# Google Scholar \[Just 💰$0.90] — Author Citations & Profiles (`blackfalcondata/google-scholar-scraper`) Actor

💰 $0.90 per 1,000 author profiles — the cheapest paid Scholar scraper. Scrape citation counts, h-index & i10-index; research interests, publications & verified email domain; track new & updated authors across runs. Structured JSON export.

- **URL**: https://apify.com/blackfalcondata/google-scholar-scraper.md
- **Developed by:** [Black Falcon Data](https://apify.com/blackfalcondata) (community)
- **Categories:** Other, Lead generation, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.90 / 1,000 results

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/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):

```bash
npm install apify-client
```

In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):

```bash
pip install apify-client
```

In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):

````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash

In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).

If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).


# README

### What does Google Scholar do?

Google Scholar extracts structured profile data from [scholar.google.com](https://scholar.google.com) — including contact details. It supports keyword search and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions and contact information) where the source provides them.

### How to use this actor

- 👉 **Register for a free Apify account** — no credit card required.
- 🎉 Just click **[Sign up free on Apify →](https://console.apify.com/sign-up?fpr=1h3gvi&fp_sid=ctarich)** and complete a quick signup.
- 💰 A free Apify account includes $5 in monthly credits — enough to test this actor.
- ⏳ Scrape during the free trial, with no commitment or upfront payment required.

### Key features

<!-- KEY_FEATURES:START -->
- **📋 Detail enrichment** — toggle two-stage scraping: first collect listings, then enrich each with full description + detail-page-only fields. Off by default to keep runs fast; flip on when you need the deep payload.
- **📌 Change classification** — each record carries a `changeType` of NEW / UPDATED / UNCHANGED / REAPPEARED / EXPIRED. Default emits NEW + UPDATED + REAPPEARED; opt into the others with `emitUnchanged` / `emitExpired`. Repost detection flags previously-expired listings that come back.
- **📦 Compact mode** — compact mode — core fields only, ideal when you're feeding an LLM or building a comparison sheet across many companies.
- **🧹 Empty-field stripping** — drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards that already handle missing fields gracefully.
- **🎯 Batch searches** — pass `["term1", "term2"]` for query, location, or city to batch multiple searches in one run — shared dedup state, single dataset, one Actor-Start charge instead of N.
- **♻️ Incremental mode** — recurring runs emit only listings whose ratings, reviews, or metadata changed — track reputation movement over time without re-processing the universe. Saves 80–95% on monitoring runs.
- **📤 Export anywhere** — Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).
- **🔌 MCP connectors** — export your results into Notion via Apify's MCP connectors — a clean run-summary page, no glue code. Opt-in via the App connector field; deterministic field-mapping, no AI. Built on Apify's connector framework, so more destinations open up as their catalog grows.
<!-- KEY_FEATURES:END -->

### What data can you extract from scholar.google.com?

Each result includes Core profile fields (`profileUrl`, `userId`, `name`, `affiliation`, `profileImageUrl`, `interests`, `citations`, and `metricsSinceYear`, and more), detail fields when enrichment is enabled (`detailFetched`), and contact information (`verifiedEmailDomain`). In standard mode, all fields are always present — unavailable data points are returned as `null`, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to get richer fields such as full descriptions and contact information where the source provides them.

### Input

The main inputs are a search keyword and a result limit. Additional filters and options are available in the input schema.

Key parameters:

- **`query`** — Free-text research topic or keywords (e.g. "deep learning"). Profiled authors who publish on the topic are discovered from the article results. Do NOT enter an author's name — author-name search is not publicly available. Use a JSON array for multiple topics.
- **`maxResults`** — Maximum total author profiles (0 = unlimited). (default: `25`)
- **`includeDetails`** — Fetch each author's full profile (metrics, publications, co-authors). Turn off for a faster, lighter run that returns discovery-level fields only. (default: `true`)
- **`compact`** — Core fields only (for AI-agent/MCP workflows). (default: `false`)
- **`excludeEmptyFields`** — Drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards. (default: `false`)
- **`incrementalMode`** — Compare against previous run state. stateKey is optional — defaults to a stable key derived from your search inputs so different searches never share state. (default: `false`)
- **`stateKey`** — Optional. Stable identifier for the tracked search universe. Leave empty to auto-generate from search inputs.
