Google Scholar [Just 💰$0.90] — Author Citations & Profiles

What does Google Scholar do?

Google Scholar extracts structured profile data from 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 → 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

• 📋 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.

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.

{
  "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.

{
  "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.

{
  "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

{
  "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 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 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 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. 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 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 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 — 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.

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

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