Applications Open for RDM 102: Beyond Research Data Management for Biomedical and Health Sciences Librarians (Spring 2020)
RDM 102: Beyond Research Data Management for Biomedical and Health Sciences Librarians (Spring 2020)
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:
- Mean, Median, and Mode: Measures of Central Tendency: Crash Course Statistics #3
- Measures of Spread: Crash Course Statistics #4
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.
Course and Project Instructors
- Shirley Zhao, MSLIS, MS, Data Science Librarian, Spencer S. Eccles Health Sciences Library, 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, Librarian for Research Data Management and Reproducibility, Division of Libraries & Center for Data Science, New York University
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.
- Application deadline: January 10, 2020
- Notifications begin: January 21, 2020
- Course: February 24 – April 24, 2020
Who can apply?
- Applicants must have previous training or experience in research data management through the RDM 101 course or attest to the objectives listed here.
- Applications are open to health science information professionals working in libraries located in the US.
- 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?