- **`skipReposts`** — When incremental, skip records whose content matches an expired record from a prior run (cross-run duplicate detection). (default: `false`)
- **`emitUnchanged`** — When incremental mode is on, also emit records whose content has not changed since the last run. (default: `false`)
- **`emitExpired`** — When incremental mode is on, also emit records that were seen before but are no longer found. (default: `false`)
- **`telegramToken`** — Telegram bot token (from @BotFather). Required for Telegram notifications.
- **`telegramChatId`** — Telegram chat or channel ID (e.g. "-100123456789"). Required when telegramToken is set.
- ...and 13 more parameters

### Input examples

**Basic search** — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

```json
{
  "query": "deep learning",
  "maxResults": 50
}
````

**Incremental tracking** — Only emit profiles that changed since the previous run with this `stateKey`.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set `emitUnchanged: true` to include unchanged records as well.

```json
{
  "query": "deep learning",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "deep-learning-tracker"
}
```

**Compact output for AI agents** — Return only core fields for AI-agent and MCP workflows.

→ Small payload with the most important fields — ideal for piping into LLMs without token overhead.

```json
{
  "query": "deep learning",
  "maxResults": 50,
  "compact": true
}
```

### Output

Each run produces a dataset of structured profile records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

### Example profile record

```json
{
  "profileUrl": "https://scholar.google.com/citations?user=iYN86KEAAAAJ&hl=en",
  "userId": "iYN86KEAAAAJ",
  "name": "Ian Goodfellow",
  "affiliation": "DeepMind",
  "profileImageUrl": "https://scholar.googleusercontent.com/citations?view_op=view_photo&user=iYN86KEAAAAJ&citpid=4",
  "interests": [
    "Deep Learning",
    "Artificial Intelligence"
  ],
  "citations": 440394,
  "metricsSinceYear": 2021,
  "citationsSinceYear": 319706,
  "hIndex": 104,
  "hIndexSinceYear": 89,
  "i10Index": 200,
  "i10IndexSinceYear": 190,
  "citationsSince2019": 319706,
  "hIndexSince2019": 89,
  "i10IndexSince2019": 190,
  "citationHistory": [
    {
      "year": 2015,
      "citations": 1247
    },
    {
      "year": 2016,
      "citations": 3451
    },
    {
      "year": 2017,
      "citations": 10140
    },
    {
      "year": 2018,
      "citations": 22923
    },
    {
      "year": 2019,
      "citations": 34140
    },
    "... 7 more items"
  ],
  "publications": [
    {
      "publicationId": "iYN86KEAAAAJ:kNdYIx-mwKoC",
      "title": "Generative adversarial networks",
      "authors": "I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, ...",
      "venue": "Advances in neural information processing systems 27",
      "year": 2014,
      "citedByCount": 120428,
      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=iYN86KEAAAAJ:kNdYIx-mwKoC",
      "citedByUrl": "https://scholar.google.com/scholar?oi=bibs&hl=en&cites=11977070277539609369,7652588910447996374,10913732071597931018,16890861223062024876,12025250469173508241,8618380841735941249,15299328419842898268,..."
