Wild data: collaborative e-research and university libraries

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Paper presented by Mary Anne Kennan, Kirsty Williamson, Graeme Johanson and Shonali Krishnaswamy at RAILS7, 10 May 2011

Paper presented by Mary Anne Kennan, Kirsty Williamson, Graeme Johanson and Shonali Krishnaswamy at RAILS7, 10 May 2011

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  • 1. Mary Anne Kennan, CSU Kirsty Williamson, CSU & Monash Graeme Johanson, Monash Shonali Krishnaswamy, Monash Wild data: collaborative e-research and university libraries Photo credit: Cathy Powers, President APSV
  • 2. Data Management
    • ‘ Deluge’ of scientific and research data
    • Associated issues of capture and management using IT, e.g., repositories (Hey 2003)
    • Universities and university libraries interested in managing data – a “natural fit” (Read 2007)
    • Value of data increased by:
      • use beyond original creating community
      • being interconnected, networked, shared, used and re-used (Borgman 2007)
    • New role for university libraries (ANDS, repository movement, open access)
    • Strategic investments by Australian academic libraries in data repositories which work with other shared technology-enhanced research infrastructures e.g., ‘eResearch’, ‘Cyberinfrastructure’, ‘eSocial Sciences’ and ‘The Grid’
  • 3. Wild data?
    • Data created and held outside of formal ‘academic’ science, often not generated by professional work, e.g., by environmental voluntary groups (EVGs)
    • Largely inaccessible data outside those often-small EVGs
    • Potential value of wild data for:
      • science, research and participative decision-making (Callon, Lascoumes & Barthe 2009)
      • academic and other environmental researchers
    • Management of data by EVGs may be:
      • poor or non-existent
      • haphazard and spasmodic regarding quality control
  • 4. Pilot project
    • Exploring kinds of data sought, generated, stored and shared by members of an EVG (Australian Plants Society of Vic)
    • Investigating members’ views about potential innovative approaches to collection and storage of data and information
    • Investigating how data can be managed and shared effectively in the future
    • Exploring possible collaborative role for university libraries in management of wild data
    • (Australian universities (ANDS etc.) at forefront of research and practice to promote better management of data created by research)
  • 5. Research Questions for Pilot
    • What data are collected by members of APSV?
    • How do they manage and store data?
    • How do they disseminate their knowledge?
    • What are data and knowledge management issues?
    • In what ways can university repositories assist?
  • 6. The Australian Plants Society Victoria (APSV)
    • APS Branches in every state
    • 1,700 Victorian members
    • APSV begun in Melbourne in 1957
    • Name change in the late 1990s:
      • from ‘Society for Growing Australian Plants’ (SGAP)
      • to ‘Australian Plants Society’ – reflecting broader approach to include, e.g., researching, observing, and conserving
    • Emphasis of members varies, e.g.,
      • Cultivation of Australian plants (priority of gardens)
      • Broader ‘field naturalist’ approaches
      • Strong scientific interest
      • Social engagement with like-minded people
  • 7. APS (Cont.)
    • 27 Study Groups (Australia wide)
      • focus on particular species, e.g., acacia, correa
      • attempt at more scientific activities
    • Many early members wanted focus just on being good indigenous gardeners
    • Others wanted to improve scientific credibility
    • Sources
      • John Walter, SGAP: The Story of Arthur Swaby and the Society for Growing Australian Plants , Australian Plants Society Inc, 2007.
      • Phil Hempel, 2007 survey of APSV members
    • APSV URL: http://www.apsvic.org.au/
  • 8. Pilot Study Method
    • Interpretivist/constructivist research philosophy
    • Ethnographic method and interview technique
    • Purposive sample to reflect membership of APSV
    • 15 interviews of 1-2 hours, semi-structured and audio-taped
    • Analysis through identification of categories and themes
  • 9. Participant Analysis Sample ages closely reflect age profile of ASPV: 88% of membership was aged over 50 in 2007 ( Phil Hempel, 2007 survey of APSV members) Gender Age Length APSV Membership 7 male 8 female 40-49 1 50-59 4 60-69 5 70-79 3 80-89 2 1-5 years 1 6-10 years 3 11-15 years 3 21-25 years 3 30+ years 5
  • 10. Main Kinds of Data Collected
    • Photographs – all participants
    • Location (often from GPS)
    • Notes about habitats, plant observations (e.g., flowers, fruit, colours, distortions, rarity, height)
    • Specimens (cuttings and seeds)
    • Plant lists (generated from plant observations)
  • 11. Main Storage Methods
    • Computers (photos and spreadsheets)
    • Hand-written notes ( sometimes methodically filed)
    • Fridges (for cuttings)
    • Databases (personal and shared e.g. NatureShare)
    • Websites (personal and group-based)
    • CDs
    • Memory
  • 12. Knowledge Dissemination: Major Methods
