📔 Book Club Catch Up

I'm a few chapters behind

Welcome to the one hundred and seventh issue of Monday Morning Data Science from the Fred Hutch Data Science Laboratory. We are excited to show you what we have been working on (Fresh from the Lab), plus links that we think you would be interested in (Our Weekly Bookmarks Bar). Part of the purpose of this newsletter is to start conversations, so if you have a question or there is something you would like to share with us please let us know by responding directly to this email.

Fresh from the Lab

• [Article: Cancer Researchers Find a Way Around AI’s Biggest Bottleneck] This article by Adam Mills in Newsweek describes CAIA’s federated learning platform which enables institutions train AI models on millions of patient records without sharing raw data, overcoming major privacy and data-sharing barriers.

• [Talk: Empowering Learners with WebR, Pyodide, and Quarto] Fred Hutch Data Science Lab director of training Ted Laderas discusses the challenges of workforce training, highlighting that learners often feel overwhelmed, as evidenced by one quitting due to frustration with RStudio. He emphasizes the importance of improving the learning experience through tools like WebR and Pyodide, which can lower cognitive load and encourage active learning.

Our Weekly Bookmarks Bar

• [Research Paper: An Open-Source Simulation Environment for Studying Agentic Markets] Researchers at Microsoft including friends of the lab Jake Hofman and Dan Goldstein introduce Magentic Marketplace, an open-source simulation platform designed to study how autonomous AI agents interact in digital markets and their effects on consumer welfare, fairness, and market efficiency. Experiments revealed that while advanced models can enhance decision-making, agents remain prone to issues such as manipulation, bias, and reduced performance when faced with too many choices, which highlights the need for oversight and responsible market design.

• [Blog Post: The Hidden Magic of Tidy-Select] This post by Joshua Marie explains that tidyselect provides a universal “selection language” in R that allows functions like where(), starts_with(), and contains() to work seamlessly across tidyverse packages such as dplyr and tidyr, even without explicitly loading them. It also highlights how this API integrates with “data-masking” functions like across(), if_all(), and pick(), making column selection in R more flexible, consistent, and powerful.

As always you can contact us by replying directly to this email, or if you work within the Fred Hutch/University of Washington/Seattle Children’s Cancer Consortium you are welcome to join us on the Fred Hutch Data Slack Workspace. For more information about the Fred Hutch Data Science Lab, visit our website: https://hutchdatascience.org/. See you in two weeks!

- The Fred Hutch Data Science Laboratory