    },
    {
      "publicationId": "iYN86KEAAAAJ:ZeXyd9-uunAC",
      "title": "Deep learning",
      "authors": "I Goodfellow",
      "venue": "MIT press",
      "year": 2016,
      "citedByCount": 102827,
      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=iYN86KEAAAAJ:ZeXyd9-uunAC",
      "citedByUrl": "https://scholar.google.com/scholar?oi=bibs&hl=en&cites=16766804411681372720,2705125642663662569,16538979328513850542,15784429166264333912"
    },
    {
      "publicationId": "iYN86KEAAAAJ:KxtntwgDAa4C",
      "title": "Explaining and Harnessing Adversarial Examples",
      "authors": "I Goodfellow, J Shlens, C Szegedy",
      "venue": "ICLR",
      "year": 2014,
      "citedByCount": 31410,
      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=iYN86KEAAAAJ:KxtntwgDAa4C",
      "citedByUrl": "https://scholar.google.com/scholar?oi=bibs&hl=en&cites=14908107896544813002,6802210412227455718"
    },
    {
      "publicationId": "iYN86KEAAAAJ:1taIhTC69MYC",
      "title": "TensorFlow: Large-scale machine learning on heterogeneous systems",
      "authors": "M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ...",
      "year": 2015,
      "citedByCount": 23576,
      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=iYN86KEAAAAJ:1taIhTC69MYC",
      "citedByUrl": "https://scholar.google.com/scholar?oi=bibs&hl=en&cites=6781733040830078545"
    },
    {
      "publicationId": "iYN86KEAAAAJ:MXK_kJrjxJIC",
      "title": "Intriguing properties of neural networks",
      "authors": "C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ...",
      "venue": "arXiv preprint arXiv:1312.6199",
      "year": 2013,
      "citedByCount": 22780,
      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=iYN86KEAAAAJ:MXK_kJrjxJIC",
      "citedByUrl": "https://scholar.google.com/scholar?oi=bibs&hl=en&cites=2835128024326609853,14134151322722010398"
    },
    "... 245 more items"
  ],
  "publicationCount": 250,
  "scrapedAt": "2026-07-26T10:41:22.898Z",
  "verifiedEmailDomain": "deepmind.com",
  "homepageUrl": "http://www.iangoodfellow.com/",
  "coauthors": [
    {
      "userId": "kukA0LcAAAAJ",
      "name": "Yoshua Bengio",
      "affiliation": "Professor of computer science, University of Montreal, Mila, IVADO, CIFAR",
      "profileUrl": "https://scholar.google.com/citations?user=kukA0LcAAAAJ&hl=en"
    },
    {
      "userId": "km6CP8cAAAAJ",
      "name": "Aaron Courville",
      "affiliation": "Full Professor, DIRO, Université de Montréal, Mila, Cifar CAI chair",
      "profileUrl": "https://scholar.google.com/citations?user=km6CP8cAAAAJ&hl=en"
    },
    {
      "userId": "c646VbAAAAAJ",
      "name": "Mehdi Mirza",
      "affiliation": "DeepMind",
      "profileUrl": "https://scholar.google.com/citations?user=c646VbAAAAAJ&hl=en"
    },
    {
      "userId": "MOgfm8oAAAAJ",
      "name": "David Warde-Farley",
      "affiliation": "Staff Research Scientist at Google DeepMind",
      "profileUrl": "https://scholar.google.com/citations?user=MOgfm8oAAAAJ&hl=en"
    },
    {
      "userId": "nHh9PSsAAAAJ",
      "name": "Bing Xu",
      "affiliation": "INT21 AI",
      "profileUrl": "https://scholar.google.com/citations?user=nHh9PSsAAAAJ&hl=en"
    },
    "... 15 more items"
  ],
  "portalUrl": "https://scholar.google.com/citations?user=iYN86KEAAAAJ&hl=en",
  "listingId": "7bb235a836255da33c73429eadbdbdb155deb2feff9bb7057f8db845ba4cd226",
  "contentHash": "d0e27e1a6af87ac8f46ec83a2dd6daa42af17477f8f5a7c5fbab8e768855facc",
  "searchQuery": "deep learning",
  "contentQuality": "full",
  "detailFetched": true,
  "source": "scholar.google.com"
}
```

### Incremental fields

When incremental mode is on, each record also carries:

- `changeType` — one of `NEW`, `UPDATED`, `UNCHANGED`, `REAPPEARED`, `EXPIRED`. Default output covers `NEW` / `UPDATED` / `REAPPEARED`; set `emitUnchanged: true` or `emitExpired: true` to opt into the others.
