Search papers, grants, and
researcher profiles at scale

ApifyForge helps researchers and librarians build literature review datasets by searching PubMed, OpenAlex, and Semantic Scholar in batch. Best for systematic reviews requiring cross-database deduplication of 500-5,000 papers. Less suitable when you need full-text PDFs or citation graph analysis. Costs $0.05 per query.

The problem

Literature reviews require searching multiple databases — PubMed, Google Scholar, Semantic Scholar, ORCID — and manually tracking results. A systematic review of 500+ papers takes weeks of searching, deduplicating, and organising.

The solution

Apify actors search academic databases programmatically. PubMed for biomedical literature. OpenAlex for cross-discipline papers. Semantic Scholar for AI-powered relevance. ORCID for researcher profiles. Run batch queries, deduplicate results, and export structured datasets for systematic reviews.

How it works

1

Define search criteria

Specify your keywords, date ranges, journals, and inclusion/exclusion criteria — just like a systematic review protocol.

2

Search across databases

Run PubMed, OpenAlex, and Semantic Scholar actors with the same queries. Each returns structured data: title, authors, abstract, citations, DOI.

3

Deduplicate and merge

Export results from all sources, deduplicate by DOI, and merge into a master dataset. Flag papers appearing in multiple databases for higher confidence.

4

Analyse and visualise

Import into reference managers via CSV, or feed into bibliometric analysis tools. Use citation counts to identify seminal papers and trends.

What does it cost?

Searching 50 queries across 3 databases costs $2-5 in PPE charges. A full systematic review dataset of 2,000 papers: $10-25. Monthly monitoring: $5-10.

Estimate your cost →

Ready to get started?

Sign in and set up the CLI in under 2 minutes. Your Apify token stays on your machine.

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