Cancer Data Science Pulse

Data Standards
Imaging Data Commons website portal featuring a radiology image and a graph chart, Cases by Major Primary Site. Chest has the most, then brain, head and neck, breast.

Imaging Data Commons Brings the Power of the Cloud to Cancer Research

On October 20th, NCI launched the Imaging Data Commons (IDC), the latest data repository to be offered within the Cancer Research Data Commons (CRDC) infrastructure. Through the IDC, both researchers and clinicians will have access to a wide range of cancer-related images, including radiology and pathology imaging data, as well as their accompanying metadata.

Joe helps a young camper bait her fishing pole

Turning Life’s Passion into Purposeful Work—Following A Fellow’s Path to CBIIT

NCI offers a broad range of fellowships, enabling us to tap some of the brightest minds in science and technology to further advance scientific discovery. Here, Joseph Flores-Toro, Ph.D., a fellow in the Office of Data Sharing (ODS), describes the path he took in becoming a fellow for CBIIT.

Illustrated drawing of a large group of diverse people. Headline: BIG DATA Meets Social Determinants of Health-thumbnail-MDT.png

How Can Big Data Help to Address Health Disparities?

Dr. Tony Kerlavage, director of NCI’s Center for Biomedical Informatics and Information Technology (CBIIT), sat down to discuss one key component of racial inequality, the issue of health disparities, as it relates to Big Data. As noted by Dr. Kerlavage, representing our diverse U.S. population in research and in the workforce are key, but we also need better data.

Sherri De Coronado

Spotlight on CBIIT Staff: Sherri de Coronado

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. This spotlight features Sherri de Coronado, program manager in the CBIIT Cancer Informatics Branch.

Illustration shows how different types of data can be aggregated for analysis. This aggregation is further enhanced by harmonization through the Center for Cancer Data Harmonization or CCDH and through the use of Application Programming Interfaces (API), which help to automate the aggregation-and-harmonization process, ultimately making it easier and more efficient to analyze data. Bringing such disparate data together allows for more informed research into how to prevent, diagnose, and treat cancer.

Cancer Data Aggregator—The Engine that Could . . . Drive Data Aggregation in a Whole New Way

NCI initiatives are accumulating a wealth of data from the fields of genomics, proteomics, single-cell, radiology, molecular imaging, clinical findings, and more. The newly awarded Cancer Data Aggregator (CDA) is currently being designed and developed to allow scientists to crosstalk among these very diverse data sets, facilitating interoperability not only within the Cancer Research Data Commons but throughout the larger data ecosystem.

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.

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.

Dr. Ed Helton

Ed Helton, Standards Luminary, Is Missed

The world of clinical research standards, including the BRIDG Model that bridges research and healthcare, would truly not be what it is today without the significant and selfless contributions of Dr. Edward Helton.

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The Network of "BioThings"

Biomedical knowledge is typically centered around the variety of biological entity types, such as genes, genetic variants, drugs, diseases, etc. Collectively, we refer to them as "BioThings." The volume of biomedical data has grown explosively, thanks to the efforts of many different researchers and consortia. This explosive growth includes many different types of data using many different formats and standards, making it difficult to unify the disparate sources of data.

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