| Summary: | Our library's traditional reference service is effective in helping users discover and gain access to data,
while our new data services focus on management, analysis, and presentation of statistical data. This
paper describes the process of how we gained a better understanding of the variety of users' data needs,
and how we gradually established some new data services based on our current capabilities. How were
we to understand and evaluate our users' needs of data services? What are our users' unspoken data
needs? How would we extend traditional data reference services? How should we plan programs to meet
these needs? What was within our means and what was beyond our capacity? This paper uses a case
study of the new data services at the John Cotton Dana Library, at Rutgers, The State University of New
Jersey, Newark campus, to demonstrate the possible ways to extend data reference services and provide
data computing services. Typical examples are described and analyzed, and then general concepts,
classification, and conclusions are given to inspire the design and implementation at other libraries. Library
users can be classified into many different categories, and each of these may have different needs.
Research centers might have big projects involving data gathering and applications where we can mainly
provide consultation, while an individual faculty member or student might need the librarians as research
partners, with help for their specific problems. Traditional data reference services are still the fundamental
part of a reliable data service, although many reference librarians are doing the job without using the title of
data services. Data services, even computing data services, can be provided at different levels based on
the users' needs and library's resources. Computing data services can involve group training and statistical
analysis assistance, but it can be extended to data project management and data management as more
and more research is required to have formal and accountable procedures when dealing with data.
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