The study investigated how librarians can use tools like Semantic MEDLINE (SemMed) to connect with scientists. A survey of 17 scientists who participated in a SemMed tutorial found that 7 (59%) requested a SemMed login and 10 (41%) planned to request one, showing interest in using the tool. The project provided the associate fellow opportunities to teach scientists about SemMed and potentially foster future research collaborations. It demonstrated how tools like SemMed can help librarians connect with scientists.

1. Susan L. Roy, MS, MLS 1, 2 and Thomas C. Rindflesch, PhD1, 3
National Library of Medicine1, National Institutes of Health Library2, Lister Hill National Center for Biomedical Communications 3
Objective Tutorial Methods Survey Methods Survey Results
The purpose of this study was to investigate  A survey was sent out to the scientists 1-2.5 weeks  17 respondents (77.27% response rate)
ways librarians can use tools such as Semantic after SemMed tutorial participation.  7 (59%) requested SemMed login, 10 (41%) plan to request login, none said
MEDLINE (SemMed) to create connections they did not plan to request login information.
 The survey contained 9 open response and/or
with scientists. Basic scientists are heavy users
of library resources, and yet an underserved Likert scale rated questions.
population for many libraries. Outreach to  Survey responses were collected for 1 month.
scientists by utilizing tools and technology
such as SemMed, a program that extracts
semantic relations from citations in PubMed,
can have tremendous potential fostering Tutorial Results
collaborations by promoting scientific
innovation.
Introduction
Schematic diagram of SemMed processing
Conclusions
This project provided opportunities for the Associate Fellow to learn how to
teach basic scientists about SemMed and an opportunity to foster potential
future research collaborations. With the methodologies and technologies
described here, we show how tools such as SemMed can be used by librarians
for making connections to scientists, and we lay the groundwork for the
Example of an extracted semantic predication Associate Fellow to continue collaborative efforts with basic scientists and the
NLM Semantic MEDLINE group.
Acknowledgements
This research was performed under an appointment with the National Library of Medicine Associate
Fellowship Program, administered by the Oak Ridge Institute for Science Education (ORISE) and
managed by Oak Ridge Associate Universities (ORAU).
SemMed visualization of semantic predications Susan Roy would also like to thank Dr. Kathel Dunn and the 2011-2012 Associate Fellows, Bethany
extracted from MEDLINE citations Harris, Michele Mason-Coles and Jessi Van Der Volgen for their support during the Fellowship year.