About NIH Grant Explorer

This site is run by a named person, not an anonymous team. Dr. Meng Zhao is the editorial lead and sole editor of NIH Grant Explorer. Every tool description, reference page, and guide published here passes through her review before it goes live; there are no ghostwritten posts attributed to invented staff.

Meng Zhao oversees long-form editorial coverage on NIH grant strategy, study section behavior, and mechanism selection at NIH Grant Explorer. Her work focuses on translating raw NIH RePORTER data and publicly available federal records into practical guidance for first-time R01 applicants, postdocs preparing K-series transitions, and trainees navigating F-series fellowships.

NIH publishes every funded project through RePORTER, so the raw data has been public for years. The problem is that raw award records rarely answer the questions researchers actually have. A graduate student finishing a PhD wants to know which labs in her subfield received new funding this year, because those are the labs most likely to be hiring. A postdoc weighing a K99 application against a faculty search wants to know whether his topic's funding is growing or shrinking, and at which institute. A first-time R01 applicant wants to know whether an R21 is a sensible stepping stone or a detour, and which study sections see proposals like hers.

Answering any of those questions from raw RePORTER records means running repeated searches, exporting results, and building your own spreadsheets. Most trainees and faculty do not have time for that. NIH Grant Explorer exists to do that aggregation work once, present it clearly, and pair it with guides that explain what the numbers do and do not mean. The goal is not to replace official NIH guidance; it is to help you arrive at the official sources with a sharper question.

The site began as an internal set of scripts for answering exactly these questions and grew into a public resource once it became clear how many people were rebuilding the same spreadsheets independently. The audience it serves first is graduate students, postdocs, and early-career faculty, though research administrators and science journalists use the same tools.

The site has three kinds of content, all free to use:

Interactive tools that query NIH data

• Trends: charts of NIH funding for any keyword across fiscal years, with award counts, totals, and top institutes.
• PI Finder: search recently funded principal investigators by research area, useful for lab discovery and outreach.
• Weekly Updates: grants awarded in the most recent reporting window.
• Grant Search: a filterable search across funded projects by keyword, mechanism, institute, and year.
• Compare Topics: side-by-side funding trajectories for two research topics.
• Institute Fit: which NIH institutes and centers fund work on a given topic, and in what proportion.
• Check PI: look up an investigator's award history before contacting a lab.
• Grant Output: publications linked to a grant, drawn from public NCBI records.

Long-form guides

The guides section currently holds 49 long-form articles on funding strategy (mechanism selection, study sections, resubmission), career navigation (using award data to find labs and mentors), and how to read NIH data without over-interpreting it.

Reference pages

Reference pages cover NIH activity codes (R01, R21, K99, F32, and the rest) and research topics, each combining a plain-language explanation with recent award data.

NIH RePORTER is the authoritative federal database of funded projects, and nothing on this site overrides it. When the two disagree, RePORTER is right. What this site adds on top of the raw database:

• Trend aggregation: multi-year totals, counts, and institute breakdowns for a topic in one chart instead of dozens of separate searches.
• Decision frames: tools organized around real choices (which mechanism, which institute, which labs) rather than around database fields.
• Plain-language guides: explanations of mechanisms, study sections, and application strategy that the database itself does not provide.
• Hiring-signal workflows: using new awards as a starting point for lab discovery and PI outreach, with caveats about what a new award does and does not guarantee.
• Source identifiers: grant displays include official project numbers so you can find and verify the underlying RePORTER record quickly.

Our Data & Methodology page documents exactly which API powers each tool and where the numbers can mislead.

NIH Grant Explorer is supported by on-page advertising. Every tool, guide, and reference page is free to use: there are no paywalls, no premium tiers, no affiliate links, and no sponsored rankings. No lab, university, or company can pay to appear in search results, trend charts, or guides, and advertisers have no influence over editorial content.

The site is independent. It is not affiliated with, endorsed by, or operated on behalf of the National Institutes of Health, the Department of Health and Human Services, or any federal agency. When a question touches eligibility, compliance, or submission policy, we link to the official NIH source rather than answering in its place.

Three published documents describe how pages are reviewed before and after publication:

• Editorial Guidelines: the standards every guide is checked against (accuracy, sourcing, caveats, no overstated claims).
• Data & Methodology: where the numbers come from, how often they refresh, and known limitations.
• Contributors: who does the work and which review checklist applies to each content type.

The short version: guides are checked for factual accuracy against official NIH sources before publication, data displays carry caveats where the underlying records are noisy or lagged, and pages show an updated date when their substance changes. None of this makes the site infallible, which is why every grant record links back to its official entry on reporter.nih.gov.

Spotted an error? Email admin@labcat.ai or use the contact page with the page URL, and it will be checked against the underlying NIH record.