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Updated: 2 hours 47 min ago

Applications Open for RDM 102: Beyond Research Data Management for Biomedical and Health Sciences Librarians (Spring 2020)

Wed, 2019-12-04 11:14
Course Description

Biomedical and health sciences librarians are invited to participate in a rigorous online training course going beyond the basics of research data management, sponsored by the National Network of Libraries of Medicine Training Office (NTO). This course will expand on concepts covered in RDM 101: Biomedical and Health Research Data Management Training for Librarians, and threaded throughout will be the librarian’s role in research reproducibility and research integrity. It will also include practice in using Jupyter Notebooks through an open-source browser-based application (JupyterHub) that allows users to create and share documents that contain live code, equations, visualizations, and narrative text.

The major aim of this course is to provide an introduction to the support of data science and open science with the goal of developing and implementing or enhancing data science training and services at participants’ institutions. This material is essential for decision-making and implementation of these programs, particularly instructional and reference services. The course topics include an overview of data science and open science, data literacy, data wrangling, data visualization, and data storytelling.

To have a successful experience in this course, we recommend that you are familiar with the concepts covered in RDM 101 and statistical concepts addressed in these videos:

The program spans 9 weeks from February 24 – April 24, including 5 modules of asynchronous content, a catch-up week, and a synchronous online session during the week of April 20. The format includes video lectures, readings, case studies, hands-on exercises, and peer discussions. Under the guidance of a project instructor, participants will complete a Final Project to demonstrate improved skills, knowledge, and ability to support data science services at their institution. Expect to spend about 6 hours each week on coursework and the project.


  • Shirley Zhao, MSLIS, MS, Assistant Librarian (Clinical), Spencer S. Eccles Health Sciences Library, The University of Utah
  • Leah Honor, MLIS, Education & Clinical Services Librarian, Lamar Soutter Library, University of Massachusetts Medical School
  • Tess Grynoch, MLIS, Research Data & Scholarly Communications Librarian, Lamar Soutter Library, University of Massachusetts Medical School
  • Nancy Shin, MLIS, NNLM PNR Research and Data Coordinator, Health Sciences Library, University of Washington
  • Vicky Steeves, MLIS, Librarian for Research Data Management and Reproducibility, Division of Libraries & Center for Data Science, New York University

CE Credits
Participants who complete all modules, the Final Project, and the course evaluation will receive 36 hours of Medical Library Association Continuing Education credit. No partial CE credit is granted.

What does it cost?
There is no charge for participating in the program.

Important Dates
  • Application deadline: January 10, 2020
  • Notifications begin: January 21, 2020
  • Course: February 24 – April 24, 2020

Who can apply?
Applications are open to health science information professionals working in libraries located in the US. Applicants must have previous training or experience in research data management through the RDM 101 course or attest to the objectives listed here. Enrollment is limited to 40 participants.

How to apply
Complete the online application form by January 10, 2020. The application will gather the following information:

  • Name, email address, phone number, state, place of employment, current job title.
  • Did you complete RDM 101: Biomedical and Health Research Data Management for Librarians? (It is not required for RDM 102).
  • Please briefly describe your knowledge or experience in research data management and/or data science.
  • Why do you want to take this course?

Contact NTO.

Categories: Data Science

NIH Requests Public Comment on a Draft Policy for Data Management and Sharing and Supplemental Draft Guidance

Thu, 2019-11-07 10:23

Yesterday, NIH released a Draft NIH Policy for Data Management and Sharing and supplemental draft guidance for public comment. The purpose of this draft policy and supplemental draft guidance is to promote effective and efficient data management and sharing that furthers NIH’s commitment to making the results and accomplishments of the research it funds and conducts available to the public. Complete information about the draft Policy and draft supplemental guidance can be found on the NIH OSP website.

Stakeholder feedback is essential to ensure that any future policy maximizes responsible data sharing, minimizes burden on researchers, and protects the privacy of research participants.  Stakeholders are invited to comment on any aspect of the draft policy, the supplemental draft guidance, or any other considerations relevant to NIH’s data management and sharing policy efforts that NIH should consider.

To facilitate commenting, NIH has established a web portal that can be accessed here. To ensure consideration, comments must be received no later than January 10, 2020.

For additional details about NIH’s thinking on this issue, please see Dr. Carrie Wolinetz’ latest Under the Poliscope blog:

NIH’s DRAFT Data Management and Sharing Policy: We Need to Hear From You!

NIH will also be hosting a webinar on the draft policy in the near future. Please stay tuned for details.

Questions may be sent to

Categories: Data Science

Upcoming Research Data Management Webinar: If You Share It, Will They Come? Quantifying and Characterizing Reuse of Biomedical Research Data

Wed, 2019-09-04 10:04

Date: Wednesday, October 2nd

Time: 2:00PM – 3:00PM ET

Presenter: Lisa Federer, PhD, MLIS is the Data Science and Open Science Librarian at the National Library of Medicine (NLM), focusing on developing efforts to support workforce development and enhance capacity in the biomedical research and library communities for data science and open science. Prior to joining NLM, Lisa spent five years as the Research Data Informationist at the National Institutes of Health Library, where she developed and ran the Library’s Data Services Program. She holds a PhD in information studies from the University of Maryland and an MLIS from the University of California-Los Angeles, as well as graduate certificates in data science and data visualization. Her research focuses on quantifying and characterizing biomedical data reuse and development of meaningful scholarly metrics for shared data.

Description: Since the mid-2000s, new data sharing mandates have led to an increase in the amount of research data available for reuse. Reuse of data benefits the scientific community and the public by potentially speeding scientific discovery and increasing the return on investment of publicly funded research. However, despite the potential benefits of reuse and the increasing availability of data, research on the impact of data reuse is so far sparse. This talk will provide a deeper understanding of the impacts of shared biomedical research data by answering the question “what happens with datasets once they are shared?”

Specifically, this talk will demonstrate that data are often reused in very different contexts than for which they were originally collected, as well as explore how patterns of reuse differ between dataset types. This talk also considers patterns of data reuse over time and the topics of the most highly reused datasets to determine whether it is possible to predict which datasets will go on to be highly reused over time. Finally, career stage and geographic location of data reusers provide an understanding of who benefits from shared research data. These findings have implications for several stakeholders, including researchers who share data and those who reuse it, funders and institutions developing policies to reward and incentivize data sharing, and repositories and data curators who must make choices about which datasets to curate and preserve

Registration: Registration is free and can be accessed through the NNLM class instance.

For additional information, please contact Kiri Burcat.

Categories: Data Science