The Data Services team provides research support through classes and consultations in REDCap, data management, data visualization, and other topics. Class descriptions are below. The Data Services team also manages the NYU Data Catalog. If you would like to share descriptions of your data on the catalog, please contact firstname.lastname@example.org.
Our librarians are available for one-on-one consultations to provide assistance with REDCap, data visualization, research data management, or data sharing.
Classes and Workshops
The data services team also offers the following workshops periodically throughout the year. To find out about scheduled classes, subscribe to the Data Services listserv here.
REDCap is a secure, HIPAA compliant web-based application that supports efficient data capture for research studies. Participants in this introductory workshop will learn to build web-based data collection forms, to use basic REDCap functionality and export data. Use of REDCap requires an NYULMC KID and password.
This workshop will offer hands-on experience working with more advanced features of REDCap. We will discuss creating and using longitudinal forms and creating surveys to email to study participants. Use of REDCap requires an NYULMC KID and password.
This workshop will offer hands-on experience creating forms more efficiently, including using the data dictionary and the tool's form library to build questionnaires, and will include information on how to import data from other systems and monitor data collection errors more effectively. Use of REDCap requires an NYULMC KID and password.
This workshop will provide attendees with an overview of best practices in clinical research data management, including outlining and developing the key elements of a data management plan, developing a data collection plan, and improving efficiencies and workflows for research projects. The workshop is case study based and tied to resources available at NYU Langone Health (e.g., REDCap).
This workshop covers best practices in research data management that will facilitate better documentation and organization of research data, current initiatives and requirements around data management, sharing and reproducibility, and how to effectively share data.
The clinic is a forum for researchers, students, and others to receive constructive feedback on their charts, figures, and other visualizations.
Microsoft Excel offers powerful data visualization features, many of which require delving beneath the surface to fully use. This hands-on workshop will explore data viz function to help in biomedical research and publishing.
This hands-on workshop will teach attendees how to make persuasive, publication-ready scientific charts and graphs using GraphPad Prism, an MCIT-supported software program that requires no coding. This session will cover design best practices, building charts and multi-panel figures, and formatting for publication.
This workshop discusses best practices to improve comprehension of charts and figures, and how to tailor them to different audiences. The discussion emphasizes framing your visualizations around the needs of your reader and considerations for crafting data visualization based on differing contexts.
This workshop introduces attendees to the basics of R using the R Studio interface. The class assumes no previous programming background, and covers key terminology and concepts, troubleshooting, data processing, plotting, and writing scripts.
This workshop will address how to create publication-ready charts and graphs in R using the ggplot2 package. A basic understanding of R is assumed.
This hands-on workshop provides a basic introduction to Git, a version control system for tracking changes in computer files, and GitHub, an online collaboration tool that uses Git.
What kinds of research questions can data science answer? How can a researcher effectively communicate with a data scientist? This course is designed to provide biomedical researchers with a basic introduction to the concepts, techniques, and language of data science, and is intended for researchers interested in learning more about data science, but not becoming data scientists themselves.