# OpenAlex Research Radar — Impact, Funders & Competitors (`ryanclinton/openalex-research-search`) Actor

Understand a research field in one run: landmark and rising papers by field-normalized impact (FWCI), the institution and funder graph, who's winning, and where the opportunities are. Deterministic research intelligence, not raw rows.

- **URL**: https://apify.com/ryanclinton/openalex-research-search.md
- **Developed by:** [Ryan Clinton](https://apify.com/ryanclinton) (community)
- **Categories:** AI, Developer tools
- **Features:** Limited permissions
- **Stats:** 10 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 paper fetcheds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## OpenAlex Research Radar -- Impact, Funders & Competitors

![OpenAlex Research Radar -- understand a research field in one run](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/hero.png)

**Understand a research field in one run.** Point this actor at any topic and get back the landmark papers, the rising papers, the leading institutions, the dominant funders, the emerging opportunities, the competitive landscape, and the research risks behind it. Built on [OpenAlex](https://openalex.org/) (250M+ scholarly works), fully deterministic, no LLMs.

It answers the question every OpenAlex wrapper leaves you with: **"I got 10,000 papers, now what?"** Instead of rows, you get decisions: which work is genuinely high-impact for its field, who is winning the field, where it's growing, and which papers are safe to read or cite first -- as structured JSON your agent, dashboard, or workflow can branch on.

#### Questions this actor answers

- Which papers can I not afford to miss?
- Who is winning this field, and which institutions are emerging?
- Where is the funding flowing?
- What topics are accelerating, and which are mature or declining?
- Which opportunities are high-growth-low-competition or underfunded-but-high-impact?
- Which papers are safe to ingest into a RAG corpus?
- What changed in this field since last month?

![Questions this actor answers](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/questions.png)

#### Best for

- **Find the papers that matter, not just the papers that are old** -- landmark and rising work ranked by impact normalized for its field.
- **Map a field** -- the leading institutions, countries, and funders, and how it's growing.
- **See who's winning** -- institutions ranked head-to-head by research momentum, with a market-map of leaders, challengers, and emerging players.
- **Find where the opportunities are** -- high-growth, low-competition and underfunded-high-impact areas, surfaced as deterministic signals.
- **Spot what's breaking out** -- young, fast-accelerating papers before their citation counts catch up.
- **Monitor research shifts** -- scheduled runs that surface what's new, rising, or retracted.
- **Build a citation-safe / open-access corpus** -- a RAG-ready manifest with retracted work excluded.

The engine underneath leans on FWCI, OpenAlex's field-weighted citation impact; the "Why this is different" section explains it. Every score is deterministic, so the same query returns the same intelligence every run.

#### Ready-to-run examples

One-click, pre-configured runs (edit the query and go):

- [Find landmark research papers in any field](https://apify.com/ryanclinton/openalex-research-search/examples/find-landmark-research-papers) -- top work ranked by field-normalized impact (FWCI), not raw citations.
- [Find rising and breakout research papers](https://apify.com/ryanclinton/openalex-research-search/examples/find-rising-research-papers) -- young papers accelerating before their citation counts catch up.
- [Map a research field: institutions, funders and topics](https://apify.com/ryanclinton/openalex-research-search/examples/map-a-research-field) -- the institution / funder / topic graph and growth behind a field.
- [Build a citation-safe RAG corpus](https://apify.com/ryanclinton/openalex-research-search/examples/citation-safe-rag-corpus) -- open-access, retraction-checked papers ready to ingest.
- [Triage research papers: what to read, cite or skip](https://apify.com/ryanclinton/openalex-research-search/examples/research-paper-triage) -- every paper scored must-read / read / scan / skip.
- [Compare research institutions head-to-head](https://apify.com/ryanclinton/openalex-research-search/examples/compare-research-institutions) -- rank institutions by research momentum to see who's winning a field.
- [Find emerging research topics and opportunities](https://apify.com/ryanclinton/openalex-research-search/examples/find-emerging-research-topics) -- topic growth, trends, and high-growth-low-competition openings.
- [Monitor a research field for changes](https://apify.com/ryanclinton/openalex-research-search/examples/monitor-a-research-field) -- scheduled runs that surface new, rising, and retracted work since last run.
- [Who is winning a research field](https://apify.com/ryanclinton/openalex-research-search/examples/who-is-winning-this-field) -- discover the leading institutions in a field, ranked by research momentum.
- [Research funding landscape](https://apify.com/ryanclinton/openalex-research-search/examples/research-funding-landscape) -- the funders behind a field, ranked by works funded and the impact of that work.

See all on the [examples page](https://apify.com/ryanclinton/openalex-research-search/examples).

