gjuggler

The most interesting thing here is that Claude Science runs a local server and a web-based UI that connects to that server from your browser. This is very different from Claude Code and Cowork, where the UI is more tightly coupled to the host machine (which makes things like computer use possible).

I think I recognize the strategy: most pharma environments connected to interesting data are tightly locked down, to the point where you can't just connect your Macbook to the source data.

Similarly, access to large genomic biobank datasets like UK Biobank or NIH's All of Us program is granted only through a Trusted Research Environment (TRE), a remote data analysis platform usually quite restricted on internet access, etc. You can't easily run desktop apps, but these environments do usually support running JupyterLab or VS Code, tunneling the user interface through to the end user. (Source: I previously ran the team that built the All of Us TRE.)

Claude Science looks a lot more like something one could imagine spinning up in one of those highly-constrained data environments (with the "server" running within the TRE and the UI proxied to the end user's browser) than the does-everything Claude mega-app. That will be critical for traction within pharma R&D environments.

I will say that for moderately-computational scientists, who are daily driving RStudio, JupyterLab, or maybe VS Code, Claude Science will be quite an unfamiliar shaped product. I'll be curious to see whether something like this gains adoption (1) in place of, (2) alongside, or (3) eventually wrapping around the more traditional data science workbench tools out there.

Thanks for sharing your process! It's helpful and refreshing to hear from someone about how they engage with AI when writing, and where / when the detection tools may fail.

(We obviously live in a more nuanced world than most social media interactions might make you think :P)

> On a lighter note, decades ago, in middle school, we had an exercise to summarize a book we read.

My first experience with plagiarism was in first grade, when we were told to write a book report about a subject during our library time. I diligently took my book on the musk ox and copied three pages word-for-word into my notebook as my report. I can't remember when or how we learned this wasn't "right", but I still think back on that and laugh.

It's important to recognize that PLOS' new data sharing policy is only about data directly tied to the publication of a traditional journal article. So while PLOS authors are being forced to share their data, it's only done in the context of a published article, which will accrue citations, recognition, and attribution in the standard way.

So any data shared by this policy WILL absolutely be tied to the traditional methods of scientific credit, via the linked journal article. To me, requiring that reasonable data is published and archived alongside scientific literature doesn't seem absurd at all.

> Journals try to enforce data sharing because they want to maintain their position as arbiters of academic affairs.

Is there any evidence behind this claim? PLOS seems to behave in exactly the opposite way. It's true that SOME journals use extreme selectivity or control over copyright to maintain their position as arbiters of science. But PLOS, whose largest journal PLOS One is both open access and makes no judgment on the impact of the science it publishes, seems to be actively reducing the amount of control it exerts over academic activities.

While this problem may be true of certain poor actors in some fields of science, I would argue that it's far more the exception than the rule.

My experience (in genomics / evolutionary genetics) was overwhelmingly positive, with huge amounts of collaboration all around. Scientists did try to publish at the highest-impact journal because they care about their career prospects, and there were often competitive labs racing to the finish line with a breakthrough publication.

But I never saw researchers withhold important details in order to accelerate their own publication or to hamper the efforts of others. By and large, I saw huge teams collaborating on large projects with a deep sense of purpose to move the field forward and improve understanding.

In fact research funding in genomics became so collaborative — with increasing portions of the research budget being put towards huge consortium-based projects — that smaller labs began criticizing funding agencies because their smaller projects weren't being funded. These projects are typically more competitive and higher risk, yielding potentially high-impact articles with a far smaller number of authors.

What's the answer in the end? As with many things, it's balance. Cooperation is great, but to a fault. Sometimes having small labs working in relative isolation, even competing against other small labs, can yield great innovation and progress. Other times you need a huge collaborative effort to do something big, expensive and important for the future of the field.