Cancer Data Science Pulse

The Cancer Data Science Pulse blog provides insights on trends, policies, initiatives, and innovation in the data science and cancer research communities from professionals dedicated to building a national cancer data ecosystem that enables new discoveries and reduces the burden of cancer.

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Using Cancer Data Science to Advance Your Research

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.

Two illustrated characters sit at a table. A laptop and books are on the table and buildings are shown in the background. Speech "bubbles" are shown above the characters and their words are indicated by three dots. Meant to show an interview situation in an office.

Job Interview Questions for Cancer Data Scientists and How to Answer Them

Learn what one NCI hiring manager is looking for when interviewing a candidate for a cancer data scientist job. CBIIT’s own Dr. Daoud Meerzaman provides his input on what certain questions mean during the interview process.

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NCI Brings Cancer Data to Life

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.

Artists conception of federated learning. Shows four separate buildings linked together to form a center "brain." The idea is to show how people can be in different places and still contribute equally to research.

Federated Learning—A Solution for Democratizing Data for Cancer Research?

Federated learning (FL) might well be the next paradigm shift in democratizing vast amounts of data from many data sources for use in cancer research. FL offers a decentralized, but collective, approach to using data to better understand cancer.

Examples of data visualization showing a circular heat map and a PCA biplot.

Visualizing Data Using Circular Heatmaps and Biplots—Pro-Tips From NCI Researchers

We’re continuing our blog series on data visualizations with a look at circular heatmaps and biplots. This blog features images and tips from Drs. Arashdeep Singh and Sridhar Hannehalli of NCI’s Center for Cancer Research.

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How the Mitelman Database Can Help You Explore Genomic Abnormalities

Are you researching genomic abnormalities? Bioinformatician Deena Bleich gives an overview of the online tool, “Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer,” and how it can help you analyze genomic data.

blue circle with orange flame in the middle of a light blue cloud icon to represent the FireCloud icon. Background shows multi-colored stacked rectangles to represent data

FireCloud: A Secure Platform For Data Analysis Powered by Terra

In this blog, we’re spotlighting how researchers can leverage FireCloud, one of NCI’s Cloud Resources, for accessing data, running analysis, and collaborating with others in the cancer research community.

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The 12 Blogs of CBIIT—Your Favorite Data Science Reads from 2022

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

Artist's conception of how NCI fellowships help to grow data scientists. Shows drawing of two men and two women standing in separate flowerpots with a watering can overhead.

Growing the Field—NCI Fellowship Opportunities in Data Science

Interested in a data science fellowship? Whether you’re considering data science as a career, you’re a new graduate, or you’ve been working in academia or industry for many years, NCI has fellowship opportunities to help you grow your career.

Futuristic cloud image with lines connecting to icons that represent key parts of data use, including a laptop, a globe, people, a mobile device, a lock, a key, a magnifying glass. All set against a blue background with smaller images denoting computer code.

Improving Access to NCI’s Individual-Level Genomics and Other Omics Data

Learn more about new streamlined access to broad-use data sets within the database of Genotypes and Phenotypes (dbGaP).

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