Editorial Resource for Researchers
Find the right AI tools for your research —not just another listicle.
Curated tools, honest comparisons, and step-by-step workflows for researchers using AI across the scientific process — from literature review to lab automation.
Find your workflow
Tools organized by how researchers actually work, not by AI category.
Literature Review
Discover, screen, and synthesize papers faster — without sacrificing rigor.
Explore tools →Data Analysis
Run statistical analysis and generate visualizations with natural language, no code required.
Explore tools →Writing & Submission
Polish manuscripts, manage citations, and prep for journal submission.
Explore tools →Experiment Design & Lab Automation
See how AI is starting to help design experiments and run autonomous lab workflows.
Explore tools →Built for researchers, not marketers.
Every tool listing includes honest limitations, a "last verified" date, and — where it matters — a note on pricing changes. We're not paid to rank anyone first.
Organized by your workflow, not by tool category.
You don't think in tools. You think in stages: find papers, screen them, analyze data, write it up. So do we.
Field guides for your discipline.
Biology, chemistry, physics, materials science, climate science, and more — see the tools that actually matter for your field, not a generic AI list.
Latest Tutorials
All tutorials →- Data AnalysisUsing AI to Write and Debug Research CodeHow to use AI coding assistants to write data processing scripts, debug error messages, translate analyses between languages, and document code — with guidance on verifying AI-generated code before using results in a paper.
- Writing & RevisionUsing AI for Academic Writing and RevisionHow to use AI assistants responsibly for academic writing tasks: structural feedback on drafts, clarity editing, simplifying jargon-heavy explanations, and generating abstract variants — with guidance on where to draw the disclosure line.
- Domain DiscoveryPredicting Protein Structure with the AlphaFold ServerA practical walkthrough of submitting a protein structure prediction job through the AlphaFold Server, interpreting the confidence scores in the output, and knowing when to trust the result — and when not to.
Latest Trends
All news →- July 2026AI-for-Science Catchup: Developments We've Been TrackingGenerative crystal design moves from research papers to usable tools, Elicit ships Research Agents and a public API, and autonomous lab platforms cross from industry into academic reach.
- July 2026What Changed in AI-for-Science This Month: July 2026Argonne extends its battery-materials foundation model toward molecular crystals, ECMWF's AIFS remains the operational benchmark, NOAA's GraphCast-derived models keep outperforming legacy systems, and a 45-year GraphCast hindcast archive opens up new climate research.
- July 2026GenCast's Real Numbers, and a New Open-Source ChallengerGenCast's peer-reviewed Nature results are genuinely strong — beating ECMWF's ensemble system on 97.2% of verification targets. Now NVIDIA's open-source Earth-2 suite claims to beat GenCast too, but that comparison isn't independently verified yet.
Featured Field Guides
See all field guides →Used AI in your own research?
Help other researchers skip the trial and error. We publish real, anonymized workflows from grad students and researchers — what worked, what didn't, and what you'd do differently. Your workflow could save someone else weeks.
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