Program Session

9 - Rigor and Reproducibility Opens the Door for a Required Library Course for Doctoral (PhD) Students

Tuesday, May 30
3:45 PM - 3:50 PM
Room: 611

Objectives: Librarians at an academic medical center sought to address information and data management skills gaps observed in basic science graduate students. Leadership of the graduate program recognized the need for this instruction and were strongly supportive of a library course to meet these needs, but low registration by graduate students was a barrier to providing this instruction.


Methods: The need to improve basic science graduate student competencies in information and data management was identified in discussions between the Vice Dean for Science and the Library Director. An eight class course was developed that included instruction in literature searching, citation management, data management, and data visualization. The initial offering of the course was cancelled due to low registration. At the request of the dean of the graduate program, the library added course content on rigor and reproducibility, specifically with respect to NIH requirements in this area. NIH training grants require that students receive instruction in this area, thus allowing the dean to leverage this requirement to establish the library course as a requirement of all first year PhD and MD/PhD students.


Results: The eight class course was offered to all 42 first year PhD and MD/PhD students in the graduate institute of an academic medical center, with registration split between fall and spring offerings of the course. One element of the evaluation data collected was whether students would use what they learned. Despite low registration before the course was required, the median percentage across course topics of students in the fall semester reporting that the content of each of the classes was valuable was 83%.


Conclusion: There is great value in requiring instruction in information and data management for basic science graduate students, as these are skills that they will use in their work, but these students often either do not value or do not prioritize this type of instruction, and so do not take advantage of optional instructional offerings in this area. Requirements around instruction for students on training grants in NIH rigor and reproducibility requirements can provide critical leverage for making these types of classes required.


Keywords: data management
data visualization
rigor and reproducibility
library instruction
basic science



Program Session

Alisa Surkis

Head, Data Services and Translational Science Librarian
NYU School of Medicine
New York, New York

Alisa Surkis is the Head of Data Services and the Translational Science librarian at the NYU Health Sciences Library. The Data Services unit provides support for research data management and data visualization, maintains an institutional data catalog, and serves as a locus for education on collecting, managing, analyzing, visualizing, and sharing data. In her role as Translational Science Librarian, Dr. Surkis is the Director of Team Science for the NYU-HHC CTSI and works to facilitate research collaboration. She received her MLS from Queens College, and has an MS in physics from Stanford University, and a PhD in neuroscience from NYU.

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Fred LaPolla

Knowledge Management Librarian
NYU Health Sciences Library
New York, New York

Fred LaPolla is a junior faculty member with NYU's Health Sciences Library Data Services team. He is interested in data visualization, bibliometrics, and research data management. Fred earned his MLS at Queens College, CUNY.

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Nicole Contaxis

Data Catalog Coordinator
NYU Health Sciences Library
New York, New York

Nicole Contaxis, MLIS, is the Data Catalog Coordinator at the NYU School of Medicine. She plans and conducts outreach efforts for the catalog, curates the datasets in the catalog, and assists researchers as they submit descriptions of their data. She has broad interest in data sharing, data curation, and outreach strategies.

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Kevin B. Read

Knowledge Management Librarian
NYU Health Sciences Library
New York, NY

Kevin Read is Knowledge Management Librarian at the NYU Health Sciences Library. He is a member of the Data Services Team, and provides training and research support for faculty, residents and staff on topics including: research data management, REDCap, systematic reviews, grant support and citation management. His areas of expertise include data discovery, metadata, research data management, data sharing, literature searching, and education. Kevin is also the lead of the NYU Data Catalog project that makes datasets created by, and of interest to NYU researchers more discoverable. Kevin participates in several NIH-wide initiatives including the NIH Big Data to Knowledge Indexing Working Group, and NIH BioCADDIE.

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