***

![Rows in, shortlist out: 10,000 papers become the few that matter](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/before-after.png)

### Why this is different

Raw citation counts reward old papers and big fields. **Field-weighted citation impact (FWCI)** normalizes for both: a 50-citation pure-maths paper can outrank a 500-citation computer-science paper because FWCI compares each work against others *in its own field and year*. This actor leads with that signal -- the thing a "sort by citations" tool structurally cannot tell you -- and with the **scientometric graph** (institutions, countries, authors, funders) that turns "a list of papers" into "a map of who does this research, where, and on whose money."

#### Which academic-search actor should I use?

This actor owns the **bibliometric / scientometric** lane: field-normalized impact and the institution / author / funder / concept graph across all of scholarship. Reach for a sibling when your question is shaped differently:

| Your question | Use |
|---------------|-----|
| How impactful is this work *for its field*, and who / where / what-funding drives the area? | **This actor** (OpenAlex) |
| Just give me the single decision / answer for a research question | [Semantic Scholar Research Intelligence](https://apify.com/ryanclinton/semantic-scholar-search) |
| Compress clinical papers into evidence units and grade the evidence | [PubMed Evidence Search](https://apify.com/ryanclinton/pubmed-research-search) |
| Bulk metadata with BibTeX / OA / retraction flags from a DOI list | [Crossref Academic Paper Search](https://apify.com/ryanclinton/crossref-paper-search) |
| Is this *preprint* safe to cite, and does it have code? | [arXiv Preprint Intelligence](https://apify.com/ryanclinton/arxiv-paper-search) |

What this actor surfaces that the siblings structurally can't: the **institution graph** (ROR IDs + country codes + type), the **author graph** (ORCID coverage), the **funder graph**, and the full **concept hierarchy** (topic → subfield → field → domain) -- scored and ranked across the whole corpus, then handed to you as **decisions** (`must-read` / `read` / `scan` / `skip`) a downstream Dify, n8n, or agent node branches on without parsing prose.

***

![Raw OpenAlex wrapper vs Research Radar: same data, different output](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/comparison.png)

### What you get from one call

Records are grouped by job (each carries a `recordType`). Read the brief first; reach for the rest when you need them.

**Research intelligence** -- understand the field

- **`research-brief`** -- the hero synthesis: canonical topic, executive summary, key findings, must-read papers, ordered reading list, suggested next queries, confidence read.
- **`research-landscape`** -- field / concept distribution, institution and country leaderboards, funder landscape, OA profile, impact distribution, timeline, maturity, honest gaps.
- **`field-benchmark`** -- median FWCI vs the 1.0 field baseline, share beating the field, top-1% / top-10% counts, overperformers, under-cited-but-rising.
- **`paper`** -- one per work: impact class, dual-axis scores, momentum, emergence class, OA status, integrity + citation-risk, confidence + caveats, the entity graph, and a recommendation.

**Research signals** -- what's growing and where the openings are

- **`trend-signal`** -- topic-growth class (exploding / growing / steady / declining) from true field-level volume.
- **`opportunity-signal`** -- deterministic, methodology-on-the-record heuristics: high-growth-low-competition, underfunded-high-impact.
- **`research-delta`** -- monitor mode: what's new, rising, or retracted since the last run, with the alerts that triggered.

**Competitive intelligence** -- who's winning

- **`institution-comparison`** -- institutions ranked head-to-head by research momentum, each with dominant + emerging topics and a named leader.
- **`market-map`** -- institutions and countries placed in a leaders / challengers / emerging / niche quadrant with volume and impact percentiles.

**Entity intelligence** -- the graph behind the field

- **`topic-profile`** -- maturity stage, real growth + velocity + trajectory, leading countries / institutions / funders, citation concentration, adjacent topics.
- **`institution-profile` / `funder-profile` / `author-profile` / `source-profile`** -- each entity's footprint in the result set, plus a `collaboration-network` insight (density, dominant + bridge institutions).
- **`search-plan` / `summary`** -- the interpreted query (filters, counts) and run-level headline metrics (mirrored to the `SUMMARY` key-value record).

Key-value exports: `SUMMARY`, `RESEARCH_BRIEF`, `RESEARCH_LANDSCAPE`, `FIELD_BENCHMARK`, `QUERY_AUDIT`, and `RAG_MANIFEST` (citation-safe open-access subset) on every run; `GRAPH_NODES` / `GRAPH_EDGES` (for Gephi / Neo4j / Cytoscape) and `BIBTEX_EXPORT` / `RIS_EXPORT` when opted in; `TOPIC_PROFILE`, `MARKET_MAP`, `INSTITUTION_COMPARISON` when those modes run.

