Industry Guide: Research Labs
How to Use Claude in Scientific Research: Running a Lab With AI
Built for the lab, not just the individual paper.
This guide is for principal investigators, lab managers, research software engineers, and research office staff who want to put Anthropic's Claude to work across a lab or institute: shared analysis pipelines, lab documentation, grant proposals, onboarding, and review of computational work. If you want help with your own reading and writing as an individual researcher, our separate researchers guide covers that. This page is about the lab as a unit.
Knowing how to use Claude in scientific research at the lab level means building shared workflows: analysis pipelines that everyone can rerun, protocols and SOPs kept current, literature tracking for the group's questions, grant proposals drafted from the lab's own results, and a review step that checks code, calculations, and citations before anything is published. Anthropic offers a beta scientific workbench, free Team seats for eligible PIs, and research credits through its AI for Science program. Scientists keep the questions, the interpretation, and the responsibility.
Published
Key Takeaways
- At lab scale, the best uses are shared analysis pipelines, protocols and SOPs, literature tracking, grant writing, onboarding, and computational review.
- Anthropic's beta scientific workbench, launched in June 2026, includes a reviewer agent that checks citations and calculations.
- Anthropic announced 10,000 free Claude Team seats for eligible PIs and expanded AI for Science research credits in August 2026.
- Named labs in Anthropic's posts include the Cheeseman Lab at the Whitehead Institute, Stanford's Biomni project, UCSF, and the Allen Institute.
- Reproducibility and research integrity rules apply to AI-assisted work exactly as they do to any other method.
Copy and Paste
Prompts You Can Use Today
Turn a notebook into a shared pipeline
You are a research software engineer for a lab. Refactor the analysis notebook below into a reproducible pipeline: a command-line script with parameters for input paths and thresholds, pinned package versions in a requirements file, a README describing inputs and outputs, and tests that run on a small synthetic dataset. Keep the scientific logic identical and list every place where you had to guess the intended behavior. Notebook: [paste].
The list of guesses is where the original author needs to confirm intent before the lab adopts the pipeline.
Experimental design review
Review the experimental design below as a skeptical colleague in our field. Identify confounds, missing controls, underpowered comparisons, and sources of batch effects, and suggest concrete changes. For each suggestion explain the problem it prevents. Do not change the scientific question. Also list the assumptions I should state explicitly in the methods. Design: [paste].
Asking for the problem each change prevents helps you judge which suggestions actually matter.
Grant proposal specific aims draft
Draft a Specific Aims page for a grant proposal using only the preliminary results, figures legends, and research summary I have pasted. Structure it as: the gap in knowledge, the long-term goal, the objective and central hypothesis, the rationale, three aims with a sentence on approach and expected outcome each, and the expected impact. Keep claims within what the preliminary data supports and mark any claim that needs a citation with [CITE]. Material: [paste].
Check the funder's current format rules; the structure above is a common pattern, not a template from any funder.
Protocol update from a lab meeting
Here is our current SOP for the assay and the notes from today's lab meeting where we agreed on changes. Produce an updated SOP with every change tracked in a change log at the top, keep all unchanged steps word for word, and list any agreed change that conflicts with another step or with the safety notes. SOP and meeting notes: [paste].
Word-for-word preservation of unchanged steps makes the update easy to review.
Hypothesis generation from the lab's data
Based on the summary of our results and the papers I have attached, propose five testable hypotheses that could explain the unexpected finding described below. For each, state the prediction, the simplest experiment that could falsify it, and what evidence from the attached material supports or weakens it. Rank them by how cheaply they can be tested. Material: [paste].
Ranking by cost of testing keeps the output useful for planning rather than speculative.
From Anthropic
What Anthropic offers for Scientific Research Labs
Claude Science
A beta AI workbench for scientists launched in June 2026 for Pro, Max, Team, and Enterprise plans, with more than sixty curated skills and connectors, rendering of protein structures and genome tracks, compute management across laptops, HPC clusters, or GPUs, and a reviewer agent for citation and calculation verification with auditable artifacts.
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AI for Science program
An Anthropic program offering free API credits to researchers at research institutions working on high-impact scientific projects, initially focused on biology and life sciences, including biological systems, genetic data, drug discovery, and agricultural productivity.
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Expanding support for scientists
An August 2026 announcement of free standard Claude Team seats for principal investigators at academic and nonprofit institutions, expanded AI for Science credits with a scope broadened to compute-heavy research, and a life sciences verification program with the U.S. government.
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Claude for Life Sciences
Anthropic's life sciences offering, launched in October 2025, with connectors to research tools including PubMed, Benchling, BioRender, Wiley's Scholar Gateway, Synapse.org, and 10x Genomics.
