# OpenAlex Academic Paper Search

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,

- Template page: [https://www.octoparse.com/template/openalex-academic-paper-search](https://www.octoparse.com/template/openalex-academic-paper-search)
- Categories: Education
- Status: `PUBLISHED`
- Run mode: `CLOUD`

## Pricing

Free.

## What is an Octoparse template?

An Octoparse template is a ready-to-use scraping workflow with predefined inputs and structured outputs. Use the exact parameter keys below; do not invent parameters.

## Overview

This ready-to-use template collects structured data from search-results pages.

## Output

The current workflow extracts 14 fields, including `DOI`, `PDF_URL`, `タイトル`, `参考文献数`, `学科`, `採集日時`, `掲載誌`, `検索キーワード`, `概要`, `発表年`, `種別`, `著者`, `被引用数`, `記事URL`.

## Use Cases

- Use structured results for research, comparison, and monitoring.
- Collect rows without building the extraction workflow from scratch.
- Combine results with other templates for downstream enrichment.
- Review a representative result before starting a large batch.

## Why Octoparse

- **No coding required:** complete Keywords, Number of papers per keyword, Email (optional) and start the template in cloud mode.
- **Flexible export:** review and export results as CSV, Excel, or JSON.

## Input

### Keywords

Required. MainKeys Constraints: items.minLength=1; items.maxLength=100.

### Number of papers per keyword

Optional. limit Constraints: minimum=, maximum=1000.

### Email (optional)

Optional. mailto Constraints: minLength=0; maxLength=100.

## How to Use

# OpenAlex Academic Paper Search (for Japan) – Usage Guide

Collects academic paper metadata from the OpenAlex API (240M+ works, CC0/public domain, commercial use allowed). No per-line charge.

## Input Parameters

- **keyword** (required, up to 5000 per run) – Enter one keyword per line.
- **limit** (optional) – Max results per keyword. Default: 100 if blank.
- **mailto** (optional) – Your email address. Joins OpenAlex’s polite pool for ~10x faster, more stable collection. No registration needed; takes effect immediately.

## Output Fields

Each returned paper includes:

- Title, Authors, Abstract, Article URL, DOI, Citation count, Reference count, Year, Venue, Type, PDF link, Research fields.

## Limitations

- Fields such as Abstract, DOI, PDF link, and Research fields may be empty if the source record does not provide them.
- Overly generic keywords (e.g., stopwords like “it”) may be treated as invalid and return no results. Use specific keywords.

## How to Use

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

## Tips

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

## Limitations

Results depend on what the source site exposes at run time. Fields may be empty when a source page does not provide a value. The template enforces the input limits shown above.

## Tips

Use specific, valid inputs and review a representative result before starting a large batch. Remove duplicate inputs when repeated records are not needed.

## FAQ

### What do I need to enter?

Use the exact inputs and limits listed above.

### What data does this template return?

See Output Schema below; availability can vary by source record.

### How is usage billed?

See the Pricing section above for current pricing and billing details.

## Related Templates

- [RISS Academic Information Search Scraper](https://www.octoparse.com/template/riss-academic-information-search-scraper)
- [Naver Academic Scraper](https://www.octoparse.com/template/naver-academic-scraper)
- [Bic Camera Search Results Scraper](https://www.octoparse.com/template/bic-camera-search-results-scraper)
- [Google Search Scraper](https://www.octoparse.com/template/google-search-scraper)
- [Google Search Scraper (Free)](https://www.octoparse.com/template/google-advanced-search)

## Input Schema

```json
{
  "title": "OpenAlex Academic Paper Search input",
  "description": "Input parameters configured for the current published template version.",
  "type": "object",
  "schemaVersion": 1,
  "additionalProperties": false,
  "properties": {
    "keywords": {
      "title": "Keywords",
      "description": "MainKeys",
      "type": "array",
      "items": {
        "type": "string",
        "minLength": 1,
        "maxLength": 100
      },
      "x-octoparse": {
        "apiEnabled": true
      }
    },
    "number_of_papers_per_keyword": {
      "title": "Number of papers per keyword",
      "description": "limit",
      "type": "integer",
      "maximum": 1000,
      "x-octoparse": {
        "apiEnabled": false
      }
    },
    "email": {
      "title": "Email (optional)",
      "description": "mailto",
      "type": "string",
      "minLength": 0,
      "maxLength": 100,
      "x-octoparse": {
        "apiEnabled": false
      }
    }
  },
  "required": [
    "keywords"
  ]
}
```

## Input JSON Example

Example 1

```json
{
  "keywords": [
    "artificial intelligence"
  ],
  "number_of_papers_per_keyword": 1
}
```

Example 2

```json
{
  "keywords": [
    "artificial intelligence"
  ],
  "number_of_papers_per_keyword": 1
}
```

Example 3

```json
{
  "email": "heather@octoparse.com",
  "keywords": [
    "machine learning",
    "web scraping",
    "deep learning",
    "スクレイピング",
    "人工知能"
  ],
  "number_of_papers_per_keyword": 100
}
```

