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Research Integrity Advisor and Data Management


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Dr Paul Wong from the Australian Research Data Commons presented at the University of Technology Sydney's RIA Data Management Workshop on 21 June 2018. In partnership with the Australian Research Council, the National Health and Medical Research Council, the Australian Research Data Commons, and RMIT University, this is part of a national workshop series in data management for research integrity advisors.

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Research Integrity Advisor and Data Management

  1. 1. Paul Wong Research Integrity Advisor Data Management Workshop UTS, 21 June 2018
  2. 2. The Australian Research Data Commons (ARDC) makes Australia’s research data assets more valuable for researchers, research institutions and the nation. Research Data Australia Cite My Data / DOIs minting In 2016/7, 163 workshops, forums, and webinars etc., over 8,000 participants Developed online resources, guides, videos etc. Co-funded 304 data projects, $62Min total 2018 focus is Data Enhanced Virtual Labs (STEM& HASS)
  3. 3. • 40+ guides organised around different topics • Content is a moving target – changing policy landscape, new practices etc. • Designed as a community resource • If you see gaps, we want your help to make them better
  4. 4. • A dedicated set of webpages on data management • A community resource • If you see gaps, we want your help to make them better
  5. 5. Research data: as input & output Research data may include: ü Laboratory and field notes ü Raw experimental data ü Analysed data ü Simulations and software ü Databases ü Clinical data, including clinical records ü Questionnaires/surveys ü Images and photographs ü Audio-visual materials Moynihan's field notes, Panama, 1958 – CC BY Screen capture of “Com puter sim ulation of M arch 22, 2014 landslide event near O so, W ashington, by David L. G eorge and Richard M . Iverson, USG S” hKr7g CC BY
  6. 6. Creative arts research data Research data in the creative arts may include: ü Audio-visual recordings of a creative work ü Visual diaries ü Journals ü Drawings ü Photographs ü Manuscripts ü Musical annotations ü 3D models
  7. 7. Research Data: a Broad Church Hand written letters Images or photos Soil samples Tissue samples Archeological dig sites ….. Scanned & OCR version Scanned digital version Analysed result of samples Analysed result of samples 3D models of the dig site ….. Physical Digital ARDC’s primary focus is digital data
  8. 8. Why Bother? Why managing (digital) research data? In fact, why bother managing anything? • Prevent bad things from happening. • Enable good things to happen.
  9. 9. Data and Research Integrity Nature 533, 452–454 (26 May 2016) doi:10.1038/533452a. Reprint with permission © 2016 Macmillan
  10. 10. Data and Research Integrity “The Availability of Research Data Declines Rapidly with Article Age”, Vine et al, Current Biology, Volume 24, Issue 1, p94–97, 6 January 2014 • “For papers where authors reported the status of their data, the odds of the data being extant decreased by 17% per year...” • “Responses included authors being sure that the data were lost (e.g., on a stolen computer) or thinking that they might be stored in some distant location (e.g., their parent’s attic) to authors having some degree of certainty that the data are on a Zip or floppy disk in their possession but no longer having the appropriate hardware to access it.”
  11. 11. Make Data Awesome Open Research Data Collection Showcase communities/projects/open-research-data-collection #Dataimpact stories Data contribution to Research Impact in the U.K. the-value-of-open-data/data-engagement-and-impact
  12. 12. Data Management in Practice • One of ANDS’ guides to outline, in an easy to understand practical framework, how research data can be managed effectively in an institutional setting. • 15 key points – with short descriptions, 7 pages long. • Incorporating project management best practice • Shared responsibilities model • Continual data curation approach • Road tested with librarians, data managers, researchers and research support staff
  13. 13. The Current Thinking: FAIR Findable, Accessible, Interoperable, Reusable 15 principles to ensure research data is FAIR Mark D. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data (2016). DOI: 10.1038/sdata.2016.18 “FAIRness is a prerequisite for proper data management and data stewardship”
