RIN case studies in the life sciences: findings on data management

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Presentation by Aaron Griffiths, Research Officer at the Research Information Network at the Embedding Institutional Data Curation Services in Research (EIDSCR) workshop on 14 October 2009.

http://eidcsr.blogspot.com/2009/09/eidcsr-workshop-on-14-october.html

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RIN case studies in the life sciences: findings on data management

  1. 1. RIN case studies in the life sciences:  findings on data management Aaron Griffiths 14 October 2009
  2. 2. Forthcoming  RIN/British Library report:  Case studies in the life sciences: Understanding  researchers’ information needs and uses (November 2009) Research by ISSTI and DCC (Edinburgh)
  3. 3. RIN case studies aim:  “To enhance understanding of how researchers  locate, evaluate, organise, manage, transform  and communicate information sources as an  integrated part of the research process, with a  view to identifying how information‐related  policy, strategy and practice might be  improved to meet the needs of researchers.”
  4. 4. Case study research teams 1. Animal genetics and animal disease genetics 2. Transgenesis in the chick and development  of the chick embryo 3. Epidemiology of zoonotic diseases 4. Neuroscience 5. Systems biology 6. Regenerative medicine 7. Botanical curation
  5. 5. Research methods • Probes (information lab books) • Interviews • Focus groups
  6. 6. Information flow maps
  7. 7. Animal Genetics and Animal Disease Genetics 
  8. 8. Transgenesis in the chick and development of  the chick embryo
  9. 9. Botanical curation
  10. 10. Getting to grips with managing data 1. There is little evidence of planned data  management as standard practice 2. Confusion over terms has implications for  practice 3. Effective curation needs human infrastructure,  and the more local the better
  11. 11. A culture of sharing – with caveats • Ethos of sharing in the life sciences • Different modes of sharing
  12. 12. Constraints on sharing • Barriers to sharing and re‐using:  – career imperatives – protectiveness – confidentiality – lack of trust in cyberspace • Provisos for sharing
  13. 13. Needs for data services & support • Most groups need more locally‐available  support on handling data • Challenges include data volumes and  standardisation requirements • Funding concerns over data curation
  14. 14. Conclusions • Gulfs between practices and e‐science visions • Diversity of research and information flows • Policy to be informed by researchers’ practices
  15. 15. Recommendations: funders • Engage further with researchers to identify constraints  and develop more experimental policies to build upon  existing information sharing • Define more closely which data and information they  expect to be shared, to what ends and under what  circumstances • Monitor the development of hybrid information  support roles • Assess national requirements for skills in research data  curation and support
  16. 16. Recommendations: HEIs • Attend to features of current professional  formation processes ‐ including training and  career development, and professional  recognition and reward structures ‐ which  currently inhibit the effective use and  exchange of information
  17. 17. Recommendations: library and  information service providers • Work towards better portals and tools to  identify information resources • Work towards developing easy‐to‐use, tool‐ based support for researchers to undertake  their own data curation • More active engagement between data  producers and curators
  18. 18. aaron.griffiths@rin.ac.uk

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