Example 4

```json
{
  "keywords": [
    "machine learning",
    "web scraping",
    "deep learning",
    "スクレイピング",
    "人工知能"
  ]
}
```

Example 5

```json
{
  "email": "heather@octoparse.cm",
  "keywords": [
    "machine learning",
    "web scraping",
    "deep learning",
    "スクレイピング",
    "人工知能"
  ],
  "number_of_papers_per_keyword": 500
}
```


## Output Schema

```json
{
  "schemaVersion": 1,
  "type": "object",
  "properties": {
    "DOI": {
      "type": "string",
      "description": ""
    },
    "PDF_URL": {
      "type": "string",
      "description": ""
    },
    "タイトル": {
      "type": "string",
      "description": ""
    },
    "参考文献数": {
      "type": "string",
      "description": ""
    },
    "学科": {
      "type": "string",
      "description": ""
    },
    "採集日時": {
      "type": "string",
      "description": ""
    },
    "掲載誌": {
      "type": "string",
      "description": ""
    },
    "検索キーワード": {
      "type": "string",
      "description": ""
    },
    "概要": {
      "type": "string",
      "description": ""
    },
    "発表年": {
      "type": "string",
      "description": ""
    },
    "種別": {
      "type": "string",
      "description": ""
    },
    "著者": {
      "type": "string",
      "description": ""
    },
    "被引用数": {
      "type": "string",
      "description": ""
    },
    "記事URL": {
      "type": "string",
      "description": ""
    }
  },
  "title": "OpenAlex Academic Paper Search output",
  "description": "Initial output fields derived from the current published implementation. Field descriptions are maintained by Data Center; production field names and types should be refreshed from recent successful runs.",
  "additionalProperties": false
}
```

## Output Example

Example 1

```json
{
  "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"
}
```

Example 2

```json
{
  "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": "2026-09-09 03:48:53",
  "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": "22264",
  "記事URL": "https://openalex.org/W2122410182"
}
```

Example 3

```json
{
  "DOI": "https://doi.org/10.2208/jscejppce.76.1_84",
  "PDF_URL": "https://www.jstage.jst.go.jp/article/jscejppce/76/1/76_84/_pdf",
  "タイトル": "FLOOD CONTROL INFORMATION SYSTEM FOR SECURING EVACUATION AND PREVENTING FLOODING: DEVELOPMENT AND APPLICATION TO THE WEST AREA OF YOKOHAMA STATION",
  "参考文献数": "1",
  "学科": "Energy, Environment, Agriculture Analysis; Climate Change and Sustainable Development; Earthquake and Disaster Impact Studies",
  "採集日時": "2026-09-01 02:48:06",
  "掲載誌": "Journal of Japan Society of Civil Engineers Ser F5 (Professional Practices in Civil Engineering)",
  "検索キーワード": "スクレイピング",
  "概要": "地下街等において水害に対して適切に浸水防止や避難を行うには，降雨や水位等の状況を把握することが必要となる．本研究では地下街等の施設管理者の水防活動を調査し，必要な情報を入手するための情報システムの役割と課題を整理した．その結果，限られたエリアを多角的に把握したい施設管理者のリクワイヤメントに対して，自らの観測設備の情報を公平に広域的に提供する行政のシステムでは適合しないという課題が確認された．このため，統合的な水防情報システム「エリアレイン」を開発して，2018年8月から横浜駅西口の事業者等を利用者とする実証試験を開始した．「エリアレイン」は，対象エリアの情報を自動的に収集し，危険度の判定，情報の要約，関係者への通知，可視化を行う．利用者による有用性の評価は，浸水，水位，降雨，潮位の順であった．",
  "発表年": "2020",
  "種別": "article",
  "著者": "Wataru Kobayashi; Miho Ohara",
  "被引用数": "1",
  "記事URL": "https://openalex.org/W3107778396"
}
```

Example 4

```json
{
  "DOI": "https://doi.org/10.2508/chikusan.93.347",
  "PDF_URL": "https://www.jstage.jst.go.jp/article/chikusan/93/4/93_347/_pdf",
  "タイトル": "毎日の日乳量記録に基づく機械学習手法を用いた分娩後早期泌乳曲線予測精度の検討",
  "参考文献数": "12",
  "学科": "Milk Quality and Mastitis in Dairy Cows",
  "採集日時": "2026-08-31 10:04:50",
  "掲載誌": "Nihon Chikusan Gakkaiho",
  "検索キーワード": "機械学習",
  "概要": "乳用雌牛の泌乳曲線における分娩後早期の予測について，牛群ごとの毎日乳量を用いた機械学習による手法を試行した．各個体の日乳量，両親の乳量および泌乳持続性の推定育種価の平均，産次および分娩月を特徴量として，分娩後305日までの日乳量予測モデルを牛群ごとに構築した．モデルは，予測日直近10日分の日乳量を特徴量として各予測日の予測誤差を最小とするモデル（モデル1），および予測開始日の直近10日分の日乳量を特徴量として予測開始日～分娩後305日の予測誤差を最小とするモデル（モデル2）を用いた．2牛群の489乳期および664乳期の記録を用い，分娩後60日時点の予測精度を評価した．両親の育種価は予測精度向上への寄与が認められなかった．予測乳量の絶対平均誤差は，モデル1で2.08～6.69 kg, モデル2で2.87～5.71 kgだった．モデル1は，モデル2よりも予測開始日に近い期間の予測精度が高い傾向があった．",
  "発表年": "2022",
  "種別": "article",
  "著者": "優介 伊藤; 直澄 田鎖; 雄大 太田; 裕樹 大井; 諭 山口; 武志 山崎",
  "被引用数": "2",
  "記事URL": "https://openalex.org/W4311259948"
}
```

