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Data Services Cross Training for Librarians

Data Services Cross Training for Librarians. A Report on UC Berkeley's Data Boot Camp 2008. Harrison Dekker IASSIST 2009 - Tampere, Finland 27 May, 2009. Cross Training. from Wikipedia - http://en.wikipedia.org/wiki/Cross-training_(business).

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Data Services Cross Training for Librarians

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  1. Data Services Cross Training for Librarians A Report on UC Berkeley's Data Boot Camp 2008 Harrison Dekker IASSIST 2009 - Tampere, Finland 27 May, 2009

  2. Cross Training from Wikipedia - http://en.wikipedia.org/wiki/Cross-training_(business) • Helps patrons/customers/clients in the long run, as employees are empowered to answer questions about the entire organization. • Requires staff to re-evaluate the reasons and methods for accomplishing their work; inefficient methods, outdated techniques and bureaucratic drift are challenged, if not eliminated. • Raises an awareness of what other departments do. • Routine scheduling is enhanced with the ability to move staff about the "Operation". • Better coverage, increased flexibility and ability to cope with unexpected absences, emergencies, illness, etc. • Can increase the "employability" of staff who have the opportunity to train in areas they were not originally hired for.

  3. Our Goals • Prepare staff to deliver data services in their own libraries • Promote the Data Lab • Improve awareness of the breadth/scope of data services • Acquaint library staff with key non-library data service providers • Promote collegiality and fix past "gatekeeper" practices • Identify potential "adjunct" staff for new Data Lab

  4. Participants Trainers: • Harrison Dekker - Head of Library Data Lab • Jon Stiles - UC DATA • Susan Grand - UC DATA Trainees: • 9  library participants - Law (2), Institute of Government Studies, Agricultural Economics, Environmental Design Library, Earth Sciences (2), Education and Psychology, International and Area Studies, Data Lab • 2 other staff attended week-long ICPSR Social Sciences Data Services Workshop

  5. Campus Locations

  6. Curriculum

  7. Course Overview • 16 hrs of instruction - 12 hrs classroom + 4 self-study • hands-on training in data reference  and technical  aspects of delivering data services.  • In depth attention given to special U.S. Census data products, IPUMS, National Historic GIS, Current Population  Survey, and ICPSR.   • Cover the standard types of social science data, e.g. longitudinal, survey, aggregated vs. microdata. • Brief introduction to various web and desktop software applications used to retrieve and manipulate data sets.

  8. Syllabus - Week 1 • Day    1 • Overview (Jon) • History and evolution of data services, types of services • Key issues - metadata, preservation, secondary analysis • Types of Data  (Harrison) • discussion and illustrative examples of: microdata vs. aggregated data, administrative vs. survey (including types of surveys), economic/financial data, geospatial data • Must-know resources: CPS, PUMS, GSS, NHGIS, IPUMS • Day 2 • Statistical literacy • interpreting tables, crosstabs, central tendencies, dispersion, weighting (Jon) • Data formats and data analysis technology (Harrison) • file format issues, data manipulation vs. data analysis, web-based vs desktop application, open-source vs  proprietary software • Data reference interview.

  9. Syllabus - Week 2 •  Day 3 • Data Archives (Jon) • how to search, navigate, and retrieve data from ICSPR/Roper/..., understanding and using a codebook • strategies for finding archived data on the web • Demos of web-based data analysis and search tools • SDA, DataFerrett, DataVerse (distribute SPSS/Stata/StatTransfer exercises) (Harrison) • searching for data on the web • Day 4 • UC Berkeley overview • Role and structure of IS&T Data Services, Campus GIS services, SSCL and other department labs, Statistical consulting service, Learning resources • UC produced/hosted data: Statewide Database, Field Polls, Human Mortality Database, CDRiP • Collaboration discussion, future directions, Library Data Lab role • Other issues.

  10. Outcomes • Four day format prevented some staff from participating • Followup assessments were good - (contact me for a copy of the survey results) • More coverage of economic data sources would be helpful • Statistical literacy aspect of the class was the most challenging

  11. "Data Boot camp really opened my eyes about the nitty gritty of social science data.  Though I didn't come out of it feeling proficient working with data, it gave me a basic understanding of the process. This has allowed me to discuss my users' data needs with them which has both improved my ability to find the data they need and to refer them effectively for assistance with data resources on campus."

  12. Future Directions • Ongoing one-off in-house workshops on data sources and technology • Broader audience: Workshops or for-credit courses(Berkeley, SJSU...) • Address other emerging data and technology training needs like GIS, data curation, visualization  Contact: Harrison Dekker hdekker@library.berkeley.edu

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