- `isRepost`, `repostOfId`, `repostDetectedAt` — populated when a new listing matches the tracked content of a previously expired one. Set `skipReposts: true` to drop detected reposts from the output.

### How to scrape scholar.google.com

1. Go to [Google Scholar](https://apify.com/blackfalcondata/google-scholar-scraper?fpr=1h3gvi) in Apify Console.
2. Enter a search keyword.
3. Set `maxResults` to control how many results you need.
4. Enable `includeDetails` if you need full descriptions, contact info.
5. Click **Start** and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

### Use cases

- Extract profile data from scholar.google.com for market research and competitive analysis.
- Monitor new and changed profiles on scheduled runs without processing the full dataset every time.
- Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
- Export clean, structured data to dashboards, spreadsheets, or data warehouses.

### How much does it cost to scrape scholar.google.com?

Google Scholar uses [pay-per-event](https://docs.apify.com/platform/actors/paid-actors/pay-per-event) pricing. You pay a small fee when the run starts and then for each result that is actually produced.

- **Run start:** $0.005 per run
- **Per result:** $0.0009 per profile record

Example costs:

- 10 results: **$0.014**
- 25 results: **$0.028**
- 100 results: **$0.095**
- 200 results: **$0.18**
- 500 results: **$0.46**

#### Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of profiles that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 100 results per run, daily polling (30 runs/month). Event-pricing examples scale linearly with result count.

| Churn rate | Full re-scrape run cost | Incremental run cost | Savings vs full re-scrape | Monthly cost after baseline |
|---|---:|---:|---:|---:|
| 5% — stable niche query | $0.10 | $0.0095 | $0.09 (90%) | $0.28 |
| 15% — moderate broad query | $0.10 | $0.02 | $0.08 (81%) | $0.55 |
| 30% — high-volume aggregator | $0.10 | $0.03 | $0.06 (66%) | $0.96 |

Full re-scrape monthly cost at daily polling: $2.85. First month with incremental costs $0.37 / $0.63 / $1.02 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage is included in the per-result fee shown above.

### FAQ

#### How many results can I get from scholar.google.com?

The number of results depends on the search query and available profiles on scholar.google.com. Use the `maxResults` parameter to control how many results are returned per run.

#### Does Google Scholar support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed profiles on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

#### Can I integrate Google Scholar with other apps?

Yes. Google Scholar works with Apify's [integrations](https://apify.com/integrations?fpr=1h3gvi) to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

#### Can I use Google Scholar with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the [Apify API](https://docs.apify.com/api/v2). Client libraries are available for JavaScript, Python, and other languages.

#### Can I use Google Scholar through an MCP Server?

Yes. Apify provides an [MCP Server](https://apify.com/apify/actors-mcp-server?fpr=1h3gvi) that lets AI assistants and agents call this actor directly. Use compact mode, a single `descriptionFormat`, and `excludeEmptyFields` to keep payloads manageable for LLM context windows.

#### Is it legal to scrape scholar.google.com?

This actor extracts publicly available data from scholar.google.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

#### Your feedback

If you have questions, need a feature, or found a bug, please [open an issue](https://apify.com/blackfalcondata/google-scholar-scraper/issues?fpr=1h3gvi) on the actor's page in Apify Console. Your feedback helps us improve.

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### Getting started with Apify

New to Apify? [Create a free account with $5 credit](https://console.apify.com/sign-up?fpr=1h3gvi\&fp_sid=ctarich) — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click **Start** — export results as JSON, CSV, or Excel

Need more later? [See Apify pricing](https://apify.com/pricing?fpr=1h3gvi).

### Disclaimer

This actor accesses only publicly available data on scholar.google.com. You are responsible for how you use the extracted data — in particular any personal information such as names, phone numbers, or email addresses — and for complying with Google Scholar's terms of use, applicable data-protection law (including the GDPR where it applies), and the anti-spam rules of your jurisdiction.

This actor is not affiliated with, endorsed by, or connected to Google Scholar.

