Step-by-step workflows and tool comparisons for using AI at every stage of the research process.
A 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.
How 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.
How 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.
A 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.
A structured workflow for using Elicit and Semantic Scholar together to find, screen, and extract from a large literature — built around the specific demands of a PhD-level review rather than a quick search.
How to upload a dataset to Julius AI, use natural language to run descriptive statistics, generate visualizations, and identify patterns worth investigating — with notes on verifying the code it writes.
A practical guide to using large language models for scientific writing tasks — covering what they genuinely help with, where they introduce risk, and how to prompt them to get useful output rather than generic text.
A practical workflow for uploading a paper collection to NotebookLM, asking targeted questions across sources, and building a working synthesis — without reading every paper in full before knowing what matters.
How to use Perplexity to rapidly orient yourself in an unfamiliar research area — building a conceptual map, identifying key terms, and finding the primary sources worth reading — before committing to a full literature search.
A practical workflow for combining Semantic Scholar's search with ResearchRabbit's citation network visualization to rapidly build a comprehensive map of a research field — identifying the landmark papers, key authors, and active frontiers.
A step-by-step guide to importing search results from Scopus or PubMed into Rayyan, configuring blind dual-review, using AI suggestions to prioritize screening, and resolving conflicts — producing a PRISMA-compliant screened record set.
How to use Zotero as the foundation of a research reading system, combine it with AI tools for synthesis, and maintain a citation library that actually stays organized.
A practical step-by-step workflow for using AI tools to accelerate systematic literature reviews — from initial discovery through structured extraction, synthesis, and reference management.
A practical comparison of three major academic literature databases — covering coverage, cost, API access, and which one fits different research use cases, including free alternatives to institutional subscriptions.
Three different tools all claim to help researchers find and synthesize literature — but they work very differently. Deep Research writes a report from web sources; Perplexity answers questions with inline citations; Elicit extracts structured data from academic databases. Here is when to use each.
A practical comparison of three approaches to AI-assisted research data analysis — covering when natural-language tools are genuinely useful, when they aren't, and how to combine them with a traditional coding workflow.
A practical comparison of the two most widely used AI writing assistants in academic research — covering where each one excels, honest limitations, and when to use one over the other.
Zotero is open-source and free with the richest plugin ecosystem; Mendeley is polished and Elsevier-integrated but raises data ownership concerns; Paperpile is the cleanest Google Docs integration but requires a paid subscription. This comparison covers the practical differences that affect daily research workflows.
AlphaFold, ESMFold, and RFdiffusion are often mentioned together but address fundamentally different problems. This page explains what each one actually does and which task each is built for.
A practical comparison of the three most widely used academic reference managers — covering cost, storage, collaboration, and which type of researcher each one suits best.
GraphCast, GenCast, and Pangu-Weather are the three most prominent AI global weather forecasting models. This page explains how they differ and which is the right tool for each use case.
A practical comparison of the four most-used AI tools for academic literature review — covering where each one fits, honest limitations, and recommended combinations by project type.
