses outside the door and agreed that all of our data would be public immediately.” Dr John Trojanowski, University of Pennsylvania ... increases the speed of discovery
- 5. Returns for institutions “If an institution spent A$10 million on data, what would be the return? The answer is: more publications; an increased citation count; more grants; greater profile; and more collaboration.” Dr Ross Wilkinson, ANDS www.ariadne.ac.uk/issue72/oar-2013-rpt
- 6. Researchers get a citation boost “Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression.” Piwowar H., Day, R and Fridsma, D. (2007) Sharing detailed research data is associated with increased citation rate. DOI: 10.1371/journal.pone.0000308
- 7. But, there are also barriers... Who owns the data? • Researchers? • University? • Commercial partners? • Funders? • … People are often misinformed about who owns the data. It is particularly hard to determine in international projects or ones with industry. Restrictions on sharing • Patentable data • Commercial sensitivities • Personal, identifiable data • Lack of consent • … There are legitimate reasons to agree embargo periods, impose conditions, or to share only some of the data. However, these are often given as reasons not to share data at all. www.dcc.ac.uk/sites/default/files/documents/events/ workshops/IHW-2013/UKDA-barriers-to-data-sharing.pdf
- 8. And opportunity costs By Emilio Bruna http://brunalab.org/blog/2014/09/04/the-opportunity-cost- of-my-openscience-was-35-hours-690 For his most recent paper: 1. Double checking the main dataset and reformatting to submit to Dryad: 5 hours 2. Creating complementary file and preparing metadata: 3 hours 3. Submission of these two files and the metadata to Dryad: 45 minutes 4. Preparing a map of the locations: 1 hour 5. Submission of map to Figshare: 15 minutes 6. Cleaning up and documenting the code, uploading it to GitHub: 25 hours 7. Cost of archiving in Dryad: US$90 8. Page Charges: $600
- 9. What needs to change? Conclusions from Emilio Bruna: • Develop a better system of incentives from the community for archiving data and code • Teach our students how to do this NOW - it’s much easier if you develop good habits early • Minimise the actual and opportunity costs We need to stop telling people “You should” and get better at telling people “Here’s how”
- 10. What is involved in data curation • Data Management Planning • Data creation • Annotating / documenting data • Analysis, use, versioning • Storage and backup • Publishing papers and data • Preparing for deposit • Archiving and sharing • Licensing • Citing… Plan Create Document Use Share Publish
- 11. Data Management Plans Brief plans to determine how data will be created, managed and shared. DMPs usually cover: 1. Description of data to be collected / created 2. Standards and methodologies for data collection & management 3. Any issues or restrictions due to ethics and Intellectual Property 4. Plans for data sharing and access 5. Strategy for long-term preservation DMPs are often submitted as part of grant applications, but are useful whenever you’re creating data.
- 12. Help with DMPs A web-based tool to help researchers write data management plans https://dmponline.dcc.ac.uk Framework for creating a DMP A list of common elements explaining why they are important and giving example answers www.icpsr.umich.edu/icpsrweb/content/ datamanagement/dmp/framework.html www.dcc.ac.uk/sites/default/files/documents /resource/DMP_Checklist_2013.pdf Examples plans www.dcc.ac.uk/resources/data-management- plans/guidance-examples
- 13. Managing and sharing data: a best practice guide http://data-archive.ac.uk/media/2894/managingsharing.pdf
- 14. Training materials FOSTER project • Open science training • Courses across EU • Portal to OA materials • Guidance on Horizon 2020 • Free online training course • Aimed at PhD students • Case studies, quizzes etc • Data handling tutorials – R – SPSS – ArcGIS – Nvivo http://datalib.edina.ac.uk/mantra www.fosteropenscience.eu
- 15. DCC tools catalogue A catalogue of RDM tools for different audiences. Tools for researchers focus on data handling, managing workflows, citation and impact. www.dcc.ac.uk/resources/external/tools-services
- 16. Tools to help with RDM activities impactstory.org Citation & impact owncloud.org www.datacite.org thedata.org www.taverna.org.uk www.myexperiment.org www.labtrove.org Documentation & metadata dataup.cdlib.org Workflow management Storage & collaboration
- 17. Metadata standards catalogue Use standards wherever possible for interoperability www.dcc.ac.uk/resources/ metadata-standards
- 18. Data repositories http://databib.org http://service.re3data.org/search
- 19. 1. How do you foster open science? • Make it feasible to comply – provide tools and infrastructure • Train people early in their careers • Incentivise openness • Listen to researchers and learn from their experience about what doesn’t work • Follow up on any demands made in policies
- 20. 2. Who is responsible for providing infrastructure and support? Discipline Funders Institution Third-party services National provider Data centres e.g. via NERC Institutional support for discipline-specific tools e.g. Monash MeRC partnership on tools like OMERO National brokerage of deals with third-party providers e.g. Jisc Janet deals with Arkivum And what about co-ordination?
- 21. 3. Who should pay? Funding Research Data Management "A conversation with the funders” The DCC held a special event on this topic in the UK, but there’s still a long way to go www.dcc.ac.uk/events/research-data- management-forum-rdmf/ rdmf-special-event-funding- research-data-management
- 22. Thanks – any questions? DCC guidance, tools and case studies: www.dcc.ac.uk/resources Follow us on twitter: @digitalcuration and #ukdcc
Editor's Notes
- #3: Quite forward-thinking for such an early OA policy to be framed in terms of data and primary materials too, not just publications.
- #6: He was making a comparison with the Hubble telescope, which A$1.5 billion is spent on each year. The cost of the Hubble archive (A$1 million per annum) is just a fraction of this, but given the OA mandate, they’ve see the research publications produced by Hubble discoveries double.
- #7: There have been lots of studies in this area since that show a demonstrable citation boost, though not as high as 69%. This figure was for microarray data from cancer trials and it seems that the early datasets had a particularly strong impact and came from authors who were well-cited. A more realistic figure across the board is probably 10-30% increase.