# Google Scholar Author Alternative: Find Experts & KOLs by Topic (`pandadad0214/researcher-expert-finder`) Actor

Use this to find the academic experts and key opinion leaders on a research topic (Google Scholar author search alternative). Input: a topic plus country, institution and year filters. Output: ranked experts with ORCID, affiliation, h-index and topic papers.

- **URL**: https://apify.com/pandadad0214/researcher-expert-finder.md
- **Developed by:** [Panda Federation](https://apify.com/pandadad0214) (community)
- **Categories:** Lead generation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## Google Scholar Author Alternative: Find Experts & KOLs by Topic

> **Google Scholar author scraper alternative** on Apify: type a research topic and get a ranked list of the academic experts and key opinion leaders who publish on it, with ORCID, affiliation and top papers from OpenAlex. Apify's free plan includes $5 of monthly platform credit, enough for roughly 2,000 experts at $0.0025 each (pricing below).

**Type a research topic and get a ranked list of the people who actually publish on it: name, ORCID, OpenAlex ID, current institution and country, works, citations, h-index, top topics, their newest matching papers with DOIs, and profile links. Download it as Excel or CSV.**

Finding the right expert usually means hours of searching papers, guessing who matters and copying names into a spreadsheet. **Researcher & Expert Finder** does it in one run using only **free, open scholarly data**: the **OpenAlex** catalogue of 250M+ works and authors (CC0) and the **ORCID public API**. No Google Scholar scraping, no blocked IPs, no CAPTCHAs.

- 🎯 **Ranked by real topic output:** experts are ranked by how many papers they have on *your* topic, how cited those papers are, and their h-index, not just by fame
- 🌍 **Filters that matter:** country, institution (including its schools, labs and hospitals), publication years, "active since", minimum matching papers, total works, citations and h-index
- 🧾 **Evidence included:** each expert's newest matching papers (title, year, DOI, citations) and their most-cited paper on the topic, so you can see *why* they're on the list
- 🪪 **ORCID details:** current employment and role, keywords, websites and a short biography from the public ORCID record
- 🧪 **No collaboration noise:** papers with hundreds of authors (big physics or genomics consortia) are left out by default so they don't flood the ranking
- 📊 **Excel + CSV export:** formatted workbook with clickable profile links, a Papers sheet, a search log and a read-me
- 💸 **$0.0025 per expert.** 50 experts cost $0.125. Searches with no results cost nothing

### Who it's for

- **Conference and event organisers:** build speaker and reviewer shortlists for a session topic in minutes
- **Journal editors and program officers:** find qualified peer reviewers outside the usual circle, filtered by country or institution
- **Pharma, medtech and biotech teams:** map key opinion leaders (KOLs) and academic collaborators in a therapeutic area
- **Journalists and podcasters:** find credible academic sources on a topic, with their latest work to read first
- **Recruiters, tech-transfer and BD teams:** spot active researchers at a target university or in a target country
- **Researchers:** find collaborators, or see who leads a field you're moving into

> **Not for coursework.** Researcher & Expert Finder maps who publishes on a topic from public bibliographic records. It doesn't write anything for you and isn't meant for completing assignments. Treat it as a starting point and check people's profiles before contacting them.

### How to use the Google Scholar author scraper alternative

1. Click **Try for free** and type a **research topic** (use quotes for exact phrases, e.g. `"antimicrobial resistance" wastewater`).
2. Optional: add **countries** (2-letter codes like `US`, `GB`, `DE`), an **institution**, a **year range** or minimum impact numbers.
3. Set **Maximum experts**. You pay per expert returned, so this also caps the cost.
4. Click **Start**. A 25-expert run usually takes 10-30 seconds. Download **EXPERTS_XLSX** or **EXPERTS_CSV** from **Storage → Key-value store**, or use the dataset via API.

