Compare Google Scholar Scraper Side by Side
Compare Google Scholar Scraper Side by Side — a practical guide with a ready-to-run configuration and structured output.
Need to compare google scholar scraper across targets? Thirdwatch's Google Scholar Scraper turns it into structured rows — the fields you need, ready to export.
Skip the setup: Run this as a ready-to-go task on Apify — pre-loaded with the configuration from this guide.
Why Compare side by side works
Put the same fields side by side for every target — the deltas are where decisions happen. This guide shows the configuration that pulls google scholar scraper data from Google Scholar Scraper.
How does this compare to the alternatives?
Manual copy-paste works for a handful of rows; dedicated per-site scrapers on other platforms either gate the same data behind sign-in or charge flat monthly fees regardless of usage.
Why this Actor
- Purpose-built for Google Scholar Scraper — no generic crawling config to tune
- Pay-per-result pricing — you pay only for rows that come back
- Structured output ready for JSON/CSV export or API consumption
- No login, cookies, or API keys needed for the public surfaces it reads
How to do it in 3 steps
Step 1: Configure the input
Set the inputs as shown below — query takes the targets, and the optional fields bound the run size.
Step 2: Run the Actor
Run it from the console, the API, or the linked saved task. One dataset row is written per record.
Step 3: Use the output
Every row carries the fields shown in the sample — export as JSON, CSV, or feed it straight into your pipeline.
{
"queries": [
"large language models"
],
"maxResults": 10
}Each dataset row looks like:
{
"title": "Graph clustering with graph neural networks",
"url": "http://www.jmlr.org/papers/v24/20-998.html",
"authors": "A Tsitsulin , J Palowitch , B Perozzi , E M\u00fcller",
"venue": "Journal of Machine Learning\u00a0\u2026, 2023",
"year": "2023",
"snippet": "\u2026 of Graph Neural Networks (GNNs)\u2014specialized deep learning architectures for dealing with graph structured data, such as social networks (Perozzi et al., 2014b), recommender \u2026",
"citedBy": 645,
"pdfUrl": null,
"query": "graph neural networks",
"position": 1
}What to watch for
Rate the targets reasonably — large batches benefit from modest concurrency. Source sites may throttle heavy bursts; retry once and widen filters if a target surface is thin.
Related use cases
- Feed Google Scholar Scraper Data Into Your Pipeline
- Extract Google Scholar Scraper at Scale
- Monitor Google Scholar Scraper on a Schedule
- Export Google Scholar Scraper to CSV
- All Thirdwatch use-case guides
Run the Google Scholar Scraper on Apify Store — pay per result, free to try, no credit card to test.
Frequently asked questions
What does Google Scholar Scraper return?
One dataset row per result — with the fields shown in the sample output. Export as JSON, CSV, or Excel.
How do I control run size?
`query` selects the targets and the max/limit fields bound how many rows come back. Start small, then scale.
Can I run this on a schedule?
Yes — save it as a task and attach a schedule in Apify Console for daily/weekly pulls.
Is this data public?
The Actor collects publicly visible data only — the same information a logged-out visitor sees.
What formats can I export?
JSON, CSV, Excel, XML, or direct API access to the dataset — plus webhooks and integrations.
What if a run returns fewer rows than expected?
The source limits some results; retry once, and widen the query or filters if the target surface is genuinely thin.