RIN Disciplinary Case Studies: understanding the information needs of life science researchers

  • 1,083 views
Uploaded on

Presentation given by Stuart Macdonald at the IASSIST 2010 Conference, Cornell University, Ithaca, New York. 1 - 4 June 2010.

Presentation given by Stuart Macdonald at the IASSIST 2010 Conference, Cornell University, Ithaca, New York. 1 - 4 June 2010.

More in: Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
1,083
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
7
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. CC image by ecstaticist courtesy of Flickr – http://www.flickr.com/photos/ecstaticist/1337749333/ RIN Disciplinary Case Studies: understanding the information needs of life science researchers Stuart Macdonald Researcher EDINA & Data Library University of Edinburgh [email_address] IASSIST 2010, Cornell University, 2 June 2010
  • 2.
    • Advances in new ICT technologies,
    • the data deluge, funding body
    • requirements have brought major
    • changes for life science researchers
    • The eight-month RIN-funded project
    • was carried out by a team of social
    • scientists and information service
    • specialists from ISSTI, DCC, and IS
    • at the University of Edinburgh.
    • Principal Investigators:
    • Professor Robin Williams
    • (ISSTI) and Graham Pryor (DCC).
    • The aim was to identify ‘how
    • information-related policy, strategy
    • and practice might be improved to
    • meet the needs of researchers’.
    CC image by Sean McGrath courtesy of Flickr – http://www.flickr.com/photos/mcgraths/3597037843/
  • 3. CC image by Hurley Gurley courtesy of Flickr – http://www.flickr.com/photos/hurleygurley/5134027/ Seven case studies were conducted across a diverse range of laboratories and research groups from botany to clinical neuroscience. Deployed a range of quantitative methods and tools designed to ‘enhance understanding of how researchers locate, evaluate, organise, manage, transform and communicate information sources as an integrated part of the research process’. 5-day information diaries (x55) F-2-F interviews, (x24) Cognitive mapping (1 per case) Focus groups (1 per case)
  • 4. CC image by Ecstaticist courtesy of Flickr – http://www.flickr.com/photos/ecstaticist/321582062/ CC images by Elephantik courtesy of Flickr – http://www.flickr.com/photos/joemaguiredesign/2300745142/
    • Diversity of Cases:
    • Enormous range of information use and exchange (formal/informal, internal/external) across the research groups
    • Activities of individual members of research groups strongly influenced by their role, expertise and responsibility
    • There is much talk of ‘big science’, and our initial research design presumed that we would be studying large-scale formal collaborations.
    • We found most research groups in the life sciences operate on a relatively small scale.
  • 5. Cognitive Maps Adapted from a lifecycle model developed by C. Humphrey (2006) different colours represent different types of activity within an information cycle. CC image by philippeleroyer courtesy of Flickr – http://www.flickr.com/photos/philippeleroyer/3944665610/
  • 6. CC image by Tuis courtesy of Flickr – http://www.flickr.com/photos/tuis_imaging/515380689/
    • Information patterns and behaviour:
    • Researchers discover and gain access to information mainly via direct access to web-based resources – little use of centralised services (e.g. library)
    • Google – the ultimate enabler often delivering serendipitous contextual information
    • Limited awareness of available services and resources but loyalty to those they like or trust
    • Researchers used informal and trusted sources of advice from colleagues, rather than institutional services to help them identify information sources & resources
  • 7. CC image by Darwin Bell courtesy of Flickr – http://www.flickr.com/photos/darwinbell/300495624/
    • Researchers however were more ready to share methods and tools than experimental data
    • Immaterial to researcher whether they need to use an information portal, commercial website, publisher’s web service or bibliographic database – orientation is primarily pragmatic!
    • The use of Web 2.0 tools for scientific research purposes was far more limited than expected
    • Centralised training not specific enough for kinds of refined utilities being used
  • 8. CC image by CaptPiper courtesy of Flickr – http://www.flickr.com/photos/piper/22584430/ ‘ Impressionistic’ taxonomy of case study research data Some form of taxonomic ordering is needed to facilitate a comparative analysis of the diversity of our cases Our findings proposed a simple two-by-two matrix along two dimensions: • Volume of data being handled • Complexity or heterogeneity of that data
  • 9. CC images courtesy of Flickr – http://www.flickr.com/photos/11304375@N07/2326596014/ Credit: M. M. Alvarez, T. Shinbrot, F. J. Muzzio, Rutgers University, Center for Structured Organic Composites  
    • Researchers have concerns about misuse of research data, ethical restraints and IPR
    • Some disciplines lend themselves more than others to ‘openly sharing’
    • Researchers retain a keen sense of ownership towards data which represents their ‘competitive
    • advantage’ and ‘intellectual capital’
    • Researchers felt that only they had the subject knowledge to curate their own data
    Research data sharing: sharing of complex data is more problematic than sharing of research results via publications which remains the primary vehicle for dissemination and reward
  • 10. CC image by Myxi courtesy of Flickr – http://www.flickr.com/photos/myxi/4623192231/
    • Researchers’ perceptions of future challenges and needs:
    • Bioinformatics support (centralised, preferably local & easily accessible)
    • Standardisation (data can be highly variable, different forms and formats, specificity of software)
    • Data deluge and technical barriers (fear that there will be too much data to handle, process, even look at)
    • Support for development of data curation should not be at the expense of funding for research
    • Short term nature of funding may frustrate attempts to build & sustain data repositories
  • 11. CC image by cyberdees courtesy of Flickr – http://www.flickr.com/photos/cyberdees/3760447610/
    • Challenges and conclusions:
    • Institutional information services need to develop services that are seen as beneficial to researchers and can add value to the research process (tools, advice, subject-specific documentation and training, infrastructure)
    • Development of research data curation skills and training to cater for diversity of research output and practice as viable career option
    • Institutional information service need to develop intuitive tool-based support for practitioners to assist in the curation of their own data
    • Research and funding councils, information service providers and institutions need to understand the practices of the research communities if policies and strategies are to be effective - A single approach to the future of life sciences or a one-size-fits-all information policy will not be productive or effective
  • 12. CC images by enggul courtesy of Flickr – http://www.flickr.com/photos/enggul/2361808668 / Thanking You! Acknowledgements: Dr. Wendy Marsden (ISSTI / Innogen) Ann Bruce (ISSTI / Innogen) The full report ‘Patterns of information use and exchange: case studies of researchers in the life sciences’ is available at: http://www.rin.ac.uk/case-studies . All images CC Attribution-NonCommercial 2.0 Generic or Attribution 2.0 Generic