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Roles of Shared Data Collections in Neuroscience

Explore roles played by shared data collections in neuroscience research, exemplified by the NACR case study. Learn about resource collections, data curation, and implications for scientific production and communication.

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Roles of Shared Data Collections in Neuroscience

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  1. The Roles of Shared Data Collections in Neuroscience Melissa Cragin Data Curation Conference November 21, 2006

  2. Introduction • Resource Collections • Features of the Neurosciences field • A Case Study – the NACR • Findings: Roles of the Collection • Next steps in the research

  3. Features of Resource Collections

  4. The Neurosciences Field • Rapid growth over the last 30 years • Multi-disciplinary and highly specialized • fragmented knowledge base • Problem-oriented research is increasing • Need to integrate data across scales • Many data types, file formats and standards • Sharing experimental data is not a traditional practice • Often highly competitive

  5. Methods • Case Study developed over last two years • Interview Participants (n=25) Users (20) Depositors (9) End-users and Prospective users (11) Database developers (3) Consultants (2) • Document analysis

  6. The NeuroAnatomical Cell Repository • Public data collection and services • Multiple data types (2D + 3D images, animations, numerical data, annotations, etc.) • Distributed architecture • Oracle – SRB configuration • Dependencies on external organizations for continuity of service • Web-based • Interface for deposition • Data views publicly accessible • Data download on request • Moving from Beta to Production level system

  7. NACR as a Resource Collection

  8. Curation tasks for the NACR Verify & Annotate Mobilize Documentation “Packaging”

  9. Data management Analysis Re-analysis Novel “cross-analysis” Algorithm development Modeling Collaboration Communication forum Informal Communication Peer Review Images for talks Use in the classroom Activities Associated with Use of the Collection

  10. The NACR in Scientific Production • Project coherence • a single “location” for project data “The great thing that I found, it takes a project and groups it all together, it forces you to put everything in one location, and that’s great. … This reduces search time to locate it, etc. will make things quicker to get at; this is simplistic but not trivial, it will save me time.” [C1MDP1]

  11. Production, cont’d. • Opportunities for novel analysis • Spatial registration of data • Facilitates re-analysis of one’s own data • “cross-analysis” “Because if you have similar pieces of information across projects you might be able to map trends that are rather subtle.”[P1MDP1] • data mining • in ways comparable to Literature-based Discovery tools, such as testing hypotheses

  12. Production, cont’d • Secondary users of this resource • Algorithm development • Modeling • Supporting collaboration

  13. The NACR and Scholarly Communication • Informal Communication • Sharing data with unintended users (e.g. clinical colleagues) • Formal Communication • Publishing • Educational applications • Undergraduate summer program in neurosciences • Use of images for teaching

  14. Summary • Roles of the NACR in: • Scientific Production • Data Management • Modeling • Scholarly Communication • Peer Review • Curation activities along the deposit process • “Drafting” scientists into the use of shared collections • “Points of entry”

  15. Next Steps… • Comparative analysis of several resource collections • Begin to assess the variety of resource level data collections • Are there predominant models of resource level data collections? • Are there indicators of collection longevity? • Map data collection uses onto current models of scholarly communication • Extend our knowledge about data practices • What are scientists’ roles in data curation across the data lifecycle?

  16. References • Ascoli, G.A. (2006). Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nature Reviews Neuroscience, 7(4), 318-324. • Crawford, S.Y., Hurd, J.M., & Weller, A.C. (1994). From Print to Electronic: The Transformation of Scientific Communication. Medford, NJ: Information Today. • National Science Board (2005). Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century. Available: http://www.nsf.gov/pubs/2005/nsb0540/nsb0540.pdf [Accessed February 7, 2006]

  17. Thank you Melissa Cragin Graduate School of Library and Information Science University of Illinois at Urbana-Champaign cragin@uiuc.edu This project has been supported in part by NSF grant # 0222848.

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