#### Input example

```json
{
  "topic": "\"antimicrobial resistance\" wastewater",
  "institution": "University of Oxford",
  "yearFrom": 2018,
  "activeSince": 2024,
  "minHIndex": 10,
  "maxResults": 25
}
```

### Output example

![Example output table from a real researcher-expert-finder run](https://api.apify.com/v2/key-value-stores/0eeXxTD8eJjgE6yTA/records/shot-researcher-expert-finder)

One row per expert. A real row from a run for `perovskite solar cells` with `countries: ["US"]` (top 10: Kanatzidis, Yan, Huang, Zhu, Jen, McGehee, Yang, Berry, Luther, Sargent; lists shortened):

```json
{
  "rank": 2,
  "name": "Yanfa Yan",
  "expertScore": 96.7,
  "orcid": "0000-0003-3977-5789",
  "openAlexId": "A5088860714",
  "institution": "University of Toledo",
  "country": "US",
  "institutionType": "education",
  "topicWorks": 209,
  "topicCitations": 24603,
  "mostCitedPaper": {
    "title": "Unusual defect physics in CH3NH3PbI3 perovskite solar cell absorber",
    "year": 2014,
    "doi": "10.1063/1.4864778",
    "citations": 2713,
    "url": "https://doi.org/10.1063/1.4864778"
  },
  "worksCount": 903,
  "citations": 69463,
  "hIndex": 133,
  "lastActiveYear": 2026,
  "topTopics": ["Perovskite Materials and Applications", "Chalcogenide Semiconductor Thin Films", "Organic Electronics and Photovoltaics"],
  "recentPapers": [
    {
      "title": "Tuning of Aromatic Amine Polymer Side chains for Application as Hole Transport Materials in Wide-Bandgap Perovskite Solar Cells",
      "year": 2026,
      "doi": "10.1021/acsapm.6c02188",
      "citations": 0,
      "url": "https://doi.org/10.1021/acsapm.6c02188"
    }
  ],
  "orcidCurrentEmployment": ["Professor, Physics and Astronomy, University of Toledo"],
  "orcidKeywords": null,
  "websites": null,
  "openAlexUrl": "https://openalex.org/authors/A5088860714",
  "orcidUrl": "https://orcid.org/0000-0003-3977-5789"
}
```

#### Fields

| Field | What it is |
|---|---|
| `rank`, `expertScore` | Position and score (0-100, see below) |
| `name`, `orcid`, `openAlexId` | Name and identifiers |
| `institution`, `country`, `institutionType`, `institutionRor`, `otherCurrentInstitutions` | Last known institution(s) in OpenAlex. With a country or institution filter, the matching one is shown first |
| `topicWorks` | Papers by this person whose title or abstract matches your topic (and your year / country / institution filters) |
| `topicCitations`, `mostCitedPaper` | Citations of their papers among the most-cited matching papers (up to 400) on your topic, and their single most-cited one |
| `worksCount`, `citations`, `hIndex`, `i10Index`, `twoYearMeanCitedness` | Whole-profile numbers from OpenAlex |
| `lastActiveYear` | Latest year with a publication |
| `topTopics`, `topFields` | Their main research topics and fields across all their work |
| `recentPapers` | Their newest matching papers: title, year, DOI, citations, link |
| `orcidCurrentEmployment`, `orcidKeywords`, `websites`, `orcidBiography` | From the public ORCID record (when they have one and fill it in) |
| `openAlexUrl`, `orcidUrl`, `openAlexApiUrl` | Profile links |

#### How the expert score works

`expertScore` = **50% matching papers** + **30% citations of their top matching papers** + **20% h-index**, each on a log scale relative to the best candidate in the same run. So a specialist with many papers on your exact topic outranks a famous generalist who wrote one. Prefer a single number? Set **Sort** to matching papers, citations or h-index.

#### Files in the key-value store

| Key | What it is |
|---|---|
| `EXPERTS_XLSX` | Excel workbook: ranked experts with clickable links, a **Papers** sheet (one row per recent paper), a **Search log** and a **Read me** |
| `EXPERTS_CSV` | Same table as CSV (UTF-8, opens cleanly in Excel) |
| `SUMMARY` | JSON search log: your filters, the institution matched, matching works, candidates, how many each filter removed |

### For AI agents

Use this Actor to find and rank the academic experts and key opinion leaders on a research topic. Call it with Apify's MCP server (`https://mcp.apify.com`, tool `call-actor`, Actor `pandadad0214/researcher-expert-finder`) or the API (`POST https://api.apify.com/v2/acts/pandadad0214~researcher-expert-finder/run-sync-get-dataset-items`).