![What you also get: find landmark papers, see who's winning, find what's next, build citation-safe corpora](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/feature-callouts.png)

#### Sample output

Research brief (the record most users read first):

```json
{
  "recordType": "research-brief",
  "headline": "CRISPR Gene Editing -- 100 papers, mature field, high confidence",
  "mustRead": [
    { "title": "Multiplex genome engineering using CRISPR/Cas systems", "impactClass": "landmark", "fwci": 142.3, "oa": true }
  ],
  "confidence": { "level": "high", "explanation": "100 papers analysed and 78% carry a field-normalized impact signal." }
}
```

Opportunity signal:

```json
{
  "recordType": "opportunity-signal",
  "signalType": "high_growth_low_competition",
  "topic": "Agentic AI evaluation",
  "growthScore": 92, "competitionScore": 41, "opportunityScore": 89,
  "methodology": "growthScore = clamp(50 + YoY %); competitionScore = log10(field works) x 16.7; opportunityScore = growth x 0.6 + (100 - competition) x 0.4.",
  "limitations": "Deterministic heuristic, not a forecast."
}
```

Rising paper (the breakout the citation count hasn't caught up to yet):

```json
{
  "recordType": "paper",
  "title": "Toolformer: Language Models Can Teach Themselves to Use Tools",
  "emergenceClass": "breakout",
  "emergenceScore": 91,
  "citationMomentum": "accelerating",
  "recommendation": "must-read"
}
```

Topic profile:

```json
{
  "recordType": "topic-profile",
  "topic": "Large Language Model Agents",
  "maturityStage": "growth",
  "publicationGrowthYoYPct": 184,
  "trajectory": { "direction": "accelerating", "confidence": "high" },
  "leadingInstitutions": [ { "name": "Stanford University", "papers": 9 } ]
}
```

Market map (quadrants are deterministic and fully auditable -- every entry carries its volume and impact percentile):

```json
{
  "recordType": "market-map",
  "institutions": [
    { "name": "Massachusetts Institute of Technology", "works": 14, "avgImpact": 71, "volumePercentile": 96, "impactPercentile": 88, "quadrant": "leader" }
  ]
}
```

Research delta (monitor mode, what changed since last run):

```json
{
  "recordType": "research-delta",
  "newPaperCount": 23,
  "newHighImpactCount": 4,
  "risingPaperCount": 11,
  "triggeredAlerts": ["new_high_impact", "citation_breakout"]
}
```

Institution comparison (competitive mode):

```json
{
  "recordType": "institution-comparison",
  "leader": "Massachusetts Institute of Technology",
  "ranking": [
    { "rank": 1, "name": "Massachusetts Institute of Technology", "researchMomentum": 84, "works": 50, "highImpactShare": 0.42 }
  ]
}
```

***

![Sample output: papers ranked with impact class, FWCI, emergence, and a recommendation](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/output-table.png)

![Discovery products: Opportunity Finder and Rising Papers](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/discovery-cards.png)

### The intelligence layer (per paper)

| Signal | What it tells you |
|--------|-------------------|
| `impactClass` | Field-normalized tier: `landmark` (top 1%) · `high-impact` (top 10%) · `influential` · `solid` · `emerging` · `low-citation` · `unranked` |
| `fwci` | Field-weighted citation impact (1.0 = field average; 10+ = landmark) |
| `citationPercentile` | Field+year normalized percentile, with top-1% / top-10% flags |
| `impactScore` / `relevanceScore` | Dual axis: how influential (normalized) vs how well it fits your query -- they genuinely disagree, so you sort by the question you're asking |
| `score` | Flagship composite (0-100), weighted by your analysis mode, with a `scoreFactors` breakdown |
| `citationMomentum` | `accelerating` · `steady` · `declining` · `new` -- from the year-by-year citation trajectory |
| `emergenceClass` / `emergenceScore` | `breakout` · `rising` · `watch` · `normal` -- the trajectory axis: a young, accelerating paper reads as `breakout` even before its FWCI settles |
| `scoreConfidence` / `knownLimitations` | `high` · `medium` · `low` plus plain-English caveats, so a sparse-metadata or very-recent score is never read as authoritative |
| `openAccessStatus` | `gold` · `green` · `hybrid` · `bronze` · `diamond` · `closed`, plus a free-to-read URL |
| `integrityStatus` / `citationSafe` / `ragSafe` | Retraction and paratext flags; whether it's safe to cite and safe to feed a RAG index |
| `citationRisk` | `low` · `medium` · `high` with reasons, tiered from retraction, citation-window, author concentration, and missing field-normalized signal |
| `field` / `subfield` / `domain` / `topConcepts` | The concept hierarchy and Wikidata-linked concepts |
| `institutions` / `leadInstitution` / `countries` | The affiliation graph with ROR IDs, countries, and types |
| `orcidShare` / `funders` / `collaborationScope` | Author disambiguation coverage, funding, and `single` / `multi-institution` / `international` reach |
| `recommendation` / `whyRead` / `summary` | The decision, the reasons, and an LLM-quotable one-liner |

***

### How it works

```
Search query + filters
        |
        v
  [Fetch works]  ----->  OpenAlex API (free polite pool, 200 per page)
        |
        v
  [Score each work]  - field-normalized impact class + dual-axis scores
        |             - citation momentum, OA + integrity, concept graph
        |             - institution / author / funder graph
        v
  [Build landscape]  - field/concept/institution/country/funder leaderboards
        |             - maturity, impact distribution, honest gaps, drift
        v
  [Build research brief]  - must-read papers, reading order, next queries
        |
        v
  [Stream records]  ----->  decision-first dataset views + KV mirrors
```

Analysis is deterministic and computed from the works already fetched -- no extra paid API calls for the landscape or brief.