### Search keywords

google scholar scraper, google scholar api, apify google scholar, google scholar data extraction, scholar.google.com scraper, scholar.google.com data, scholar.google.com api.

# Actor input Schema

## `query` (type: `string`):

Free-text research topic or keywords (e.g. "deep learning"). Profiled authors who publish on the topic are discovered from the article results. Do NOT enter an author's name — author-name search is not publicly available. Use a JSON array for multiple topics.

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

Maximum total author profiles (0 = unlimited).

## `includeDetails` (type: `boolean`):

Fetch each author's full profile (metrics, publications, co-authors). Turn off for a faster, lighter run that returns discovery-level fields only.

## `proxyConfiguration` (type: `object`):

Network egress configuration. The default settings work reliably for this source; no special configuration is required.

## `compact` (type: `boolean`):

Core fields only (for AI-agent/MCP workflows).

## `excludeEmptyFields` (type: `boolean`):

Drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards.

## `incrementalMode` (type: `boolean`):

Compare against previous run state. stateKey is optional — defaults to a stable key derived from your search inputs so different searches never share state.

## `stateKey` (type: `string`):

Optional. Stable identifier for the tracked search universe. Leave empty to auto-generate from search inputs.

## `skipReposts` (type: `boolean`):

When incremental, skip records whose content matches an expired record from a prior run (cross-run duplicate detection).

## `emitUnchanged` (type: `boolean`):

When incremental mode is on, also emit records whose content has not changed since the last run.

## `emitExpired` (type: `boolean`):

When incremental mode is on, also emit records that were seen before but are no longer found.

## `telegramToken` (type: `string`):

Telegram bot token (from @BotFather). Required for Telegram notifications.

## `telegramChatId` (type: `string`):

Telegram chat or channel ID (e.g. "-100123456789"). Required when telegramToken is set.

## `discordWebhookUrl` (type: `string`):

Discord incoming webhook URL. Server Settings → Integrations → Webhooks → New Webhook.

## `slackWebhookUrl` (type: `string`):

Slack incoming webhook URL. api.slack.com/messaging/webhooks.

## `notificationLimit` (type: `integer`):

Maximum number of records included in each notification message (1–20).

## `notifyOnlyChanges` (type: `boolean`):

When Incremental Mode is on, only send notifications for NEW and UPDATED records. Has no effect outside incremental mode.

## `whatsappAccessToken` (type: `string`):

WhatsApp Cloud API permanent access token (System User token from Meta Business). Recipient must have messaged the business number within the last 24h (service-conversation window — free since Nov 2024).

## `whatsappPhoneNumberId` (type: `string`):

Your WhatsApp Business phone-number ID (numeric, from Meta dashboard). Required when whatsappAccessToken is set.

## `whatsappTo` (type: `string`):

Recipient phone in E.164 format without + (e.g. "436641234567"). Recipient must have messaged your business number within last 24h.

## `webhookUrl` (type: `string`):

Receives a JSON POST with {metadata, items} after each run. Universal escape hatch for n8n / Make / Zapier / custom backends.

## `webhookHeaders` (type: `object`):

Optional JSON object of custom headers (e.g. {"Authorization":"Bearer ..."}).

## `appConnector` (type: `string`):

Optional. Pick a connected app under Settings → API & Integrations to receive your results (including any contact details). Best-effort across MCP connectors as Apify expands its catalog.

## `mcpIssueTeam` (type: `string`):

Only when the connected app is an issue tracker: the team (name or ID) the summary issue is created under, if that app requires one.

## `descriptionFormat` (type: `string`):

Choose which representation of the listing description to include. `all` keeps every variant; the others keep only the selected one.

## `startUrls` (type: `array`):

One or more direct Google Scholar author-profile or article-search URLs to process. When set, the research-topic search is ignored.