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In Practice
How Scientific Research Labs Teams Use Claude
According to Anthropic's published customer story
Biomni
According to Anthropic's January 2026 post on accelerating scientific research, Biomni, a Stanford project built on Claude, completed genome-wide association studies in minutes instead of months and analyzed hundreds of wearable data files in just over half an hour.
According to Anthropic's published customer story
Cheeseman Lab
Anthropic's January 2026 post describes the Cheeseman Lab at the Whitehead Institute building a Claude-powered tool to automate interpretation of CRISPR gene knockout experiments.
According to Anthropic's published customer story
UCSF Brain Tumor Center
In Anthropic's June 2026 workbench announcement, a UCSF Brain Tumor Center researcher said the workbench enabled comprehensive germline workups in roughly a tenth of the previous time.
According to Anthropic's published customer story
Allen Institute
Anthropic's June 2026 announcement describes an Allen Institute neuroscientist producing several long reviews with citations checked by reviewer agents.
According to Anthropic's published customer story
FutureHouse
FutureHouse's co-founder, quoted in Anthropic's October 2025 life sciences announcement, said Claude helps power the organization's bioinformatics and literature analysis workflows.
Illustrative walkthrough
Making a lab's core analysis reproducible
- The lab's research software engineer asks Claude to refactor a widely used notebook into a pipeline with tests and a README.
- Claude produces the pipeline and lists the places where the intended behavior was unclear.
- The postdoc who wrote the notebook resolves each question, and one threshold turns out to have been set by accident.
- A second lab member runs the pipeline on a dataset with known results and confirms the outputs match.
- The PI approves the pipeline as the lab standard, and new members start from it.
Scientific Research Labs and Claude: the Published Numbers
| Figure | Context | Source |
|---|---|---|
| genome-wide association studies in 20 minutes | Biomni, a Claude-based agent from Stanford, per Anthropic's January 2026 post | Source |
| 450+ wearable data files from 30 people in 35 minutes | Biomni analysis, estimated as a three-week task by hand, per Anthropic's January 2026 post | Source |
| roughly one-tenth the time | Germline workups at the UCSF Brain Tumor Center, per Anthropic's June 2026 announcement | Source |
| 10,000 free standard Claude Team seats for scientists | For PIs or equivalents at academic and nonprofit institutions, per Anthropic's August 2026 announcement | Source |
Core Workflows
Claude Scientific Research Use Cases for Labs and Institutes
These are the places where AI for scientific research and AI for research labs pay off at the level of the group, not just the individual.
Shared analysis pipelines
AI for lab data analysis turns one person's notebook into a documented, tested pipeline the whole lab can rerun, which serves reproducibility.
Protocols and SOPs
Lab meeting decisions turned into updated protocols with change logs, and safety notes checked for conflicts.
Hypotheses and design
AI for hypothesis generation and AI for experimental design review, grounded in your data and attached papers, for the PI and team to judge.
Grant writing
AI for grant writing drafts aims pages, significance sections, and progress reports from the lab's own results, with claims marked for citation.
Onboarding
New students and staff get explanations of the lab's codebase, methods, and past decisions, drawn from lab documents.
Computational review
Checking code, statistics, and citations before submission, including with the reviewer agent in Anthropic's scientific workbench.
What Anthropic Offers Scientists
Anthropic's Workbench and Programs for Research Institutions
In June 2026 Anthropic launched a beta AI workbench for scientists, available on Pro, Max, Team, and Enterprise plans. According to Anthropic, it comes with more than sixty curated skills and connectors preconfigured for genomics, single-cell work, proteomics, structural biology, and cheminformatics; renders protein structures, genome browser tracks, and chemical structures; manages compute across laptops, HPC clusters, or on-demand GPUs; and includes a reviewer agent for citation and calculation verification, with auditable artifacts and full code history.
For access, Anthropic's AI for Science program has offered free API credits since May 2025 for researchers at research institutions working on high-impact projects. In August 2026 Anthropic announced 10,000 free standard Claude Team seats for PIs and equivalents at academic and nonprofit institutions, and broadened AI for Science beyond biology to ambitious, compute-heavy research.
Labs in biology can also use the life sciences connectors Anthropic has released, including PubMed, Benchling, bioRxiv and medRxiv, and Synapse.org. Check which models and connectors your institution's plan includes, since Anthropic has said some of its models block professional biology queries outside a separate verification program.
What Labs Report
How Research Labs Are Using AI
Anthropic's January 2026 post on accelerating scientific research describes Biomni, a Stanford project built on Claude, completing genome-wide association studies in minutes rather than the months such work typically takes, and analyzing hundreds of wearable data files in just over half an hour. The Cheeseman Lab at the Whitehead Institute built a Claude-powered tool to help interpret CRISPR gene knockout experiments.