***

![What we add: from search to OpenAlex works, impact, emergence, the entity graph, signals, and decisions](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/intelligence-layers.png)

### Analysis modes

Pick the mode that matches your job; it reweights the flagship `score`:

- **Discovery** -- favours query-fit (default; "what's relevant to my search?")
- **Impact ranking** -- favours field-normalized impact ("what are the landmark papers?")
- **Landscape** -- balances both ("map the whole field")
- **Institutional** -- emphasises the affiliation graph ("who's doing this work?")
- **Monitoring** -- for scheduled re-runs that track a field over time

***

![What changed since last run: new high-impact papers, citation risers, retractions](https://apifyforge.com/readme-assets/ryanclinton-openalex-research-search/change-feed.png)

### How to use

1. Open the actor in Apify Console and click **Start**.
2. Enter a **Search Query** (searches titles, abstracts, and full text).
3. Pick an **Analysis Mode** and set **Max Results**.
4. Optionally filter by year (single or range), minimum citations, work type, country, open access, or minimum impact class.
5. Run, then read the `research-brief` first, browse the **Decisions** dataset view, or pull the `SUMMARY` / `RESEARCH_BRIEF` / `RESEARCH_LANDSCAPE` key-value records.

No API key is required -- the public OpenAlex works endpoint is free and keyless via the polite pool (100k calls/day). Supply an OpenAlex key only if you have a premium plan and want its higher limits.

#### Example input

```json
{
    "searchQuery": "CRISPR gene editing",
    "analysisMode": "impact-ranking",
    "fromPublicationYear": 2020,
    "toPublicationYear": 2024,
    "minImpactClass": "influential",
    "openAccessOnly": false,
    "maxResults": 100
}
```

#### Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("kbV7IqCW7tszfXB96").call(run_input={
    "searchQuery": "transformer neural network architecture",
    "analysisMode": "impact-ranking",
    "fromPublicationYear": 2020,
    "maxResults": 100
})

