logo
EN

[JP]OpenAlex Academic Paper Search

Education

Search and collect academic papers in bulk by keyword using the open OpenAlex API. Retrieves paper metadata including title, authors, abstract, citation count, publication year, venue, DOI, PDF link,

Templates are view-only on mobile. Please switch to a desktop to run this template.

📌 What can you collect with OpenAlex Academic Paper Search (for Japan)?

This template collects academic paper information (title, author, article URL, etc.) from the open OpenAlex API via keywords. Data is collected from OpenAlex, an open scholarly database of 240M+ works maintained by the Allen Institute. It is provided under a CC0 (public domain) license and permits commercial use.

💰 Pricing

This template currently has no per-line charge.

📦 Output

OpenAlex Academic Paper Search returns these fields:

  • タイトル (Title)
  • 著者 (Authors)
  • 概要 (Abstract)
  • 記事URL (Article URL)
  • DOI
  • 被引用数 (Citation count)
  • 参考文献数 (Reference count)
  • 発表年 (Year)
  • 掲載誌 (Venue)
  • 種別 (Type)
  • PDFリンク (PDF link)
  • 研究分野 (Research fields)

Example Output:

json

{
  "タイトル": "Deep Residual Learning for Image Recognition",
  "著者": "Kaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun",
  "概要": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks…",
  "記事URL": "https://openalex.org/W2194775991",
  "DOI": "https://doi.org/10.1109/cvpr.2016.90",
  "被引用数": "210000",
  "参考文献数": "34",
  "発表年": "2016",
  "掲載誌": "IEEE Conference on Computer Vision and Pattern Recognition",
  "種別": "article",
  "PDFリンク": "https://arxiv.org/pdf/1512.03385",
  "研究分野": "Computer Vision; Deep Learning"
}

🎯 Use Cases

  • Build article datasets with titles, authors, years, venues, and URLs.
  • Monitor research coverage of selected topics or fields over time.
  • Compare publication patterns across searches or categories.
  • Export structured content records for research or downstream analysis.

🐙 Why Octoparse

  • Ready-to-use OpenAlex workflow: Enter a keyword (up to 5000 per run), run the template, and review the structured rows returned for each paper.
  • Useful results: The タイトル, 著者, 概要, DOI, 被引用数 and other fields support comparison, monitoring, and reusable data workflows.
  • No proxy or authentication: Uses the open CC0 API directly, with relaxed, stable rate limits.
  • Export and reuse: Export rows to CSV, Excel, JSON, databases, or cloud storage, or send them to reporting and AI workflows through Octoparse APIs.

Input and Output

Input

{
  "keywords": [
    "keyword1",
    "keyword2"
  ],
  "number_of_papers_per_keyword": "1",
  "email": "name@example.com"
}

Output

[
  {
    "DOI": "https://doi.org/10.5860/choice.33-1577",
    "PDF_URL": "https://doi.org/10.5281/zenodo.11381885",
    "タイトル": "Artificial intelligence: a modern approach",
    "参考文献数": "0",
    "学科": "Cognitive Computing and Networks; Advanced Research in Systems and Signal Processing",
    "採集日時": "2026-09-10 11:56:29",
    "掲載誌": "Choice Reviews Online",
    "検索キーワード": "artificial intelligence",
    "概要": "The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.",
    "発表年": "1995",
    "種別": "book-review",
    "著者": "Dr. Anil Kumar; Sivasubramanian Balasubramanian; Dr. Haewon Byeon; Prof. Ganesh Vasudeo Manerkar",
    "被引用数": "22259",
    "記事URL": "https://openalex.org/W2122410182"
  }
]

You can schedule recurring runs, export the collected data, and reuse it through Octoparse OpenAPI or the AgentTools API. Scheduling and API access may require a paid Octoparse plan.

📝 Input

  • keyword (up to 5000 per run) (required): Enter one or more values, one per line.
  • limit (optional): Maximum number of results (papers) per keyword. Defaults to 100 if blank.
  • mailto (optional): Adding an email joins OpenAlex's polite pool for ~10x faster, more stable collection. No registration needed; takes effect immediately.

🚀 How to Use

  1. Open the template and select Try it or Start.
  2. Complete the input fields listed above.
  3. Run the task.
  4. Review the output rows and export or process the structured data.

⚠️ Limitations

  • Fields such as 概要, DOI, PDFリンク, and 研究分野 can be empty when the source record does not provide them.
  • Overly generic keywords (e.g., a stopword like "it") may be treated as invalid search terms and return no results; use specific keywords.

💡 Tips

  • Start with one or a few inputs and review the returned rows before scaling.
  • Use the most specific valid keyword to reduce unrelated or duplicate records.
  • Use 記事URL or DOI as the deduplication key.

❓ FAQ

Q: Does this template collect complete detail-page data?

A: No. It collects the search-result fields shown in Output; use a detail-page template when deeper record data is required.

Q: Can I schedule and export the collected data?

A: Yes. You can schedule runs and export data from Octoparse. Scheduling may require a paid Octoparse plan.

Q: How much does it cost?

A: This template is currently free of charge and has no per-line usage fee. Octoparse plan or resource limits may still apply.