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Planning for Research Data Management

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Slides from an Open University workshop delivered February 2017.

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Planning for Research Data Management

  1. 1. Planning for Research Data Management 7th February 2017 Wendy Mears, Research Support Librarian library-research-support@open.ac.uk
  2. 2. Overview of session • What is Research Data Management? • Why bother? • Data Management Planning: step-by-step • Questions with a little help from my friends...
  3. 3. What is Research Data Management? “Research data management concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. It aims to ensure reliable verification of results, and permits new and innovative research built on existing information." Digital Curation Centre (2011) Making the Case for Research Data Management http://www.dcc.ac.uk/sites/default/files/documents/publications/ Making%20the%20case.pdf http://www.data-archive.ac.uk/create- manage/life-cycle
  4. 4. Why bother?
  5. 5. Or even worse...
  6. 6. Good data management... • Helps you work more efficiently and effectively – Save time and reduce frustration – Highlight patterns or connections that might otherwise be missed • Enable data re-use and sharing • Allow you to meet funders’ and institutional requirements
  7. 7. Benefits of data sharing...
  8. 8. OU Principles of Research Data Management “Research data must be managed to the highest standards throughout their life-cycle in order to support excellence in research practice. In keeping with OU principles of open-ness, it is expected that research data will be open and accessible to other researchers, as soon as appropriate and verifiable, subject to the application of appropriate safeguards relating to the sensitivity of the data and legal requirements.” OU Principles of Research Data Management, April 2013 http://intranet.open.ac.uk/research-school/strategy-info- governance/docs/CoPamendedJuly2013mergedwithappendix-forintranet.pdf
  9. 9. Data Management Planning • Make informed decisions to anticipate and avoid problems • Avoid duplication, data loss and security breaches • Develop procedures early on for consistency • Ensure data are accurate, complete, reliable and secure • Save time and effort – make your life easier! Data Management Plans are useful whenever you are creating data to:
  10. 10. Data Management Planning DMPOnline https://dmponline.dcc.ac.uk A web-based tool to help you write DMPs according to different requirements. DCC, funder and OU guidance.
  11. 11. The rest of the session...
  12. 12. “Write a paragraph on the aim and purpose of your research.” 1. Introduction and Context
  13. 13. 1. Introduction and Context
  14. 14. “Describe the data aspects of your research, how you will capture/generate them, the file formats you are using and why. Mention how metadata will be created to describe the data, and your reasons for choosing particular data standards and approaches.” 2. Data types, formats, standards and capture methods
  15. 15. 2. Data types, formats, standards and capture methods
  16. 16. 2. Data types, formats, standards and capture methods Metadata tips: • Use disciplinary standards • Create a data file • Use file properties • Use functions in data analysis software, e.g. NVIVO, R, SPSS, Electronic Lab Notebooks
  17. 17. 2. Data types, formats, standards and capture methods
  18. 18. “Detail any ethical and privacy issues, including the consent of participants. Explain the copyright/IPR and whether there are any data licensing issues – either for data you are reusing, or your data which you will make available to others.” 3. Ethics and Intellectual Property
  19. 19. 3. Ethics and Intellectual Property
  20. 20. 3. Ethics and Intellectual Property
  21. 21. 3. Ethics and Intellectual Property Sharing sensitive data: • Gain consent • Anonymise • Restrict access • Lock down (with justification)
  22. 22. 3. Ethics and Intellectual Property Intellectual Property: • Secondary data use • Understanding open licences • Who owns IP of your data?
  23. 23. 3. Ethics and Intellectual Property
  24. 24. “Note who would be interested in your data, and describe how you will make them available (with any restrictions). Detail any reasons not to share, as well as embargo periods or if you want time to exploit your data for publishing.” 4. Access, Data Sharing and Re-use
  25. 25. 4. Access, Data Sharing and Re-use
  26. 26. 4. Access, Data Sharing and Re-use
  27. 27. 4. Access, Data Sharing and Re-use Licensing your data
  28. 28. ORDO Online data sharing services • Figshare • Zenodo • CKAN DataHub • Mendeley Data Directories • re3data Funders’ repository services • UK Data Service ReShare • NERC data centres 4. Access, Data Sharing and Re-use