In Anthropic's June 2026 workbench announcement, a UCSF Brain Tumor Center researcher described completing germline workups in roughly a tenth of the previous time, and a neuroscientist at the Allen Institute described producing long reviews with citations checked by reviewer agents. FutureHouse said Claude helps power its bioinformatics and literature analysis workflows.
The common thread is leverage on existing expertise. In each case scientists defined the question and judged the result, while Claude handled analysis, interpretation support, and checking at a pace a small team could not match.
Choosing Tools
Claude vs ChatGPT for Research, and Choosing an AI Research Assistant
The Claude vs ChatGPT for research comparison should be run on your lab's real work: a pipeline you already validated, a literature question you already answered, and a grant section already reviewed. Compare correctness of code and statistics, citation behavior, and how each handles uncertainty.
For an institution, choosing an AI research assistant also means choosing a governance model: which plan, which data may be used, how computational work is reviewed, and how AI use is disclosed in publications and grant applications under your funder's and journal's policies.
Most labs benefit from one shared setup rather than every member using a personal tool. Shared projects hold the lab's protocols, code conventions, and past decisions, so every member's work starts from the same context.
Getting Started
A First Semester Plan for a Lab
Agree lab norms
Decide what data can be used, how AI-assisted work is reviewed, and how it is disclosed in papers and grants.
Pick one pipeline
Turn the most-used analysis notebook into a tested, documented pipeline and have a second person validate it.
Build the lab project
Collect SOPs, code conventions, and key papers in one shared project so everyone works from the same context.
Check your eligibility
See whether your institution or PI qualifies for Anthropic's free Team seats or AI for Science credits.
Limits
Research Integrity, Reproducibility, and Ethics
Is it ethical to use AI in scientific research? It can be, when use is disclosed as your field and funders require, when the scientists remain accountable for every claim, and when AI-assisted analysis is as reproducible and reviewable as any other method. Undisclosed AI writing, fabricated citations, and unchecked analysis are integrity problems regardless of the tool.
Claude can produce code that runs and computes the wrong thing, or a citation that does not exist. Test pipelines on data with known answers, verify every citation, and keep code and prompts under version control so others can reproduce the work.
Research data may include human subjects information, unpublished results, or controlled data. Follow your IRB, data use agreements, and institutional policies, and use only deployments your institution has approved.
Make AI Part of Good Lab Practice
Labs that get reliable results from AI treat it like any other method: documented, tested, and reviewed. The free Nightschool AI curriculum is hands-on from the first lesson.
Frequently Asked Questions
Can AI design experiments?
AI can review and suggest improvements to experimental designs, such as missing controls, confounds, and batch effects, and propose falsifying experiments for a hypothesis. The scientific question, the final design, and the interpretation belong to the researchers, who know the system and its constraints.
Can AI generate research hypotheses?
Yes, when grounded in your data and literature, and the useful output is a ranked list of testable hypotheses with predictions and falsifying experiments. Treat them as starting points for discussion. Anthropic has also published a skill for scientific problem selection for life sciences teams.
Can AI search and summarize scientific literature?
Yes, especially with literature connectors such as PubMed and bioRxiv where your plan includes them. Restrict citations to papers you retrieved or attached, and verify each one. For individual reading and writing help, our separate researchers guide goes deeper.
Is it ethical to use AI in scientific research?
It can be, with disclosure consistent with your field, funder, and journal policies, full accountability by the scientists for every claim, and AI-assisted analysis that is reproducible and reviewed. Fabricated citations and unchecked analysis are integrity failures regardless of tool.
Can AI discover new algorithms or materials?
AI can accelerate parts of discovery work, such as searching literature, writing analysis code, and proposing candidates to test, and Anthropic's posts describe labs using Claude to speed up genomics analysis. Claims of discovery still require experimental validation and peer review.
What is Anthropic's scientific workbench?
Launched in beta in June 2026 for Pro, Max, Team, and Enterprise plans, it is an environment for scientists with more than sixty curated skills and connectors, visualization of protein structures and genome tracks, compute management across local and HPC resources, and a reviewer agent that checks citations and calculations.
Can AI help write grant proposals?
Yes, as a drafting aid built on your own preliminary results: aims pages, significance and approach sections, and progress reports. Keep claims within what your data supports, verify every citation, and follow your funder's rules on AI use and disclosure.
How can a lab get access to Claude for research?
Anthropic announced 10,000 free standard Claude Team seats for PIs and equivalents at academic and nonprofit institutions in August 2026, and its AI for Science program offers API credits for eligible projects at research institutions. Check Anthropic's announcements for current eligibility.
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