for rec in client.dataset(run["defaultDatasetId"]).iterate_items():
    if rec.get("recordType") == "paper":
        print(f"[{rec['impactClass']}] {rec['title']} - FWCI {rec.get('fwci')}")
```

***

### Input parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `searchQuery` | String (required) | -- | Keyword search across titles, abstracts, and full text (ignored in identifier mode) |
| `inputMode` | String | `query` | `query` (keyword search) or `identifiers` (enrich DOIs / OpenAlex IDs, or pull an author's / institution's works) |
| `identifiers` | String\[] | -- | Identifier mode: DOIs, OpenAlex work IDs (W…), ORCIDs, author IDs (A…), RORs, institution IDs (I…) |
| `analysisMode` | String | `discovery` | `discovery` · `impact-ranking` · `landscape` · `institutional` · `monitoring` |
| `maxResults` | Integer | `50` | Works to analyse, 1–10,000 |
| `minImpactClass` | String | `any` | Keep only `solid` / `influential` / `high-impact` / `landmark` and above (excluded works are counted, not hidden) |
| `excludeRetracted` | Boolean | `false` | Drop retracted works (counted in the summary) |
| `openAccessOnly` | Boolean | `false` | Only freely available works |
| `publicationYear` | Integer | -- | Exact publication year |
| `fromPublicationYear` / `toPublicationYear` | Integer | -- | Publication-year range (inclusive) |
| `minCitations` | Integer | -- | Minimum citation count |
| `workType` | String | -- | One OpenAlex work type (e.g. `article`, `review`, `preprint`) |
| `country` | String | -- | 2-letter country code of at least one author affiliation |
| `sortBy` | String | `relevance_score:desc` | `relevance_score:desc` · `cited_by_count:desc` · `publication_date:desc` |
| `outputProfile` | String | `standard` | `minimal` · `standard` · `full` |
| `outputMode` | String | `papers` | `papers` (full output) or `dashboard` (landscape digest only, single charge) |
| `emitResearchBrief` | Boolean | `true` | Include the research-brief hero record |
| `includeEntityProfiles` | Boolean | `false` | Emit institution / funder / author / source profiles + a collaboration-network insight (corpus-derived, no extra API calls) |
| `includeTopicIntelligence` | Boolean | `false` | Query mode only: emit topic-profile + trend-signal + opportunity-signal (one extra group_by query for true field growth) |
| `compareInstitutions` | String\[] | -- | Competitive mode: institution names / RORs / OpenAlex IDs to rank head-to-head (optionally scoped to `searchQuery`) |
| `includeGraphExport` | Boolean | `false` | Write `GRAPH_NODES` / `GRAPH_EDGES` to the key-value store |
| `includeCitationExports` | Boolean | `false` | Write `BIBTEX_EXPORT` / `RIS_EXPORT` to the key-value store |
| `monitorMode` | Boolean | `false` | Track this query across scheduled runs |
| `watchlistName` | String | `default` | Name for the monitored baseline (monitor mode only) |
| `monitorSensitivity` | String | `medium` | `low` (high-impact only) · `medium` · `high` (everything) |
| `alertOn` | String\[] | -- | Restrict monitor alerts: `new_work` · `new_high_impact` · `new_landmark` · `citation_breakout` · `retraction` |
| `apiKey` | String | -- | Optional OpenAlex premium key for higher limits |

***

This is competitive, technology, and innovation intelligence built on the scholarly record -- not just an academic search tool.

- **Strategy & management consultants** -- map a technology landscape end to end: leaders, challengers, and emerging players, with the funders and institutions behind them.
- **Corporate R\&D & innovation teams** -- track where a field is moving, who's accelerating, and which adjacent areas are opening up.
- **VCs & technology scouts** -- find accelerating research clusters and high-growth-low-competition openings before they're obvious.
- **Universities & research offices** -- benchmark institutions head-to-head, spot emerging competitors, track how a field and its funding are shifting.
- **Grant writers & research funders** -- see where funding concentrates and which high-impact areas look underfunded.
- **AI / RAG builders** -- assemble a citation-safe, open-access corpus (`ragSafe`) and monitor a domain on a schedule.
- **Researchers & academics** -- get the must-read and rising papers and an ordered reading list instead of a 500-row dump.
- **AI agents** -- call this for decisions (`recommendation`, `impactClass`, `decision`-grade enums) they branch on without parsing prose.

***

### Performance & cost

Deterministic analysis runs in-process on the works already fetched, so the landscape and brief add no extra API calls.

| Scenario | Papers | Approx. duration |
|----------|--------|-------------------|
| Quick search | 50 | ~5–10 seconds |
| Medium batch | 500 | ~15–25 seconds |
| Large batch | 1,000 | ~25–45 seconds |
| Maximum run | 10,000 | a few minutes |

Apify platform compute charges apply. The OpenAlex public API is free.

***

### Scoring methodology

Every score is a deterministic function of OpenAlex fields. No LLM, no randomness, the same input returns the same output.

- **`impactScore`** -- field-normalized influence, led by FWCI (field-weighted citation impact, where 1.0 is the field+year average) and OpenAlex's `citation_normalized_percentile`, with a citation-momentum adjustment. When a work is too new to carry an FWCI, a damped raw-citation contribution is used and the record is flagged accordingly.
- **`relevanceScore`** -- query fit: OpenAlex search rank (primacy), primary-topic match, and a small recency lift.
- **`score`** -- the flagship composite, a weighted blend of impact and relevance set by `analysisMode`, with a retraction penalty. `scoreFactors[]` itemizes the contributions.
- **`emergenceScore` / `emergenceClass`** -- citation trajectory from `counts_by_year` (recent half vs prior half, current partial year excluded), gated on recency. `breakout` = young + accelerating + a real citation base.
- **`citationSafe`** -- false when the work is retracted or paratext. **`ragSafe`** -- `citationSafe` AND an open full-text location is present.
- **`citationRisk`** -- `low` / `medium` / `high` tiered from signals actually in the metadata (retraction, paratext, the first-year citation window, single-author-non-journal, missing field-normalized signal despite age). It does **not** assert citation-cartel or self-citation patterns, which would need the citing-and-referenced-works graph this actor doesn't fetch.
- **`topic-profile` growth + maturity + trajectory** -- computed from one `group_by=publication_year` query returning the TRUE field-level per-year volume (not the capped result sample), with the current partial year excluded. `trajectory` reports whether growth itself is accelerating (a deterministic recent-vs-older comparison, never a numeric prediction).
