Emerging API: Rank the fastest-accelerating research topics across ~4,500 OpenAlex
Rank the fastest-accelerating research topics across ~4,500 OpenAlex topics, weekly — returns rank, momentum z-score, growth, acceleration and citation-quality growth per topic, filterable by field. Use to find what is accelerating.
$0.05 per call — pay with prepaid card credits (Authorization: Bearer ck_live_…, packs from $1) or with USDC over x402: call it, get 402 with the price, retry signed. Failed calls are never charged.
How do you call it?
GET /v1/technology/emerging — product id
technology_emerging. Over MCP, call answerpool_get with
product_id=technology_emerging.
curl "https://answerpool.io/v1/technology/emerging?limit=25"
# with a prepaid credit key
curl -H "Authorization: Bearer ck_live_..." "https://answerpool.io/v1/technology/emerging?limit=25"Live call: https://answerpool.io/v1/technology/emerging?limit=25 · JSON sample: /v1/samples/technology_emerging
What does the answer look like?
A representative response, the same static sample served at /v1/samples/technology_emerging.
scan_id | scan_2026w35 |
|---|---|
computed_at | 2026-08-24T06:00:00Z |
data_as_of | 2026-08-24 |
last_full_year | 2025 |
universe_size | 4516 |
method_id | technology_momentum_stat_v1 |
refresh_after | 2026-09-18T00:00:00.000Z |
topics
| topic_id | name | field | subfield | rank | momentum_stat | growth_1y | growth_3y | growth_5y | acceleration_3y | decile_growth_3y | works_last_full_year |
|---|---|---|---|---|---|---|---|---|---|---|---|
| T12026 | Quantum error correction | Physical Sciences | Condensed Matter Physics | 3 | 3.41 | 0.38 | 1.9 | 3.2 | 0.6 | 1.4 | 1841 |
The same sample as raw JSON
{
"scan_id": "scan_2026w35",
"computed_at": "2026-08-24T06:00:00Z",
"data_as_of": "2026-08-24",
"last_full_year": 2025,
"universe_size": 4516,
"method_id": "technology_momentum_stat_v1",
"topics": [
{
"topic_id": "T12026",
"name": "Quantum error correction",
"field": "Physical Sciences",
"subfield": "Condensed Matter Physics",
"rank": 3,
"momentum_stat": 3.41,
"growth_1y": 0.38,
"growth_3y": 1.9,
"growth_5y": 3.2,
"acceleration_3y": 0.6,
"decile_growth_3y": 1.4,
"works_last_full_year": 1841,
"works_10y": 6210,
"is_emerging": true
}
],
"refresh_after": "2026-09-18T00:00:00.000Z"
}How fresh is it, and where does the data come from?
The fastest source behind this answer can change daily, so every response carries a refresh_after timestamp — right now it would be 2026-09-18T00:00:00.000Z. Schedule the next call on that value rather than on a guess.
- OpenAlex (CC0) — upstream:
https://api.openalex.org
Method version 0.1.0 ·
serving cache 7 days ·
every response carries a result_id you can resolve at
/v1/provenance.
When should you use it?
You need a ranked, evidence-backed list of accelerating fields (optionally within one discipline) to seed scouting, diligence, or content, and would otherwise scan topic by topic.
What you skip building
- thousands of per-topic queries
- universe-wide normalization
- weekly recomputation
When should you not use it?
You already know the topic and want a judgment with leaders and works — use technology_momentum; or you want one topic's numbers — use technology_lookup.
Where else can you find this?
- Catalog entry (JSON, free)
- OpenAPI schema — input and output types, prices, the 402 flow
- llms.txt — every product in one plain-text document