## Actor input object example

```json
{
  "query": "deep learning",
  "maxResults": 5,
  "includeDetails": true,
  "proxyConfiguration": {
    "useApifyProxy": true
  },
  "compact": false,
  "excludeEmptyFields": false,
  "incrementalMode": false,
  "skipReposts": false,
  "emitUnchanged": false,
  "emitExpired": false,
  "notificationLimit": 5,
  "notifyOnlyChanges": false,
  "descriptionFormat": "all"
}
```

# Actor output Schema

## `results` (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 = {
    "query": "deep learning",
    "maxResults": 5,
    "excludeEmptyFields": false,
    "descriptionFormat": "all"
};

// Run the Actor and wait for it to finish
const run = await client.actor("blackfalcondata/google-scholar-scraper").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 = {
    "query": "deep learning",
    "maxResults": 5,
    "excludeEmptyFields": False,
    "descriptionFormat": "all",
}

# Run the Actor and wait for it to finish
run = client.actor("blackfalcondata/google-scholar-scraper").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "query": "deep learning",
  "maxResults": 5,
  "excludeEmptyFields": false,
  "descriptionFormat": "all"
}' |
apify call blackfalcondata/google-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=blackfalcondata/google-scholar-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

```json
{
    "openapi": "3.0.1",
    "info": {
        "title": "Google Scholar [Just 💰$0.90] — Author Citations & Profiles",
        "description": "💰 $0.90 per 1,000 author profiles — the cheapest paid Scholar scraper. Scrape citation counts, h-index & i10-index; research interests, publications & verified email domain; track new & updated authors across runs. Structured JSON export.",
        "version": "0.1",
        "x-build-id": "IdiUcfbC8DCTT75Nn"
    },
    "servers": [
        {
            "url": "https://api.apify.com/v2"
        }
    ],
    "paths": {
        "/acts/blackfalcondata~google-scholar-scraper/run-sync-get-dataset-items": {
            "post": {
                "operationId": "run-sync-get-dataset-items-blackfalcondata-google-scholar-scraper",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK"
                    }
                }
            }
        },
        "/acts/blackfalcondata~google-scholar-scraper/runs": {
            "post": {
                "operationId": "runs-sync-blackfalcondata-google-scholar-scraper",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor and returns information about the initiated run in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK",
                        "content": {
                            "application/json": {
                                "schema": {
                                    "$ref": "#/components/schemas/runsResponseSchema"
                                }
                            }
                        }
                    }
                }
            }
        },
        "/acts/blackfalcondata~google-scholar-scraper/run-sync": {
            "post": {
                "operationId": "run-sync-blackfalcondata-google-scholar-scraper",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK"
                    }
                }
            }
        }
    },
    "components": {
        "schemas": {
            "inputSchema": {
                "type": "object",
                "properties": {
                    "query": {
                        "title": "🔍 Research Topic / Keywords",
                        "type": "string",
                        "description": "Free-text research topic or keywords (e.g. \"deep learning\"). Profiled authors who publish on the topic are discovered from the article results. Do NOT enter an author's name — author-name search is not publicly available. Use a JSON array for multiple topics."
                    },
                    "maxResults": {
                        "title": "💯 Max Results",
                        "minimum": 0,
                        "maximum": 1000,
                        "type": "integer",
                        "description": "Maximum total author profiles (0 = unlimited).",
                        "default": 25
                    },
                    "includeDetails": {
                        "title": "📋 Include Full Profiles",
                        "type": "boolean",
                        "description": "Fetch each author's full profile (metrics, publications, co-authors). Turn off for a faster, lighter run that returns discovery-level fields only.",
                        "default": true
                    },
                    "proxyConfiguration": {
                        "title": "🌐 Proxy configuration",
                        "type": "object",
                        "description": "Network egress configuration. The default settings work reliably for this source; no special configuration is required.",
                        "default": {
                            "useApifyProxy": true
                        }
                    },
                    "compact": {
                        "title": "📦 Compact Output",
                        "type": "boolean",
                        "description": "Core fields only (for AI-agent/MCP workflows).",
                        "default": false
                    },
                    "excludeEmptyFields": {
                        "title": "Exclude empty fields from output",
                        "type": "boolean",
                        "description": "Drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards.",
                        "default": false
                    },
                    "incrementalMode": {
                        "title": "♻️ Incremental Mode",
                        "type": "boolean",
                        "description": "Compare against previous run state. stateKey is optional — defaults to a stable key derived from your search inputs so different searches never share state.",
                        "default": false
                    },
                    "stateKey": {
                        "title": "🔑 State Key",
                        "type": "string",
                        "description": "Optional. Stable identifier for the tracked search universe. Leave empty to auto-generate from search inputs."