- **`opportunity-signal`** -- a labelled heuristic, never a forecast, and each record carries its own `methodology` + `limitations`. `high_growth_low_competition`: `growthScore = clamp(50 + YoY publication growth %)` (so +50% YoY = 100), `competitionScore = log10(total field works) x 16.7` (so ~1M works = 100), `opportunityScore = growthScore x 0.6 + (100 - competitionScore) x 0.4`. `underfunded_high_impact`: `highImpactShare x 60 + (1 - fundedShare) x 40`. Growth is publication volume (not citations); competition is a volume proxy; funder coverage is incomplete -- the record says so.
- **`institution-comparison` research momentum** -- `avgImpactScore x 0.5 + emergingShare x 30 + highImpactShare x 20`, so it favours the accelerating challenger over the big incumbent. **`market-map`** places each entity by two axes relative to the set's own medians and reports a `volumePercentile` + `impactPercentile` per entity so the quadrant is auditable, not arbitrary; "declining" is omitted from a single run because it needs a time axis (monitor mode).
- **`corpusRisk`** (on the summary) -- a run-level fragility read from field concentration, single-country or few-institution dominance, retraction presence, and low international collaboration. Structural facts about the result set, not a quality judgement on any paper.
- **`scoreConfidence`** -- reflects metadata completeness (citation history, topic assignment, institution graph) and age; capped at `low` inside the first-year citation window. `knownLimitations[]` lists the caveats in plain English.
- **`field-benchmark`** -- benchmarks the corpus against the FWCI baseline of 1.0; the baseline is OpenAlex's own field normalization, not a fabricated cohort average.

### What this actor does not do

- It does **not** claim peer-review quality or make clinical / policy recommendations -- it reports OpenAlex's metadata and deterministic scores over it.
- It does **not** generate any claims with an LLM. Every field is rule-derived and reproducible.
- It does **not** guarantee OpenAlex metadata is complete; sparse or very recent records are flagged with `scoreConfidence` and `knownLimitations`, never silently scored as if complete.
- Very recent papers are often **under-cited** and carry no FWCI yet -- they surface via `emergenceClass`, not as low-impact.
- For bulk BibTeX / RIS from a DOI list, [Crossref Paper Search](https://apify.com/ryanclinton/crossref-paper-search) is the dedicated tool; the exports here are a convenience.
- Funder-ID *lookup* is not supported (the OpenAlex works funder filter is unavailable); funder *intelligence* still ships via the funder-profile and funder landscape.

***

### Research jobs this handles

One actor, one input (your topic or field), many jobs. Each row is a buyer job and the records that answer it:

| Job you're doing | How | What you get |
|------------------|-----|--------------|
| Find landmark / seminal / most-influential papers | `analysisMode: impact-ranking` | papers ranked by field-normalized impact (FWCI), `impactClass: landmark` first |
| Find emerging / rising / breakout papers and trends | `analysisMode: discovery` | `emergenceClass: breakout` papers + `trend-signal` topic growth |
| Find high-growth-low-competition or underfunded-high-impact openings | `includeTopicIntelligence: true` | `opportunity-signal` records with the score's methodology on the record |
| See who's leading a field / top institutions / who's winning | `analysisMode: landscape` + `includeEntityProfiles: true` | `market-map`, institution leaderboard, `institution-comparison` |
| Map a field's funding / top funders / funding landscape | `includeEntityProfiles: true` | `funder-profile` records + the funder landscape in `research-landscape` |
| Build a citation-safe / open-access corpus for RAG | `openAccessOnly: true` | `ragSafe` papers, retraction-excluded, with open full-text URLs |
| Decide what to read, cite, or skip | any run | per-paper `recommendation` (must-read / read / scan / skip) + `whyRead` |
| Monitor a field for what's new, rising, or retracted | `monitorMode: true` | `research-delta` since the last run with the alerts that fired |

The field or topic is your input; the job is the actor's mode. Ready-to-run versions of the most common jobs are in [Ready-to-run examples](#ready-to-run-examples) above.

### FAQ

**Do I need an API key?**
No. The public OpenAlex works endpoint is free and keyless via the polite pool (100k calls/day). Supply an OpenAlex key only if you have a premium plan and want higher limits.

**What is FWCI and why does it matter?**
Field-weighted citation impact normalizes a paper's citations against others in the same field and year. 1.0 is the field average; 10+ is exceptional. It's the fair way to compare a maths paper to a biomedicine paper -- raw citation counts can't.

**How is this different from your other paper-search actors?**
Semantic Scholar, Crossref, PubMed, and arXiv siblings return papers and metadata for their sources. This actor leads with OpenAlex's distinct assets -- field-normalized impact, the institution/author/funder graph, and the concept hierarchy -- and returns scored decisions and a landscape, not raw rows.

**Is the scoring deterministic?**
Yes. Every score, class, and recommendation is computed from OpenAlex fields with fixed rules. No LLM, no randomness -- the same query returns the same output.

**What does monitor mode do?**
With `monitorMode` on, the actor stores a per-watchlist baseline and, on the next run, emits a `research-delta` listing new papers, citation risers, and impact shifts. First run captures the baseline.

**Can I get only the digest?**
Set `outputMode` to `dashboard` to suppress per-paper records and return only the landscape digest (charged as a single event).

**What types of works are covered?**
Journal articles, conference papers, reviews, book chapters, dissertations, preprints, datasets, and more. Filter with `workType`.

**What happens with no results?**
The run completes successfully and emits a `no-results` record. Broaden the query or relax filters.

***

### Use in Dify

Drop this actor into [Dify](https://docs.apify.com/platform/integrations/dify) workflows via the Apify plugin's Run Actor node. Each paper returns scored, classified, and recommended as structured JSON: `impactClass` (landmark / high-impact / influential / solid / emerging / unranked) and `recommendation` (must-read / read / scan / skip / caution) plus the `citationSafe` / `ragSafe` booleans your downstream node branches on. A raw OpenAlex wrapper pointed at the same query returns metadata rows; this returns decisions.

- **Actor ID:** `ryanclinton/openalex-research-search`
- **Sample input** (rank a field by field-normalized impact, open access only, for a RAG corpus):