  29. 29. 4. Access, Data Sharing and Re-use
  30. 30. “Give a rough idea of data volume. Say where and on what media you will store data, and how they will be backed-up. Mention security measures to protect data which are sensitive or valuable.” 5. Short-term storage and data management
  31. 31. 5. Short-term Storage and Data Management • Follow the 3-2-1 rule: • 3 copies • At least 2 formats • 1 offsite
  32. 32. • Shared areas or SharePoint • Zendto • Be wary of Dropbox & similar • ORDO IT support for research: http://intranet6.open.ac.uk/library/main/supporting-ou- research/research-data-management/creating-your-data 5. Short-term Storage and Data Management
  33. 33. 5. Short-term Storage and Data Management
  34. 34. • Thinking ahead will help when you need to share/archive your data • Define processes at project start • Think about: –File naming and versioning –File directory structure –Metadata –File formats –Quality assurance –Data security 5. Short-term Storage and Data Management
  35. 35. 5. Short-term Storage and Data Management
  36. 36. “Consider what data are worth selecting for long-term access and preservation and how you will need to prepare those data for archiving. Say where you intend to deposit the data.” 6. Deposit and long-term preservation
  37. 37. 6. Deposit and long-term preservation Deciding what to keep: • Raw data • Derived data • Data underpinning publications • Code • Methods What are research data in your context? What would others need to understand your research?
  38. 38. 6. Deposit and long-term preservation To allow long-term access to data: • Don't use obscure formats • Don't use obscure media • Don't rely on technology being available • Provide sufficient documentation
  39. 39. For preservation, file formats should be… • Unencrypted • Uncompressed • Non-proprietary/patent-encumbered • Open, documented standard • Standard representation (ASCII, Unicode) Type Recommended Avoid for data sharing Tabular data CSV, TSV, SPSS portable Excel Text Plain text, HTML, RTF PDF/A only if layout matters Word Media Container: MP4, Ogg Codec: Theora, Dirac, FLAC Quicktime H264 Images TIFF, JPEG2000, PNG GIF, JPG Structured data XML, RDF RDBMS Further examples: http://www.data-archive.ac.uk/create-manage/format/formats-table 6. Deposit and long-term preservation
  40. 40. • Metadata is additional information that is required to make sense of your files – it’s data about data. Guidance on disciplinary metadata standards: http://www.dcc.ac.uk/resources/metadata- standards 6. Deposit and long-term preservation
  41. 41. 6. Deposit and long-term preservation
  42. 42. Library Services How we can help • Data Management Plan checking • Support with setting up new projects • Advice on preparation of data for sharing • ORDO • Online guidance • Enquiries Email: library-research-support@open.ac.uk
  43. 43. Useful links • The OU Research Data Management intranet site: http://intranet6.open.ac.uk/library/main/supporting-ou-research/research- data-management • VRE: http://www.open.ac.uk/students/research/activities/lists/organising- your-research • Digital Curation Centre: http://www.dcc.ac.uk/ • DMPOnline: https://dmponline.dcc.ac.uk/ • UK Data Archive: http://www.data-archive.ac.uk/ • MANTRA: http://datalib.edina.ac.uk/mantra/ • The Orb: http://open.ac.uk/blogs/the_orb
  44. 44. Reflection and Questions
  45. 45. Image credits Other cartoons from the Research Data Alliance 4th Plenary, Amsterdam 2014: https://rd-alliance.org/plenary-meetings/fourth-plenary/plenary-cartoons.html (CC-BY) BASF (2007) Crop Design – the fine art of gene discovery, https://www.flickr.com/photos/basf/48372670 13 (CC BY-NC-ND 2.0) Jay Oliver (2005) UGA research in Tifton, GA. June 2005, https://www.flickr.com/photos/ugacommunicati ons/6254516052 (CC BY-NC 2.0) Teddy-rised (2008) Making every litter count, https://www.flickr.com/photos/teddy- rised/2947952302 (CC BY-NC-ND 2.0) Stan Leary (2009) University of Georgia Griffin Campus:Research, https://www.flickr.com/photos/ugacommu nications/6254368548 (CC BY-NC 2.0) Morten Oddvik (2011) Papers, https://www.flickr.com/photos/mortsan/543041854 5 (CC BY 2.0) Lars Rosengreen (2012) Using a GoPro camera to collect data on pollinators, https://www.flickr.com/photos/46369606@N04 /7543827396/ (CC BY-NC-ND 2.0) Casldlyrose (2009) Be Prepared https://www.flickr.com/photos/calsidyrose/35524 73207 (CC-BY 2.0) Caleb Roenigk (2012) Writing? Yeah. https://www.flickr.com/photos/crdot/685553826 8/ (CC-BY 2.0) Jamie Henderson (2010) Day 22 https://www.flickr.com/photos/xelcise/42967348 26 (CC-BY-NC-ND 2.0) PHDComics.com (2007) http://www.phdcomics.com/comics/archiv e.php?comicid=814 (CC-BY 2.0) Sybren Stuvel (2008) Frustration https://www.flickr.com/photos/sybrenstuvel (CC- BY-NC-ND 2.0) Brian Yap (2012) Blowing Questions https://www.flickr.com/photos/sybrenstuvel (CC- BY-NC 2.0)