                    },
                    "skipReposts": {
                        "title": "🚫 Skip Reposts",
                        "type": "boolean",
                        "description": "When incremental, skip records whose content matches an expired record from a prior run (cross-run duplicate detection).",
                        "default": false
                    },
                    "emitUnchanged": {
                        "title": "🔄 Emit Unchanged Records",
                        "type": "boolean",
                        "description": "When incremental mode is on, also emit records whose content has not changed since the last run.",
                        "default": false
                    },
                    "emitExpired": {
                        "title": "⚰️ Emit Expired Records",
                        "type": "boolean",
                        "description": "When incremental mode is on, also emit records that were seen before but are no longer found.",
                        "default": false
                    },
                    "telegramToken": {
                        "title": "🔑 Telegram Bot Token",
                        "type": "string",
                        "description": "Telegram bot token (from @BotFather). Required for Telegram notifications."
                    },
                    "telegramChatId": {
                        "title": "💬 Telegram Chat ID",
                        "type": "string",
                        "description": "Telegram chat or channel ID (e.g. \"-100123456789\"). Required when telegramToken is set."
                    },
                    "discordWebhookUrl": {
                        "title": "🎮 Discord Webhook URL",
                        "type": "string",
                        "description": "Discord incoming webhook URL. Server Settings → Integrations → Webhooks → New Webhook."
                    },
                    "slackWebhookUrl": {
                        "title": "💼 Slack Webhook URL",
                        "type": "string",
                        "description": "Slack incoming webhook URL. api.slack.com/messaging/webhooks."
                    },
                    "notificationLimit": {
                        "title": "📊 Max Records Per Notification",
                        "minimum": 1,
                        "maximum": 20,
                        "type": "integer",
                        "description": "Maximum number of records included in each notification message (1–20).",
                        "default": 5
                    },
                    "notifyOnlyChanges": {
                        "title": "🔄 Notify Only New/Updated",
                        "type": "boolean",
                        "description": "When Incremental Mode is on, only send notifications for NEW and UPDATED records. Has no effect outside incremental mode.",
                        "default": false
                    },
                    "whatsappAccessToken": {
                        "title": "📱 WhatsApp Access Token",
                        "type": "string",
                        "description": "WhatsApp Cloud API permanent access token (System User token from Meta Business). Recipient must have messaged the business number within the last 24h (service-conversation window — free since Nov 2024)."
                    },
                    "whatsappPhoneNumberId": {
                        "title": "📞 WhatsApp Phone Number ID",
                        "type": "string",
                        "description": "Your WhatsApp Business phone-number ID (numeric, from Meta dashboard). Required when whatsappAccessToken is set."
                    },
                    "whatsappTo": {
                        "title": "📲 WhatsApp Recipient",
                        "type": "string",
                        "description": "Recipient phone in E.164 format without + (e.g. \"436641234567\"). Recipient must have messaged your business number within last 24h."
                    },
                    "webhookUrl": {
                        "title": "🪝 Generic Webhook URL",
                        "type": "string",
                        "description": "Receives a JSON POST with {metadata, items} after each run. Universal escape hatch for n8n / Make / Zapier / custom backends."
                    },
                    "webhookHeaders": {
                        "title": "📋 Webhook Headers",
                        "type": "object",
                        "description": "Optional JSON object of custom headers (e.g. {\"Authorization\":\"Bearer ...\"})."