Minimal input:

```json
{"topic": "perovskite solar cells", "countries": ["US"], "maxResults": 10}
```

Each dataset item includes:

- `summary`: one line, e.g. `#1 Jane Doe (expert score 92.1); MIT, US; 41 topic papers, 9,800 topic citations, h-index 55; ORCID ...`
- `rank`, `expertScore`: ranking
- `name`, `institution`, `country`: who and where
- `topicWorks`, `topicCitations`: papers and citations on your topic
- `hIndex`, `worksCount`, `citations`: career totals
- `orcid`, `openAlexUrl`: profiles

The run's key-value store has a `SUMMARY` record with counts (`returned`, `candidates`, `withOrcid`) and file names (`EXPERTS_XLSX`, `EXPERTS_CSV`), a one-line `takeaway`, and `nextStep` (the suggested follow-on tool). Every input field has a description with formats, units and limits.

### Google Scholar author scraper alternative pricing and free results

**Apify paid-plan discounts:** Bronze members pay 10% less, Silver 20% less and Gold 30% less per result. Free-plan prices are shown below; the discount applies automatically.

**$0.0025 per expert** returned to the dataset (pay-per-event), plus a tiny Actor-start fee of $0.00005. ORCID details, papers, Excel, CSV and the search log are included. Runs that find nothing cost nothing beyond the start fee. Your **maximum charge per run** is respected.

### FAQ

**Is there a free way to try this Google Scholar author scraper alternative?** Yes. Apify's free plan includes $5 of monthly platform credit, enough for roughly 2,000 experts at $0.0025 each.

**Where does the data come from?** OpenAlex (open, CC0 catalogue of scholarly works, authors and institutions) and the public ORCID registry. Both are free and built to be reused. Not affiliated with or endorsed by OpenAlex or ORCID.

**Does it use Google Scholar or Semantic Scholar?** No. Only open data with clear reuse terms, so results are stable and reproducible.

**Why do citation counts differ from Google Scholar?** OpenAlex counts citations from scholarly works only, so numbers are usually a bit lower, but consistent.

**Does it give email addresses?** No. It gives public profile links (ORCID, OpenAlex, personal websites listed on ORCID) so you can find the right contact route yourself. Please respect people's time and local privacy laws when reaching out.

**Is author matching perfect?** No. OpenAlex groups papers into author profiles automatically and sometimes splits one person into two profiles or mixes two people with the same name. Check the profile links before you rely on a result.

**How many experts can I get?** Up to 200 per run: the ranked pool is the 200 authors with the most matching papers (after your filters). Narrow the topic or add filters to go deeper into a sub-field.

**Can I use my own OpenAlex key?** Yes, though you don't need one. A free key from openalex.org gives your runs their own daily OpenAlex budget for very large or frequent searches. It's stored as a secret input and never logged.

**Can AI agents call it?** Yes, through the Apify API, the Python or JavaScript clients, or Apify's MCP server.

### More Panda Federation tools

- [Reference Checker](https://apify.com/pandadad0214/reference-checker)
- [Citation Generator](https://apify.com/pandadad0214/citation-formatter)
- [Google Scholar Scraper Alternative](https://apify.com/pandadad0214/literature-search)
- [PubMed Scraper](https://apify.com/pandadad0214/pubmed-evidence-table)
- [Grants.gov Scraper](https://apify.com/pandadad0214/grants-finder)
- [Academic Research MCP Server](https://apify.com/pandadad0214/panda-research-mcp)

[All Panda Federation Actors on Apify](https://apify.com/pandadad0214)

Questions or a custom data feed? Open an issue on this Actor's **Issues** tab.

### Changelog

- **2026-10-30 (agent update):** Built for AI agents: a one-line `summary` on every result, `takeaway` and `nextStep` in the SUMMARY record, and input fields with formats, limits and examples.
- **2026-10-30:** New search-friendly title and page, FAQ, safe demo run on empty input, Bronze/Silver/Gold discounts, output screenshot, and a next-step hint in the log and `NEXT_STEP` record pointing to Literature Search.
- **2026-10-29:** First public release with pay-per-event pricing.

