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🧬 Helpful new All of Us resource/preprint: common and rare variant association testing across 392,030 whole genomes and 3,602 phenotypes, with an “All by All” browser for exploring gene-phenotype associations. Preprint: www.medrxiv.org/content/10.6... Browser: allbyall.researchallofus.org

Hi Dani, I am a data scientist. If you could add me to the science feed, that would be great. Google Scholar: scholar.google.com/citations?us...

I don't know yet. Discerning meaningful discussion is a separate and more sophisticated analysis. Let me look into it and get back to you.

Thanks @altmetric.com! @rbly.bsky.social, hopefully LinkedIn will bring back its API. Until then, no idea. My hypothesis: LinkedIn's growth roughly tracks the decline in X, so its share is probably substantial. I'd be curious, but a little surprised, if it made X and Bluesky pale in comparison

Limitations: the counts extracted do not discern between the type of account (institutional, scientist, etc.) or uniqueness. Interesting blog post from last year relevant to the discussion where Altmetric inquired about the status of science on X and Bluesky, www.altmetric.com/blog/bluesky...

So what does this mean? Science discussion is spreading across more platforms, not consolidating on one. No single site appears to hold the whole conversation, and the balance shifts by field/journal. Relying on only one platform may limit both your reach and what you see.

The 29% reflects the sample. Across the seven journals, Bluesky's median share ran from 14% (Nature Medicine) to 50% (Nature Machine Intelligence and npj Digital Medicine). So there is no single number for "science." Where your field's conversation lives may be specific to your specialty.

Where are scientists discussing papers now: Bluesky or X? I sampled 350 recent papers that had at least one post on X or Bluesky. The median paper got 29% of its X+Bluesky attention on Bluesky. Bluesky is substantial, but X appears active. #AcademicSky #ScienceSky #StatsSky

This reminds me of doi.org/10.1111/sltb.... Very interesting to think about these potential health trajectories for interrelated traits, especially when “self-harm” can cover such different behaviors and relationships to suicidality.

They just had to spoil the ending before I finished reading it...

This is interesting. I’ve been thinking about cases where between-study heterogeneity varies for concrete measurement reasons, e.g. misclassification bias in studies using self-report data. Nice to see a general framework for testing when the constant-heterogeneity assumption is doing too much work.

Really promising direction. A colleague is leading a related study we've been working on, using LLMs to generate "future-self" narratives for patients with suicidal ideation, to help flag who's at risk: osf.io/preprints/ps...

Nice. The hard part for community curation is cold start from a handful of researchers. Could you bootstrap early rankings from Bluesky engagement on paper posts (likes/reposts)? It is an existing community-preference proxy, and unlike most platforms the open API makes it easy to get the data.

Might be less a tooling gap than a permissions one: commercial agents refuse CAPTCHAs by design because solving them circumvents bot-detection/site terms of use. An open model doesn't really sidestep that; it just shifts the terms of use (and institutional-policy) exposure onto the researcher.

Over 292,000 public comments already submitted on the proposed OMB rule revising federal grants guidance. The comment period closes today, July 13. Read it & comment: , www.regulations.gov/document/OMB... #SciencePolicy #SciSky #AcademicSky

Kudos to @pangram.com for quantifying something real. But I think this solves the wrong problem: the issue was never AI-assisted content, it's slop. Don't filter out thoughtful human-AI science collaboration. Instead of flagging AI-written posts, why not flag slop (human or AI)?

Defining phenotypes across 10,000s of ICD codes is manual & subjective. Phecoder semi-automates this, surfacing clinically relevant codes existing curation missed. Analysis led by Jamie Bennett. 📝 Preprint: www.medrxiv.org/content/10.6... 🖥️ Code: github.com/DiseaseNeuroGenomics/Phecoder