⚡️ Big Bolt of Lightning Hanging Low

Welcome to the one hundred and twenty-fourth 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.

• [MMDS Extra: Trial Finder - A Web Application to Support Clinical Trial Recruitment] In the first ever issue of MMDS Extra we bring you the story of Trial Finder, an internal application that the Fred Hutch ODCO designed in partnership with the UW Medicine/Fred Hutch CTMS Program Office, Seattle Children’s, and UW IT. Trial Finder makes it easy for providers to browse open clinical trials, and serves as one of many tools that helps providers match patients to a trial that is right for them. We wrote this post to share the lessons we learned during the UX/UI research process with design and technology practitioners working in healthcare, so we hope it’s valuable and we welcome your feedback.

• [Resource Collection: awesome-ted] Fred Hutch OCDO Director of training Ted Laderas, is know far and wide to be awesome! Finally his awesomeness has assembled in this generous collection of workshops, courses, books, and teaching materials spanning R, Python, SQL, Shiny, command-line skills, HPC, bioinformatics, and more. Whether you are learning foundational tools or looking to teach them, this is an excellent place to discover practical, openly available curriculum, including many resources developed in the Fred Hutch Data Science Lab.

• [Article: How to Do Your Own Online Health Research Like a Scientist] Friend of the lab and professor of statistics Lucy D’Agostino McGowan offers a practical framework for evaluating health questions online: define the population, intervention, outcome, and time frame, then begin with systematic reviews before turning to individual studies. She also explains how to weigh study design, certainty, bias, funding, and anecdotes. This article is a comprehensive guide worth sharing with anyone who likes to do their own research.

• [Blog Post: Building a Permissive ZIP Code Cities Dataset] Andy Marek tackles a deceptively difficult data problem: producing an openly redistributable U.S. ZIP-code-to-city mapping when the authoritative USPS mapping is proprietary. His alternative combines publicly licensed government sources including USPS locale lists, HUD population-weighted centroids, and Census geography and population data. We really appreciate this deep dive into recreating a dataset that we were surprised to learn is not open by default.

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 soon!

- The Fred Hutch Data Science Laboratory