```json
{
    "searchQuery": "retrieval augmented generation",
    "analysisMode": "impact-ranking",
    "fromPublicationYear": 2022,
    "openAccessOnly": true,
    "minImpactClass": "influential",
    "maxResults": 100
}
```

A Dify if/else node routes on the stable enums without parsing prose, for example:

- `recordType == "research-brief"` → feed `executiveSummary` + `mustRead` straight into the answer
- `recordType == "paper"` AND `ragSafe == true` → index into the vector store
- `impactClass == "landmark"` OR `recommendation == "must-read"` → surface to the user first
- `integrityStatus == "retracted"` → drop from the corpus

Opt-in modes Dify workflows can leverage: `analysisMode` (discovery / impact-ranking / landscape / institutional / monitoring) reweights the ranking; `outputMode: "dashboard"` returns a single landscape digest (one charge) when you only need the field overview; `monitorMode` + `watchlistName` make a scheduled run emit a `research-delta` of new and rising papers. The structured arrays the actor emits (`whyRead`, `keyFindings`, `suggestedQueries`, `mustRead`) are usable verbatim in a downstream node, no LLM rewriting required.

### Related actors

| Actor | Description |
|-------|-------------|
| [PubMed Research Search](https://apify.com/ryanclinton/pubmed-research-search) | Biomedical evidence appraisal and triage via PubMed/NCBI |
| [Semantic Scholar Search](https://apify.com/ryanclinton/semantic-scholar-search) | Research intelligence via Semantic Scholar's knowledge graph |
| [Crossref Paper Search](https://apify.com/ryanclinton/crossref-paper-search) | Scholarly metadata via the Crossref DOI registry |
| [arXiv Paper Search](https://apify.com/ryanclinton/arxiv-paper-search) | Preprints across physics, maths, computer science, and more |

# Changelog

This Actor's version history is a separate document: https://apify.com/ryanclinton/openalex-research-search/changelog.md

# Actor input Schema

## `searchQuery` (type: `string`):

Keyword search across titles, abstracts, and full text. Ignored when Input Mode is 'identifiers'.

## `inputMode` (type: `string`):

Query = keyword search. Identifiers = enrich a list of DOIs / OpenAlex work IDs, or pull an author's (ORCID / OpenAlex A-id) or an institution's (ROR / OpenAlex I-id) works.

## `identifiers` (type: `array`):

Used when Input Mode is 'identifiers'. DOIs, OpenAlex work IDs (W...), ORCIDs, OpenAlex author IDs (A...), RORs, or OpenAlex institution IDs (I...). Funder-ID lookup is not supported.

## `analysisMode` (type: `string`):

Reweights scoring for your job. Discovery favours query-fit; Impact ranking favours field-normalized impact; Landscape balances both; Institutional emphasises the affiliation graph; Monitoring is for scheduled re-runs.

## `maxResults` (type: `integer`):

Maximum number of works to analyse (1-10000)

## `minImpactClass` (type: `string`):

Keep only works at or above this field-normalized impact tier. Excluded works are counted and disclosed in the summary, never silently dropped.

## `excludeRetracted` (type: `boolean`):

Drop works flagged as retracted (counted in the summary)

## `openAccessOnly` (type: `boolean`):

Only include works that are freely available (open access)

## `publicationYear` (type: `integer`):

Filter to a single publication year (e.g. 2024). Leave blank to use a range or no year filter.

## `fromPublicationYear` (type: `integer`):

Start of a publication-year range (inclusive)

## `toPublicationYear` (type: `integer`):

End of a publication-year range (inclusive)

## `minCitations` (type: `integer`):

Only include works with at least this many citations

## `workType` (type: `string`):

Filter to one OpenAlex work type (e.g. article, review, preprint, book-chapter, dataset). Leave blank for all types.

## `country` (type: `string`):

Filter to works with at least one author affiliated in this 2-letter country code (e.g. US, GB, CN, DE)

## `sortBy` (type: `string`):

How to order the works fetched from OpenAlex

## `outputProfile` (type: `string`):

How much of each record to return. Minimal = the decision fields; Standard = decision + graph + concepts; Full = everything including score factors.

## `outputMode` (type: `string`):

Papers returns one record per work plus the landscape and brief. Dashboard suppresses per-paper records and returns only the landscape digest (and is charged as a single event).

## `emitResearchBrief` (type: `boolean`):

Include the deterministic research-brief hero record (must-read papers, reading order, suggested next queries)

## `includeEntityProfiles` (type: `boolean`):

Emit institution / funder / author / source profile records + a collaboration-network insight describing each entity's footprint within this result set (no extra API calls)

## `compareInstitutions` (type: `array`):

Competitive mode: institution names (e.g. "MIT", "Stanford"), RORs, or OpenAlex institution IDs. Returns a head-to-head comparison ranked by research momentum, optionally scoped to the search query. Replaces the normal corpus output.

## `includeTopicIntelligence` (type: `boolean`):

Query mode only. Emit a topic-profile (maturity stage, growth, velocity, leading countries/institutions/funders, citation concentration, adjacent topics) plus trend-signal and opportunity-signal records. Costs one extra group_by query for true field-level growth.

## `includeGraphExport` (type: `boolean`):

Write GRAPH_NODES and GRAPH_EDGES to the key-value store (paper / institution / author / topic / funder graph for Gephi, Neo4j, Cytoscape)

## `includeCitationExports` (type: `boolean`):

Write BIBTEX_EXPORT and RIS_EXPORT to the key-value store. For bulk BibTeX from a DOI list, Crossref Paper Search is the dedicated tool.

## `monitorMode` (type: `boolean`):

Track this query across scheduled runs. Stores a per-watchlist baseline and flags new papers, citation risers, and impact shifts on the next run.

## `watchlistName` (type: `string`):

Name for the monitored baseline (only used when Monitor Mode is on). Use distinct names to track several research areas independently.

## `monitorSensitivity` (type: `string`):

How readily monitor mode raises alerts. Low = high-impact / landmark / retraction only; High = every change.

## `alertOn` (type: `array`):

Restrict monitor alerts to these event types (empty = all). Values: new_work, new_high_impact, new_landmark, citation_breakout, retraction.

## `apiKey` (type: `string`):

Optional. The public OpenAlex works endpoint is free and keyless via the polite pool (100k calls/day). Supply a key only if you have a premium OpenAlex plan and want its higher limits.

## Actor input object example

