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 →- Literature ReviewUsing Deep Research (OpenAI) for a Rapid Literature ScanA step-by-step workflow for using OpenAI's Deep Research to produce a cited landscape overview in 20–30 minutes, followed by a verification pass to catch hallucinations before you build on the output.
- 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.
Latest Trends
All news →- October 2026What Changed in AI-for-Science: October 2026 DigestGoogle Gemini for Science launches with Co-Scientist multi-agent hypothesis engine; an open-source model beats GPT-4 on citation accuracy; Nature reveals pharma's private protein data advantage; a new model predicts protein-protein interactions. Plus: Deep Research expands to ChatGPT Plus, Evo 2 enters standard practice, and Overleaf AI exits beta.
- September 2026What Changed in AI-for-Science: September 2026 DigestEvo 2's 270B-parameter genomic model is the new reference point for sequence-level biology, ECMWF's ensemble weather AI extends forecast skill further, and three major tool pricing overhauls are worth knowing about.
- July 2026Drug Firms' Private Protein Data Supercharge AI Models — and Raise Access QuestionsA Nature investigation reveals that AI protein models trained on proprietary pharmaceutical data from over 20,000 structures outperform public-data models including AlphaFold variants — raising questions about who benefits from AI advances in structural biology.
Featured Field Guides
See all field guides →Used AI in your own research?
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