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)
• 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accomplishing their work; inefficient methods, outdated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.
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
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
Campus Locations
Curriculum
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.
Syllabus - Week 1
Day 1
1. Overview (Jon)
o History and evolution of data services, types of services
o Key issues - metadata, preservation, secondary analysis
2. Types of Data (Harrison)
o discussion and illustrative examples of: microdata vs. aggregated
data, administrative vs. survey (including types of surveys),
economic/financial data, geospatial data
o Must-know resources: CPS, PUMS, GSS, NHGIS, IPUMS
Day 2
1. Statistical literacy
o interpreting tables, crosstabs, central tendencies, dispersion,
weighting (Jon)
2. Data formats and data analysis technology (Harrison)
o file format issues, data manipulation vs. data analysis, web-based vs
desktop application, open-source vs proprietary software
3. Data reference interview.
Syllabus - Week 2
Day 3
1. Data Archives (Jon)
o how to search, navigate, and retrieve data from ICSPR/Roper/...,
understanding and using a codebook
o strategies for finding archived data on the web
2. Demos of web-based data analysis and search tools
o SDA, DataFerrett, DataVerse (distribute SPSS/Stata/StatTransfer
exercises) (Harrison)
o searching for data on the web
Day 4
1. UC Berkeley overview
o Role and structure of IS&T Data Services, Campus GIS services, SSCL and
other department labs, Statistical consulting service, Learning resources
o UC produced/hosted data: Statewide Database, Field Polls, Human
Mortality Database, CDRiP
2. Collaboration discussion, future directions, Library Data Lab role
3. Other issues.
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
"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."
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