Cancer Data Science Pulse

Dr. Vivian Ota Wang shares her perspectives on data bias and outlines ideas for making data more equitable, fair, and useful to the greatest number of people, all of which would benefit cancer research.

Hear from CBIIT’s Chief of the Clinical and Translational Research Informatics Branch as he gives advice on how to conduct a research project using data science and the value data science brings to supporting the cancer research community.

How can data science support your cancer research? Explore this helpful quick start guide to find out! We’ll show you an overview of how data science enhances cancer research and how you can get started applying it to your work.

Did you ever wonder what goes into making data ready for analysis by researchers around the world? In this video blog, meet "Datum," a single speck of genomic data chronicling how NCI supports cancer research by bringing data to life.

Read the blogs that topped our charts in 2022 and see if your favorite made #1!

Ever wonder what it’s like to work on a data ecosystem? Meet software engineer Ming Ying, and website specialists Hannah Stogsdill and Ambar Rana, as they describe what it’s like to design, develop, implement, and maintain NCI’s Integrated Canine Data Commons.

Watch our time capsule video to learn about the current status of the field and new technologies that are sure to be important as we embark on the next era of cancer data research.

Discover how NIH is working to make generalist repositories (GRs) part of the data sharing ecosystem. The goal is to minimize data sharing barriers while still taking advantage of GR convenience and usability.

This blog offers a primer on semantics, a topic that has broad implications for the biomedical informatics and data science fields. Here, Gilberto Fragoso, Ph.D., describes the structures that serve as a foundation for data science semantics. Those systems help improve data interoperability, allowing researchers to query, retrieve, and combine very different data sets for more extensive analysis.

In this latest Data Science Seminar, Jim Lacey, Ph.D., M.P.H., shares the lessons he learned in transitioning a large cancer epidemiology cohort study to the cloud, including the importance of focusing on people and processes as well as technology. Project managers, principal investigators, co-investigators, data managers, data analysts—really anyone who is part of a team that wants to use the cloud or cloud-based resources for their studies—should attend.