Flávia E. Rius

@flaviaerius.bsky.social

Data scientist interested in polygenic risk scores, cancer, and a few unrelated things. #rstats #bioinformatics https://flaviaerius.com

Note on using Gemini Pro for paper references: First, it hallucinated on the year and journal, making it up for a preprint paper. Second: it got wrong the order of authorship for a second paper with the same team, repeating the order of the first paper. Folks, review your AI results.

I recently discovered Conventional Comments (conventionalcomments.org) for providing a pseudo-standard set of labels for feedback and just tried it for an article review and it was really helpful to specify issues vs. thoughts vs. suggestions, etc. Hopefully it's helpful for the authors too!

We strongly suggest using the following labels:

praise:	Praises highlight something positive. Try to leave at least one of these comments per review. Do not leave false praise (which can actually be damaging). Do look for something to sincerely praise.
nitpick:	Nitpicks are trivial preference-based requests. These should be non-blocking by nature.
suggestion:	Suggestions propose improvements to the current subject. It’s important to be explicit and clear on what is being suggested and why it is an improvement. Consider using patches and the blocking or non-blocking decorations to further communicate your intent.
issue:	Issues highlight specific problems with the subject under review. These problems can be user-facing or behind the scenes. It is strongly recommended to pair this comment with a suggestion. If you are not sure if a problem exists or not, consider leaving a question.
todo:	TODO’s are small, trivial, but necessary changes. Distinguishing todo comments from issues: or suggestions: helps direct the reader’s attention to comments requiring more involvement.
question:	Questions are appropriate if you have a potential concern but are not quite sure if it’s relevant or not. Asking the author for clarification or investigation can lead to a quick resolution.
thought:	Thoughts represent an idea that popped up from reviewing. These comments are non-blocking by nature, but they are extremely valuable and can lead to more focused initiatives and mentoring opportunities.
chore:	Chores are simple tasks that must be done before the subject can be “officially” accepted. Usually, these comments reference some common process. Try to leave a link to the process description so that the reader knows how to resolve the chore.
note:	Notes are always non-blocking and simply highlight something the reader should take note of.

As a previous co-organizer for R-Ladies Seattle, I know how long a little help can go. That’s why I’m so excited that Posit is supporting community conferences and local meetups! Please share this with an organizer who wants to feed folks some pizza while learning about data science tools 🍕

Posit@posit.co · 2mo ago

Organizing a community data science event or workshop? Posit wants to help! If you need event funding, awesome swag, social media signal boosts, or guest speakers/mentors, please reach out. Learn more: opensource.posit.co/about/commun...

Come join me for a all new slidecrafting workshop! 2 hours of all new content, teaching you to go from zero to hero in quarto slides! It is free and open to anyone, very minimal quarto knowledge required, we are starting with the basics! pos.it/slide-wrkshp #quarto #slidecrafting

Slidecrafting Workshop with Emil Hvitfeldt

This will be a 2-hour workshop (with a short break) led by Emil Hvitfeldt all about making great slides in Quarto! Wed, Aug 12, 2026 12:00 PM EDT

pos.it

10 colour palettes I reach for in research charts — curated, and most are colourblind-safe. Categorical + sequential: Okabe-Ito, Paul Tol, Tableau 10, viridis, cividis, magma. Skip default tab10 and rainbow/jet. Which do you swear by? Anything I'm missing? #DataViz #ColorPalette #SciComm

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Science Bluesky is now much better than Science Twitter is… but I still miss peak pre-pandemic Science Twitter.

Katharine Hayhoe@katharinehayhoe.com · 6mo ago

Bluesky is the new science Twitter, new study by @whysharksmatter.bsky.social and Julia Wester concludes! "Results show that for every reported professional benefit that scientists once gained from Twitter, scientists can now gain that benefit more effectively on Bluesky than on Twitter."

This benckmark does not express how better Claude Code is than the other models on top. Apparently, the reasoning of Opus 4.6 being better makes all the difference. (I've tested it for some bioinformatics data analysis in the last hour and the difference is brutal, even Sonnet vs. Gemini-any).

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There is a subtle difference in transforming a vector into a file using writeLines() or write.table(col_names = F, row.names=F): the latter allows quotes in the output. It makes a lot of difference for file paths, for example. #rstats

{ellmer} 10/10 in stability to extract structured from unstructured data using #gemini API. I used type_object() to set the desired output, and json to set the input. I'm still experimenting with LLM API calls, but so far it was much better error-wise than a python script with schemas. #rstats