NCI Data Jamboree (Project Abstract Submission): Submission #3

Submission information
Submission Number: 3
Submission ID: 183223
Submission UUID: 3cfedcb5-972a-457c-9dd7-b57387311530

Created: Wed, 06/10/2026 - 14:01
Completed: Wed, 06/10/2026 - 14:01
Changed: Wed, 06/10/2026 - 14:01

Remote IP address: 10.208.24.28
Submitted by: Anonymous
Language: English

Is draft: No
First Name Jaclyn
Middle Initial N
Last Name Taroni
Degree(s) Ph.D.
Position/Title/Career Status Director of the Childhood Cancer Data Lab
Organization Alex's Lemonade Stand Foundation
Organization Address Wynnewood, PA
Email j.taroni@alexslemonade.org
List of Additional Authors
Abstract Category Evaluating data quality for reproducibility and AI-readiness
Abstract Keywords
Abstract Title Project Seeker: Jaclyn N. Taroni
Abstract Experience and expertise: I am Director of the Childhood Cancer Data Lab at Alex’s Lemonade Stand Foundation (https://www.ccdatalab.org/), where I lead a multidisciplinary team of data scientists, software engineers, and UX professionals. At the Data Lab, we build tools to make pediatric cancer data accessible, such as the Single-cell Pediatric Cancer Atlas (scpca.alexslemonade.org), and organize open science projects, such as the Open Pediatric Brain Tumor Atlas. I am a computational biologist by training. Historically, my research focus has been on leveraging large collections of transcriptomic data and machine learning in rare disease settings.

Why I want to participate: My work depends on the broader cancer data ecosystem being interoperable, well-documented, and reusable, and I want to engage directly with NCI repositories and a cross-disciplinary group on the practical barriers to finding, accessing, and integrating these resources. I am looking to contribute and learn from investigators in the broader cancer space in a hands-on collaboration setting.

What I hope to achieve: I hope to build collaborations across the cancer data community that extend the reach of open-science tooling and to help produce publicly shareable artifacts that others can build on. I am especially interested in joining a team where reproducibility and data quality are central to the problem.