    • Many print publications (New edition of Flora of Melbourne underway)
    • Newsletters – state-wide, district and study group
    • Databases (e.g.NatureShare a major enterprise) http://natureshare.org.au/
    • Websites
  • 13. Multiple Knowledge Dissemination Approaches
    •  
    • “ I do have a number of websites that I produce that nobody can access for some strange reason. We’re still trying to work out why Google can’t find them.” (Interviewee 9)
    •  
    • “ And some of that stuff has been published in newsletters and that sort of stuff and little articles. But it never really [is] collated to anything.” (Interviewee 15)
  • 14. Data & Knowledge Management Issues
    • Variety of approaches and publications (Strengths and weaknesses of district and study groups and strong local loyalties)
    • Different goals of individuals and groups (Some members still espouse original aim of SGAP; others take broader ‘field naturalist’ view)
    • Generation of different databases and websites by individuals and groups
    • Some co-ordination and great willingness to share, but some expression of need for individual control
    •   Some lack of technological expertise and skills but some strengths too
    • Lack of training in good information and knowledge management principles
    • Lack of time to manage data effectively
    • Oversight, data quality, management of “errors”
  • 15. Willingness to Share vs Desire for Control
    • “ People have been very willing to share … really anytime that we’ve asked someone… [for example] for a photo for a talk or something, you just never get a ‘no’ really.” (Interviewee 8)
    •  
    • “ I'd be lying if I said [there] … wasn’t a certain amount of pride… and it's nice to be recognised because this is your work … And one of the issues I see [in contributing to a shared database] … is if you put all of your effort into that then you lose that recognition.” (Interviewee 15)
  • 16. Local Loyalties, Problems of Replication and Lack of Time
    • “ (One member is) recording things (for our district group) … I don’t know whether we’d do it twice (to contribute to NatureShare) … because it’s another step and the loyalties are with the group and the local area and you would have to say that this is another – well just it is another level. … It’s just one more demand on your time.” (Interviewee 12)
  • 17. Need for Accurate Information
    • “ It really depends on who’s providing it. It’s too easy to get wrong information online. [Sometimes] ... I think ‘that just can’t possibly be there, it’s got to be a mistake’. … That’s one of the big problems of freely being able to put information on, because I think it then becomes useless.” (Interviewee 7)
  • 18. Role for Academic Libraries
    • Valuable data, multiple approaches
    • Data from EVGs may benefit “science”
    • Academic libraries traditionally boundary spanning (e.g., Allen 2005 ,Corrall 2010): another opportunity here
    • Data management role increasing for academic libraries and librarians
    • Libraries can integrate data with literature to create a world that allows researchers and readers to see the whole knowledge production cycle (Fink et al., 2008; Hey, Tansley, & Tolle, 2009).
    • Alternatives? Disciplinary data repositories? Issues include management, funding, resourcing etc. Future research.
  • 19. References
    • Allen, L. (2005). Hybrid Librarians in the 21st Century Library: A Collaborative Service-Staffing Model. Paper presented at the 12th National Conference, Association of College & Research Libraries, April 7-10, 2005 Minneapolis, Minnesota .
    • Borgman, C. L. (2007). Scholarship in the digital age: Information, infrastructure, and the Internet. Cambridge, Mass.: The MIT Press.
    • Callon, M., Lascoumes, P., & Barthe, Y. (2009). Acting in an uncertain world: An essay on technical democracy. Cambridge, Mass.: MIT Press.
    • Corrall, S. (2010). Educating the academic librarian as a blended professional: a review and case study. Library Management, 31(8/9), 567 - 593.
    • Fink, J. L., Kushch, S., Williams, P. R., & Bourne, P. E. (2008). BioLit: integrating biological literature with databases. Nucleic acids research, 36(suppl 2), W385-W389.
    • Hey, A. J. G., & Trefethen, A. (2003). The data deluge: an e-science perspective. In F. Berman, G. Fox & A. J. G. Hey (Eds.), Grid computing-making the global infrastructure a reality (pp. 809-824). New York: Wiley.
    • Hey, A. J. G., Tansley, S., & Tolle, K. M. (2009). The fourth paradigm: data-intensive scientific discovery: Microsoft Research.
    • Read, E. J. (2007). Data services in academic libraries: assessing needs and promoting services. Reference and user services quarterly, 46(3), 61-75.
  • 20. Acknowledgements
    • The authors acknowledge, with thanks, the support of the APSA, especially the assistance of Cathy Powers, President and Russell Best, Research Officer. We are grateful to all the interviewees who gave us their time and their views.
    • The Small Grant funding received by Mary Anne Kennan and Kirsty Williamson from the Faculty of Education, Charles Sturt University, is also acknowledged with thanks.
  • 21. Thank you! Questions?