Top tips for a data reference interview

The Data Librarian's Handbook

The reference interview - or consultation - is a well established technique used by librarians and information professionals. This helps establish the issues that a researcher wishes to cover. More fundamentally it also defines the kind of relationship a researcher will have with those offering advice. Now that the use and management of research data is so important to academics it has become an important potential element of the reference interview. This extract from The Data Librarian's Handbook shows how it can be used to bridge traditional librarianship and the expertise required by the data librarian.

Established researchers often know what datasets are available in their field of study, or at least the main sources and providers from which to seek them. They are immersed in the literature and knowledgeable about the activities of their peers, they know the institutes and principal investigators that are producing research relevant to their work. This is usually not the case with postgraduate students, even less so with undergraduate students, and nor is it the case with researchers exploring the boundaries of their disciplines or doing cross-disciplinary research. 

Fortunately for data librarians, it is possible to become knowledgeable about sourcing datasets in a given field without being an expert in that field. How do data librarians match users to the data required?  – through a reference consultation or interview.

Since every research question is unique (unlike every classroom assignment), it is easy to feel daunted when confronted by a new one. These are some helpful hints to guide you through a difficult reference interview:

  1. Buy yourself time by asking more questions before trying to come up with a source; avoid making assumptions about the user’s requirements, prior knowledge or viewpoint.
  2. Find out if the user is basing their query on a published article; ask for the citation or a copy to help you with the context. If a student, ask who is their teacher or supervisor.
  3. Ask the user to explain acronyms and jargon they use in their language; you do not have to pretend to be an expert in their area of study to help them.
  4. Take notes and write down key phrases as the user speaks (if meeting in person or on the telephone).
  5. Even if you are unable to find the perfect source for your user, you can probably give them some useful starting points for their search, based on your knowledge of data sources, or that of your peers.
  6. Do not be afraid to take time to think, search and consult others; always take the user’s e-mail address for future contact or to follow up.
  7. If you remain stumped, resort to asking others: immediate colleagues, peers at other institutions, government statistical agencies, data providers and publishers. Once you have done your homework and ruled out obvious contenders you can also post to a library mailing list or the member list of IASSIST.
  8. If the query is about using a dataset rather than finding one, take time to read the documentation, try out the interface yourself or reproduce the problem before turning to others for help.
  9. If the user does not voluntarily let you know their query has been satisfied, follow up in a reasonable amount of time to see if you can offer further assistance.

Interest in data has been growing in recent years. Support for this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes to create new knowledge, is needed more than ever.  Many librarians and information professionals are finding their working life is pulling them toward data support or research data management but lack the skills required. The Data Librarian’s Handbook, written by two data librarians with over 30 years’ combined experience, unpicks the everyday role of the data librarian and offers practical guidance on how to collect, curate and crunch data for economic, social and scientific purposes.

 

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