Academic output
h-index and citation counts from OpenAlex, disambiguated by institution match, not just the highest-h-index same-named result.
Aging & longevity research, ranked with receipts
Who's actually moving the field.
Every scientist, company, and conference here is scored across five dimensions: academic output, genuine discoveries, drug discovery, funding raised, and a speculative LLM-estimated lifespan-extension impact. Every claim links to a source. Every score shows its caveats.
Data sources
- agingbiotech.infoPeople · Companies · Conferences
- Unfiltered Longevity 100People
- Who's Who in GerontologyPeople
- joinlongevity.orgPeople · Orgs
- OpenAlexAcademic output
- GPT-6 Astra + K3Consensus scoring
Why this exists
Directories list names.
This shows evidence.
Longevity has plenty of "top people" lists. Almost none of them show their work, or agree with each other.
This portal starts from seven public directories (agingbiotech.info, the Unfiltered Longevity 100, the Who's Who in Gerontology and Biology of Ageing, and others), merges duplicate entries across sources, then runs independent research on every claim: real h-index and citation counts, specific attributed discoveries, verified drug pipeline stages, cross-checked funding figures, and a clearly-labeled speculative LLM-ensemble estimate of long-run impact.
Where we found the source directories were wrong or stale, we say so, in the open, with the correction and its source. This dataset grows and re-verifies itself over time rather than shipping once and going stale.
Tier 1Appears on 2+ independent source directories. Gets full deep-research treatment.
Tier 2Appears on 1 source directory. Lighter-touch profile from that source alone.
Verified correctionA claim in the original source directory was checked and found stale, wrong, or missing key context.
Methodology
Five dimensions.
No hidden blend.
We don't collapse five very different kinds of evidence into one silent number. Each dimension is shown side by side; you can sort by whichever matters to you. Full methodology write-up is linked in Sources below.
Genuine discoveries
Specific, citable findings mapped to a 100-area taxonomy. A tag without a cited discovery doesn't count.
Drug discovery output
Real pipeline stage, cross-checked against company sites, ClinicalTrials.gov, and press coverage, not just "affiliated with a biotech."
Funding raised
Total raised and market cap, independently verified and dated. Several source-directory figures turned out to be materially stale.
LLM-ensemble impact
A frontier-model estimate of projected lifespan-extension impact, shown with its range and explicitly marked as speculative, never as measured fact.
Source score
Scientists show AgingBiotech.info's own weighted directory score. Companies and conferences show a consensus score from GPT-6 Astra and Kimi K3, each asked independently to rate R&D productivity, longevity impact, academic contribution, and funding scale on a 0-100 scale.
Source-linked, always
Every figure links to where it came from. Where we corrected a source directory, the correction and its evidence are shown, not hidden.
The explorer
Search, sort,
and see the receipts.
Switch between scientists, companies, and conferences. Every row expands into its full evidence drawer: score breakdown, source links, and any corrections we made to the original directory listing.
Loading dataset…
Reading this responsibly
Rankings are starting points, not verdicts.
Absence isn't disqualification
Someone with no listed drug-discovery output may simply be a theorist or basic-science researcher, that's an honest "not applicable," not a low score.
Directory presence tracks visibility too
Some Tier 1 names are on multiple lists because they're prominent communicators or investors, not because they're the most scientifically productive. We tag career type explicitly rather than force everyone into a "researcher" mold.
The LLM impact estimate is the softest signal here
It's one (or a few) models' speculative extrapolation, shown with range and caveat. Treat it as a conversation starter, not a forecast.
Bibliometrics reward volume and field size
h-index and citations correlate with career length and subfield size as much as with breakthrough impact. We show recency-weighted figures where available to partially correct for this.
This dataset is a living document
It re-scrapes, re-verifies, and grows over time. Corrections we've already made are shown in the open specifically so you can judge our error rate, not just our conclusions.
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Beyond the rankings
Ecosystem orgs
worth knowing.
Communities, news outlets, and investor networks that don't fit neatly into scientist/company/conference but shape the field's information flow.
Where this comes from
Seven directories.
One merged, checked dataset.
Two of the seven originally-requested sources were excluded on principle: Expertscape's robots.txt explicitly disallows automated access to its rankings, and aging-atlas.org turned out to be an unrelated commercial directory whose terms of service prohibit automated scraping. Both are documented, not silently skipped.
Full scoring methodology, the 100-area research taxonomy, and per-entity research files (including every "researcher_notes" caveat) are available in the project repository.
01 · agingbiotech.info ↗Primary structured source: people, companies, conferences, therapeutics, trials (via public Google Sheets).
02 · Unfiltered Longevity 100 ↗100-person ranked list with category and score.
03 · Who's Who in Gerontology & Biology of Ageing ↗339 academic researcher profiles.
04 · joinlongevity.org Explorer ↗Small curated mixed directory (11 entities).
05 · OpenAlex ↗Free bibliometric API used for h-index/citation verification.
06 · Independent deep research ↗Web search, company filings, ClinicalTrials.gov, and press coverage for every scored claim.