  14. 14. anagem ent-in-practice.pdf
  15. 15. Continual data curation across domains
  16. 16. Data Curation as Documentation Assigning metadata (structured data about the data) • Who collected the data? • Who funded the research project? • When (and where) was it collected? • Instruments and setting for collecting the data? • Title of the dataset • Methods used to process the data • Etc. etc.
  17. 17. Light Touch Heavy Duty EML ISO 19115 Darwin Core Data citation Ecological Geographic Biological Metadata Structured Detailed Machine readable Structured Minimal Human readable
  18. 18. What is Data Citation? Data citation refers to the practice of providing a reference to data in the same way as researchers routinely provide a bibliographic reference to outputs such as journal articles, reports and conference papers. Citing data is now recognised as one of the key practices leading to recognition of data as a primary research output. identifiers/data-citation
  19. 19. Data Citation Standard A standard citation would include the following elements: Author(s) (Year) : Title. Publisher(s). DOI (if used) Hanigan, Ivan (2012): Monthly drought data for Australia 1890-2008 using the Hutchinson Drought Index. The Australian National University Australian Data Archive. Alternatively, Author(s) (Year): Title. Version. Publisher(s). ResourceType. Identifier Bradford, Matt; Murphy, Helen; Ford, Andrew; Hogan, Dominic; Metcalfe, Dan (2014): CSIRO Permanent Rainforest Plots of North Queensland. v2. CSIRO. Data Collection.
  20. 20. Institutional Policy and Procedures Support services - people and other means of providing advice and support IT Infrastructure - the hardware, software and other facilities Metadata management - so that data records can be meaningful and fit for purpose Institutional Data Management Framework Pre Research
  21. 21. Data Management Plan Planning • data organisation and storage; • metadata standards and guidelines; • backups; • archiving for long-term preservation; • version control and derived data products; • data sharing or publishing intentions, including licensing; • ensuring security of confidential data; • data synchronisation; and • governance, roles and responsibilities. Pre Research
  22. 22. Storage requirements may vary across domains
  23. 23. Publishing and Sharing Data Metadata Research Data Open Open Open Closed Closed Open Closed Closed Publishing and Sharing data ≠ Open Access to data “Open” and “Closed” are relative concepts. “Closed” ≈ conditional access based on individual permission “Closed” ≈ conditional access based on role Post Research mediated Personal Data: obtain consent to share from participants at the start!
  24. 24.
  25. 25. Ethics Clearance and Data Access: A Case Study Data Managing and Sharing Research Data: A Guide to Good Practice, SAGE 2014 data/book240297 https://com m ons.wikim 3AFoot_and_M outh_Disease_M Colin Sm ith [CC BY-SA 2.0] (http://creativecom m], via W ikim edia Com m ons from W ikim edia Com m ons)
  26. 26. Ethics Clearance and Data Access: A Case Study Health and Social Consequences of the Foot and Mouth Disease Epidemic in North Cumbria, 2001- 2003 (M. Mort Lancaster University 2006, funded by the Department of HealthUK, Study Number 5407) • 54 local people were recruited to write weekly diaries over 18 months to describe their lives and the recovery they observedaroundthe area • The study was supplemented with interviews and focus group discussions that included other stakeholders • The study obtained consent from participants before the research but did not get consent for sharing and archiving data • The research team and the Department of Health wanted to share and archive the data after the completionof the research. • Had toget consent retrospectively and neededexpert advice fromcopyright specialists
  27. 27. anagem ent-in-practice.pdf The framework treats DM as a set of coordinated activities to preserve the evidence base of research findings and to make the evidence base more accessible and reusable in the long run.
  28. 28. Senior Data Management Specialist +61 2 6125 0586 Dr Paul Wong With the exception of logos, third party images or where otherwise indicated, this work is licensed under the Creative Commons 4.0 International Attribution Licence. ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program.