Highlighted NIH Resources for Data Science Training
Data Science training opportunities occur all across the Institutes and Centers at the NIH. The Office of the Associate Director for Data Science is committed to highlighting and supporting ongoing activities in this area. Some examples of existing programs include:
The Data Science Training Portal (NIH only), a component of the NIH Data Science Community Area (NIH only) provides information about data science courses that are open to NIH staff. Within the portal you can discover and register for upcoming short courses. You can also provide input on which topics you wish to learn about and request topics for future courses.
The NIH Library in Building 10 provides training programs for NIH staff in more than literature search! Past programs have included classes on clinical and experimental data access, data visualization tools, and programming languages like R. In conjunction with the ADDS office and volunteer instructors from across the NIH, the NIH Library has recently started offering workshops by Software Carpentry.
The NIH Library’s Data Services program provides support data management, organization, visualization, preservation, and reuse at all stages of the research process, from project planning to post-project archiving. Services include online training materials, courses, and one-on-one consultations. Information about upcoming classes is available on the Library’s Current Training page. The NIH Library also has a Bioinformatics support program that includes online tutorials, courses, and one-on-one consultations. It even has a twitter account: @bioinformatics1.
Finally, the NIH Library is at the forefront of tech with the new Technology Sandbox. This part of the Library has 3D printers, a Raspberry Pi mini-computer and other tech tools and software that NIH staff can explore and test for their research needs. These computational technology-based devices allow scientists to prototype new equipment and devices for biomedicine. In addition, the Technology Sandbox has three high-powered bioinformatics workstations, a data sciences workstation, and two collaborative pods with specialized software for Geographic Information Systems (GIS), 3D modeling, and image and video editing. All of these workstations are free to use and can be reserved by visiting the website.
Although statisticians generate a lot of software, many have not had formal training in software development, instead learning "on the job". There is no formal process for code checking, even though the results of the code get published.
Statisticians are in the unique position to review each other’s' code in order to learn from one another. To foster collaboration and improve the quality of statistical software, a working group for reviewing statistical analysis software/code has been established at the National Cancer Institute. To join the cooperative group, contact Michael Sachs (NCI). Researchers from all NIH Institutes and Centers are welcome.
Massive Open Online Course (MOOC) providers are currently offering a variety of Data Science courses, including some funded through NIH grants. While MOOCs offer universal access and are particularly useful for highly motivated students who already know how to learn, traditional face-to-face social interaction is missing from the learning experience. For those who would benefit from in-person discussion of coursework for Data Science MOOCs or want to talk about Data Science in general, office hours at the NIH library are being planned. Particularly topics of focus and exact times will be announced here soon.
For more information, contact Michelle Dunn (NIH/OD).
FAES is a non-profit foundation housed at the NIH that supports the education, well-being, and culture of the NIH community. They offer courses in computing (Python, Perl) and statistics, as well as in applied and theoretical bioinformatics. Browse courses at https://faes.org/content/explore-courses.
Do you know a NIH Data Science training resource that you would like to be highlighted? Let us know about it by sending an email to email@example.com
This page last reviewed on September 5, 2018