AI for scientific discovery: How researchers use Claude

They are very good research assistants.

I would never trust an AI to make a scientific conclusion, but they're very good at some things:

- Plotting with natural language: "Create a scatter plot of column A versus column B, log scale Y and use stars sized by column C." Every plotting library (except maybe ggplot2 / plotnind) sucks, and the boilerplate never gets less tedius.

- Literature search, e.g. "Have there been any peer reviewed papers where they applied X method to Y domain"

- Complex visualization, e.g. "Make a self contained HTML file with a 3D interactive visualization of this graph. Add ball and spring position optimization and let me move nodes"

- Trying random stuff that would normally be too time consuming to be worth it, eg "Here is a dataset. Try regression methods A, B, and C and report a table." In this case, usually it will screw up and overfit, but it's a good smoke test.

Note how in all of these, it's being used for tedious things that provide some value, but ultimately they are only providing information to make a decision on.

Granted, I would like to have a research assistant I could train, but if your company will not create a position then there is not much you can do.

This article talks about the outputs being Claude artifacts. I am curious if anyone has feedback on using Claude artifacts? What do you like/not like?

As a hobby I run https://smalldocs.org / https://github.com/espressoplease/smalldocs an agent agnositc artifact layer, so I'm looking for feedback on artifact use in general.

Here are some sample scientific SmallDoc artifacts:

Palmer penguins: body mass, flipper length and species - https://smalldocs.org/s/YrJDCfCD8-6j_uRE0pNCbI#k=odJRxWOIySV...

AlphaFold 3: capabilities, evidence and operational limits - https://smalldocs.org/s/rRRuByQhmhZNBH_4RNCx1t#k=S0Jhyf72Q6M...

AI in science is a lot different than AI in progamming in some ways. It seems pretty harmless in programming a lot of the times because it's used to speed up rote tasks like writing boilerplate. But in science, if it does give scientists a boost in discovery, I think it's likely to lead to an even more cavalier attitude about the power that such discoveries can have, and thus lead to much more abuse of power by the wealthy and malicious actors.

> Using privacy-preserving methods, we study 207,000 Claude and Claude Code research sessions to understand how Claude is used across scientific fields and research tasks.

Did they get permission to use researchers' data or did they monitor and extract data without consent? Considering the kerfuffle OpenAI generated with their math proofs, you'd think Anthropic would take a hint and be clear that they did the research with consent, though that assumes they got consent.

Seriously, all the industry researchers at these companies went through IRB training during their PhD; have they just conveniently forgotten?

Am I the only one who finds Claude overly assertive and confident for research? I tend to get more value out of other models (e.g. Gemini 4 Argon) even if they make mistakes, and then use Claude to revise and polish.

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