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.

Metadata Automation DREAM Challenge Timeline is as follows: Pre-registration Open - August 1, 2019 Start Open Phase - January 14, 2020 Metadata Automation DREAM Challenge Webinar - January 30, 2020 Challenge Open/Start Round 1 - February 10, 2020 Start Round 2 - March 10, 2020 Round 2 Submission Deadline - April 9, 2020 Round 3 Submission Deadline - May 15, 2020 Validation Phase Open - May 18, 2020 Validation Phase Submission Deadline - May 29, 2020 Release Final Results - mid June 2020

The Quest for Harmonized Data

The quest to harmonize data has ushered in a new way of thinking about standardization. Now, rather than expecting everyone to adopt a particular model or standard, we’re seeking to leverage technology that can do some of this work for us. The DREAM Challenge was designed to make aggregating and mapping data to the correct lexicon of terms and metadata a nearly seamless step for researchers. Read more about the Challenge that’s currently underway and how we hope to address harmonization in the future.

Headshot of Mervi Heiskanen

Spotlight on CBIIT Staff: Mervi Heiskanen

This new blog installment shines a spotlight on the staff who are working to turn data and IT resources into solutions for addressing data-driven cancer research. Here we feature Mervi Heiskanen, Ph.D., program manager in the Cancer Informatics Branch at CBIIT. Much of her work focuses on data sharing and creating the tools and resources that help to make open data a reality.

Illustration shows how different puzzle pieces come together to create a picture of a bridge. This offers an analogy for how different datasets come together through harmonization. Distributed data are aggravated and then integrated. This process allows individual data with different conceptual frameworks to be joined together (through large-scale indexing frameworks and consistent identifiers) to identify gaps and to allow for better analysis, both by humans and machines.

Cancer Data Harmonization—Setting Standards and Breaking Down Silos

Pooling data from numerous sources strengthens the power of the information, but only if it can be meaningfully connected. Dr. Melissa Haendel, Director of the Translational and Integrative Sciences Laboratory, Oregon State University (OSU), and Principal Investigator for the NCI Center for Cancer Data Harmonization, and Julie McMurry, Associate Director of the Translational and Integrative Sciences Laboratory, OSU, describe the basics of harmonization and how it can help in wrangling massive amounts of data to make them more valuable to research.

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