The Data Conservancy. CNI Spring Forum April 7, 2009
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1 The Data Conservancy CNI Spring Forum April 7, 2009
2 Data Curation The Data Conservancy embraces a shared vision: data curation is not an end, but rather a means to collect, organize, validate, and preserve data to address grand research challenges that face society.
3 Goal The overarching goal of DC is to support new forms of inquiry and learning to meet these challenges through the creation, implementation, and sustained management of an integrated and comprehensive data curation strategy.
4 not a rigid road map but principles of navigation. There is no one way to design cyberinfrastructure, but there are tools we can teach the designers to help them appreciate the true size of the solution space which is often much larger than they may think, if they are tied into technical fixes for all problems.
5 Principles Our strategy focuses on connection of systems into infrastructure through a program informed by user-centered design and research, sustained through a portfolio of funding streams, and managed through a shared, coordinated governance structure. Build on existing exemplar scientific projects, communities and virtual organizations that have deep engagement with citizen scientists and extensive experience with large-scale, distributed system development
6 Partner institutions! Johns Hopkins University (Lead institution)! Cornell University! Encyclopedia of Life (Marine Biological Laboratory)! Fedora Commons! National Center for Atmospheric Research! National Snow and Ice Data Center! Portico! Tessella, Inc.! University of California Los Angeles! University of Illinois at Urbana-Champaign
7 Unfunded partner institutions! Australian National Data Service! Australian National University! British Library! Digital Curation Centre! Microsoft Research! Monash University! Nature Publishing Group! Optical Society of America! Sakai Foundation! Space Telescope Science Institute! SPARC! University of Queensland, Australia! Zoom Intelligence
8 Objectives! Infrastructure research and development!technical requirements! Information science and computer science research!scientific or user requirements! Broader impacts!educational requirements! Sustainability!Business requirements
9 Technical Architecture! Well-defined interfaces"! Emphasis on modularity and interchangeability"! Reference implementation using Fedora, but other implementations desirable"! Storage layer features"!access to data stored / preserved elsewhere"! Interface to third-party storage services "
10 Data Framework! Start with a common conceptualization that applies across scientific domains! Exploit semantic technologies! Leverage existing work! Prototype the framework in target communities!iteratively refine, learn from experience!demonstrate success, measured in terms of new science
11 Common Conceptualization Observations are the foundation of all scientific studies, and are the closest approximation to facts. Wiens, J. A. (1992). Cambridge studies in ecology: The ecology of bird communities. Foundations and Patterns, 1; Processes and Variations, 2
12 Data Model using OAI-ORE
13 Domain coverage/methods! Multi-site user research methods are a blend of:! Case study & domain comparisons! Depth & breadth! Local & global Astronomy Biodiversity Earth Science Social Science UCAR Task-based design and usability testing! Use cases, data requirements, system recommendations UCAR UCLA Ethnography, virtual ethnography, oral histories! Use cases, data requirements Interviews, Surveys, Worksheets, Content analysis! Curation requirements, taxonomy, metadata/ provenance framework UIUC
14 Design Flow Depth User Studies Breadth Comparisons User Requirements Data Framework & Architecture
15 Information science research
16 Sustainability! Diversified portfolio of funding and perspectives! Align with existing institutional priorities! Leverage partners sustainability mechanisms! Focus on economies of scale and economies of scope! Consider business requirements as equal to other requirements from inception of activity! Incorporate findings from Blue Ribbon Task Force on Sustainable Digital Preservation and Access
17 Implications for Libraries! Libraries as part of a distributed network! Data as collections! Data as services! Librarians as data scientists! Data centers are the new library stacks -- Winston Tabb (JHU Dean of Libraries)
18 Acknowledgements DataNet award for The Data Conservancy NLG grant award LG ! Tim DiLauro (technical architecture slide)! Carl Lagoze (data framework slides)! Carole Palmer (information science slides)
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