Good morning!
Welcome to the fifth ever 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
[Event: Single Cell User Group (Today!)] Every month some of the Data Science Lab members gather together with single cell practitioners to brainstorm new ideas and discuss practical problems. We invite you to drop in virtually or in person using the Teams link above.
[Event: Data House Calls (Wednesday in Arnold)] The Data Science Lab team will be on the first floor of the Arnold building for our weekly consultation hour. Please drop by and talk to us about support for data challenges, coding challenges, computing questions, data management, and more. You can join on Teams too, again the Teams link is in the event linked above.
Our Weekly Bookmarks Bar
[Open Medical Data for AI] Pranav Rajpurkar’s lab is piloting the release of de-identified medical images from all over the world, with the goal of that these images can be used for training computer vision models for medicine.
[Visualizing NFL Point Differentials] We love this visualization showing the average point differential per NFL team at every point during a football game.
[Every Beat Counts] This talk about tempo from Tal Aviram at the Audio Developer Conference is making us question the nature of time itself!
As always you can contact us by replying directly to this email, you can email Jeff Leek, Amy Paguirigan, and Sean Kross at data@fredhutch.org, or 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 next week!
- The Fred Hutch Data Science Laboratory