# Actor input Schema

## `topic` (type: `string`):

What the experts work on, e.g. perovskite solar cells, antimicrobial resistance, large language models in healthcare. Matched against paper titles and abstracts. Use quotes for exact phrases ("machine learning") and AND / OR / NOT for Boolean searches.

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

You pay per expert returned, so this also caps the cost. Up to 200 (the ranked pool is the 200 authors with the most matching papers). Whole number, 1 to 200 results; default 25.

## `countries` (type: `array`):

Optional 2-letter ISO country codes (US, GB, DE, IN, AU…). Keeps experts whose current institution is in one of these countries, and counts only papers with an author in them.

## `institution` (type: `string`):

Optional. University, hospital, lab or company name (e.g. Stanford University), a ROR ID or an OpenAlex institution ID (I…). Sub-units count too: a university includes its schools and hospitals. The matched institution is shown in the search log.

## `yearFrom` (type: `integer`):

Optional. Only count matching papers published in or after this year, e.g. 2020 for people active in the field recently. Whole number, 1900 or more.

## `yearTo` (type: `integer`):

Optional. Only count matching papers published up to this year. Whole number, 1900 or more.

## `activeSince` (type: `integer`):

Optional. Only keep people who published anything in or after this year (uses OpenAlex yearly counts for the last ~10 years). Whole number, 1900 or more.

## `minTopicWorks` (type: `integer`):

Each expert must have at least this many papers on your topic. Whole number, 1 or more papers; default 2.

## `minWorks` (type: `integer`):

Optional. Total works on the person's OpenAlex profile. Whole number, 0 or more works.

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

Optional. Whole number, 0 or more citations.

## `minHIndex` (type: `integer`):

Optional. Whole number, 0 or more.

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

Expert score (default) blends matching papers, their citations and h-index. Or sort by a single number.

## `papersPerExpert` (type: `integer`):

How many of each expert's newest matching papers to list (title, year, DOI, citations). 0 to skip. Whole number, 0 to 10 papers; default 3.

## `includeOrcid` (type: `boolean`):

For experts with an ORCID iD, add current employment and role, keywords, websites and a short biography from the public ORCID record. Default: on.

## `maxAuthorsPerPaper` (type: `integer`):

Large collaboration papers (hundreds of authors) would make everyone on them look like an expert. Papers with more authors than this are not counted. Whole number, 1 or more authors; default 50.

## `openAlexApiKey` (type: `string`):

Optional. A free key from openalex.org gives your runs their own daily OpenAlex budget for very large or frequent searches. Stored encrypted; never logged.

## Actor input object example

```json
{
  "topic": "perovskite solar cells",
  "maxResults": 25,
  "countries": [
    "US",
    "GB"
  ],
  "institution": "Stanford University",
  "yearFrom": 2020,
  "yearTo": 2025,
  "activeSince": 2023,
  "minTopicWorks": 2,
  "minWorks": 20,
  "minCitations": 500,
  "minHIndex": 10,
  "sortBy": "expertScore",
  "papersPerExpert": 3,
  "includeOrcid": true,
  "maxAuthorsPerPaper": 50
}
```

# Actor output Schema

## `table` (type: `string`):

No description

## `xlsx` (type: `string`):

No description

## `csv` (type: `string`):

No description

## `summary` (type: `string`):

Run-level summary record. Agent-friendly keys: `takeaway` (one-line plain-English result of the run), `nextStep` and `nextSteps` (the suggested follow-on tool and its Store URL).

# 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 = {
    "topic": "perovskite solar cells"
};

// Run the Actor and wait for it to finish
const run = await client.actor("pandadad0214/researcher-expert-finder").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 = { "topic": "perovskite solar cells" }

# Run the Actor and wait for it to finish
run = client.actor("pandadad0214/researcher-expert-finder").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 '{
  "topic": "perovskite solar cells"
}' |
apify call pandadad0214/researcher-expert-finder --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,pandadad0214/researcher-expert-finder"
        }
    }
}
```

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/75zQKu9fJIWdDfgPV/builds/x2vgkw624rg6vBzV2/openapi.json