Example 5

```json
{
  "DOI": "https://doi.org/10.2208/jscejhe.74.5_i_1369",
  "PDF_URL": "https://www.jstage.jst.go.jp/article/jscejhe/74/5/74_I_1369/_pdf",
  "タイトル": "A CASE STUDY OF FLOOD WATER LEVEL PREDICTION IN THE TOKORO RIVER IN 2016 USING RECURRENT NEURAL NETWORKS",
  "参考文献数": "8",
  "学科": "Hydrological Forecasting Using AI; Hydrology and Watershed Management Studies; Flood Risk Assessment and Management",
  "採集日時": "2026-08-31 10:00:19",
  "掲載誌": "Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering)",
  "検索キーワード": "機械学習",
  "概要": "洪水時を想定した水位予測に深層学習を用いた全結合ニューラルネットワーク(FCNN)などの機械学習を用いたモデル化手法がいくつか提案されている．しかし，モデル化の際に観測された水位情報の時系列性はあまり考慮されていない．本論文では，時系列情報処理構造を持つリカレント型のニューラルネットワーク(RNN)を洪水時の水位予測に用いることを提案する．提案法で2016年8月の北海道豪雨で被害を受けた常呂川の洪水事例で本川にある5地点の水位予測を行い，水位の実測値との平均二乗誤差(RMSE)で評価した．その結果，リードタイム(LT) 6hの時にRMSEが全地点平均で0.29mであった．この結果は，従来型のニューラルネットワークに比べLT 6hで0.11m小さく，RNNを用いることの有効性が示された．",
  "発表年": "2018",
  "種別": "article",
  "著者": "Koki Yamada; Yosuke Kobayashi; Makoto NAKATSUGAWA; Jay Kishigami",
  "被引用数": "18",
  "記事URL": "https://openalex.org/W2992676458"
}
```

## API

`POST https://openapi.octoparse.com/api/agentTools/executeTask`

This is the OpenAPI Agent Tools `executeTask` endpoint. Authenticate with a Bearer token.

Request body:

`templateName` is the template slug; `parameters` is a JSON object string whose keys match Input Schema; `taskName`, `idempotencyKey`, and `maxCollectedRows` are optional.

Obtain a token:

```http
POST https://openapi.octoparse.com/token
Content-Type: application/json

{
  "username": "YOUR_USERNAME",
  "password": "YOUR_PASSWORD",
  "grant_type": "password"
}
```

Use `data.access_token` as `Authorization: Bearer <token>`. After starting, poll `getTaskStatus` and export with `exportData`.

```json
{
  "templateName": "openalex-academic-paper-search",
  "taskName": "OpenAlex Academic Paper Search",
  "parameters": "{\"keywords\":[\"artificial intelligence\"],\"number_of_papers_per_keyword\":1}"
}
```

## Python Example

```python
import json
import requests

OPENAPI_BASE = "https://openapi.octoparse.com"
ACCESS_TOKEN = "YOUR_ACCESS_TOKEN"

parameters = json.loads("{\"keywords\":[\"artificial intelligence\"],\"number_of_papers_per_keyword\":1}")

response = requests.post(
    f"{OPENAPI_BASE}/api/agentTools/executeTask",
    headers={
        "Authorization": f"Bearer {ACCESS_TOKEN}",
        "Content-Type": "application/json",
    },
    json={
        "templateName": "openalex-academic-paper-search",
        "taskName": "OpenAlex Academic Paper Search",
        "parameters": json.dumps(parameters, ensure_ascii=False),
    },
    timeout=60,
)
print(response.status_code)
print(response.json())
```

## HTTP/cURL Example

```bash
curl -X POST "https://openapi.octoparse.com/api/agentTools/executeTask" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "templateName": "openalex-academic-paper-search",
  "taskName": "OpenAlex Academic Paper Search",
  "parameters": "{\"keywords\":[\"artificial intelligence\"],\"number_of_papers_per_keyword\":1}"
}
JSON
```

## MCP Setup

Connect an MCP-compatible AI client to `https://mcp.octoparse.com` using browser OAuth or an Octoparse API key. Find `openalex-academic-paper-search`, inspect its inputs, create a cloud task with the exact Input Schema keys, and export JSON results.

## Machine-readable Resources

- [Template contract](https://www.octoparse.com/templates/openalex-academic-paper-search.json)
- [Manifest](https://www.octoparse.com/templates/openalex-academic-paper-search-manifest.json)