```json
{
  "searchQuery": "CRISPR gene editing",
  "inputMode": "query",
  "identifiers": [
    "https://doi.org/10.1038/s41586-021-03819-2"
  ],
  "analysisMode": "discovery",
  "maxResults": 50,
  "minImpactClass": "any",
  "excludeRetracted": false,
  "openAccessOnly": false,
  "outputProfile": "standard",
  "outputMode": "papers",
  "emitResearchBrief": true,
  "includeEntityProfiles": false,
  "includeTopicIntelligence": false,
  "includeGraphExport": false,
  "includeCitationExports": false,
  "monitorMode": false,
  "watchlistName": "default",
  "monitorSensitivity": "medium"
}
```

# Actor output Schema

## `decisions` (type: `string`):

Quick read/skip recommendations with impact class and integrity status.

## `papers` (type: `string`):

All retrieved papers with full scoring, authorship, and open-access fields.

## `rising` (type: `string`):

Papers ranked by emergence score — fast-breaking works not yet reflected in raw citation counts.

## `landmarks` (type: `string`):

Landmark and high-impact papers by FWCI and citation percentile.

## `ragSafe` (type: `string`):

Papers that are citation-safe and have an open full-text URL — safe to feed into a RAG pipeline.

## `institutions` (type: `string`):

Institutional leaderboard by research momentum and high-impact share.

## `funders` (type: `string`):

Funders ranked by works funded and summed impact.

## `allItems` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchQuery": "CRISPR gene editing",
    "identifiers": [
        "https://doi.org/10.1038/s41586-021-03819-2"
    ],
    "maxResults": 50
};

// Run the Actor and wait for it to finish
const run = await client.actor("ryanclinton/openalex-research-search").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "searchQuery": "CRISPR gene editing",
    "identifiers": ["https://doi.org/10.1038/s41586-021-03819-2"],
    "maxResults": 50,
}

# Run the Actor and wait for it to finish
run = client.actor("ryanclinton/openalex-research-search").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchQuery": "CRISPR gene editing",
  "identifiers": [
    "https://doi.org/10.1038/s41586-021-03819-2"
  ],
  "maxResults": 50
}' |
apify call ryanclinton/openalex-research-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ryanclinton/openalex-research-search"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/kbV7IqCW7tszfXB96/builds/yZ6VDz0XtgRU6ghZi/openapi.json