                    },
                    "appConnector": {
                        "title": "Send results to a connected app",
                        "type": "string",
                        "description": "Optional. Pick a connected app under Settings → API & Integrations to receive your results (including any contact details). Best-effort across MCP connectors as Apify expands its catalog."
                    },
                    "mcpIssueTeam": {
                        "title": "Issue tracker team",
                        "type": "string",
                        "description": "Only when the connected app is an issue tracker: the team (name or ID) the summary issue is created under, if that app requires one."
                    },
                    "descriptionFormat": {
                        "title": "📝 Description Format",
                        "enum": [
                            "all",
                            "text",
                            "html",
                            "markdown"
                        ],
                        "type": "string",
                        "description": "Choose which representation of the listing description to include. `all` keeps every variant; the others keep only the selected one.",
                        "default": "all"
                    },
                    "startUrls": {
                        "title": "🔗 Paste / Direct URLs",
                        "type": "array",
                        "description": "One or more direct Google Scholar author-profile or article-search URLs to process. When set, the research-topic search is ignored.",
                        "items": {
                            "type": "string"
                        }
                    }
                }
            },
            "runsResponseSchema": {
                "type": "object",
                "properties": {
                    "data": {
                        "type": "object",
                        "properties": {
                            "id": {
                                "type": "string"
                            },
                            "actId": {
                                "type": "string"
                            },
                            "userId": {
                                "type": "string"
                            },
                            "startedAt": {
                                "type": "string",
                                "format": "date-time",
                                "example": "2025-01-08T00:00:00.000Z"
                            },
                            "finishedAt": {
                                "type": "string",
                                "format": "date-time",
                                "example": "2025-01-08T00:00:00.000Z"
                            },
                            "status": {
                                "type": "string",
                                "example": "READY"
                            },
                            "meta": {
                                "type": "object",
                                "properties": {
                                    "origin": {
                                        "type": "string",
                                        "example": "API"
                                    },
                                    "userAgent": {
                                        "type": "string"
                                    }
                                }
                            },
                            "stats": {
                                "type": "object",
                                "properties": {
                                    "inputBodyLen": {
                                        "type": "integer",
                                        "example": 2000
                                    },
                                    "rebootCount": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "restartCount": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "resurrectCount": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "computeUnits": {
                                        "type": "integer",
                                        "example": 0
                                    }
                                }
                            },
                            "options": {
                                "type": "object",
                                "properties": {
                                    "build": {
                                        "type": "string",
                                        "example": "latest"
                                    },
                                    "timeoutSecs": {
                                        "type": "integer",
                                        "example": 300
                                    },
                                    "memoryMbytes": {
                                        "type": "integer",
                                        "example": 1024
                                    },
                                    "diskMbytes": {
                                        "type": "integer",
                                        "example": 2048
                                    }
                                }
                            },
                            "buildId": {
                                "type": "string"
                            },
                            "defaultKeyValueStoreId": {
                                "type": "string"
                            },
                            "defaultDatasetId": {
                                "type": "string"
                            },
                            "defaultRequestQueueId": {
                                "type": "string"
                            },
                            "buildNumber": {
                                "type": "string",
                                "example": "1.0.0"
                            },
                            "containerUrl": {
                                "type": "string"
                            },
                            "usage": {
                                "type": "object",
                                "properties": {
                                    "ACTOR_COMPUTE_UNITS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_WRITES": {
                                        "type": "integer",
                                        "example": 1
                                    },
                                    "KEY_VALUE_STORE_LISTS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_INTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_EXTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_SERPS": {
                                        "type": "integer",
                                        "example": 0
                                    }
                                }
                            },
                            "usageTotalUsd": {
                                "type": "number",
                                "example": 0.00005
                            },
                            "usageUsd": {
                                "type": "object",
                                "properties": {
                                    "ACTOR_COMPUTE_UNITS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_WRITES": {
                                        "type": "number",
                                        "example": 0.00005
                                    },
                                    "KEY_VALUE_STORE_LISTS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_INTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_EXTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_SERPS": {
                                        "type": "integer",
                                        "example": 0
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }
    }
}
```