[
{
"id": "openalex-research-scholar-api_1000",
"service": "openalex-research-scholar-api",
"title": "OPENALEX RESEARCH SCHOLAR Record #1",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for openalex-research-scholar-api",
"inputReceived": {
"searchQuery": "machine learning",
"publicationYear": 2025,
"hasOpenAccess": false,
"maxPapers": 10
},
"data": {
"index": 1,
"relevanceScore": 0.95,
"summary": "High-fidelity structured response for OPENALEX RESEARCH SCHOLAR query parameter extraction.",
"attributes": {
"region": "Global",
"category": "OPENALEX",
"timestamp": "2026-10-12T11:07:32.519Z"
}
},
"timestamp": "2026-10-12T11:07:32.519Z"
},
{
"id": "openalex-research-scholar-api_1001",
"service": "openalex-research-scholar-api",
"title": "OPENALEX RESEARCH SCHOLAR Record #2",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for openalex-research-scholar-api",
"inputReceived": {
"searchQuery": "machine learning",
"publicationYear": 2025,
"hasOpenAccess": false,
"maxPapers": 10
},
"data": {
"index": 2,
"relevanceScore": 0.8999999999999999,
"summary": "High-fidelity structured response for OPENALEX RESEARCH SCHOLAR query parameter extraction.",
"attributes": {
"region": "Global",
"category": "OPENALEX",
"timestamp": "2026-10-12T11:07:32.519Z"
}
},
"timestamp": "2026-10-12T11:07:32.519Z"
},
{
"id": "openalex-research-scholar-api_1002",
"service": "openalex-research-scholar-api",
"title": "OPENALEX RESEARCH SCHOLAR Record #3",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for openalex-research-scholar-api",
"inputReceived": {
"searchQuery": "machine learning",
"publicationYear": 2025,
"hasOpenAccess": false,
"maxPapers": 10
},
"data": {
"index": 3,
"relevanceScore": 0.85,
"summary": "High-fidelity structured response for OPENALEX RESEARCH SCHOLAR query parameter extraction.",
"attributes": {
"region": "Global",
"category": "OPENALEX",
"timestamp": "2026-10-12T11:07:32.519Z"
}
},
"timestamp": "2026-10-12T11:07:32.519Z"
},
{
"id": "openalex-research-scholar-api_1003",
"service": "openalex-research-scholar-api",
"title": "OPENALEX RESEARCH SCHOLAR Record #4",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for openalex-research-scholar-api",
"inputReceived": {
"searchQuery": "machine learning",
"publicationYear": 2025,
"hasOpenAccess": false,
"maxPapers": 10
},
"data": {
"index": 4,
"relevanceScore": 0.7999999999999999,
"summary": "High-fidelity structured response for OPENALEX RESEARCH SCHOLAR query parameter extraction.",
"attributes": {
"region": "Global",
"category": "OPENALEX",
"timestamp": "2026-10-12T11:07:32.519Z"
}
},
"timestamp": "2026-10-12T11:07:32.519Z"
},
{
"id": "openalex-research-scholar-api_1004",
"service": "openalex-research-scholar-api",
"title": "OPENALEX RESEARCH SCHOLAR Record #5",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for openalex-research-scholar-api",
"inputReceived": {
"searchQuery": "machine learning",
"publicationYear": 2025,
"hasOpenAccess": false,
"maxPapers": 10
},
"data": {
"index": 5,
"relevanceScore": 0.75,
"summary": "High-fidelity structured response for OPENALEX RESEARCH SCHOLAR query parameter extraction.",
"attributes": {
"region": "Global",
"category": "OPENALEX",
"timestamp": "2026-10-12T11:07:32.519Z"
}
},
"timestamp": "2026-10-12T11:07:32.519Z"
}
]
curl --location --request POST 'https://zylalabs.com/api/14184/openalex+scientific+research+and+citations+scraper+api/35239/execute+openalex+scientific+research+and+citations+scraper+api' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{"searchQuery":"machine learning","publicationYear":2025,"hasOpenAccess":false,"maxPapers":10}'
注册后,每个开发者都会被分配一个个人 API 访问密钥,这是一个唯一的字母和数字组合,用于访问我们的 API 端点。要使用 OpenAlex 科学研究和引用爬虫 API 进行身份验证,只需在 Authorization 标头中包含您的 bearer token。
| 标头 | 描述 |
|---|---|
授权
|
必需
应为 Bearer access_key. 订阅后,请查看上方的"您的 API 访问密钥"。
|
无长期承诺。随时升级、降级或取消。 免费试用包括最多 50 个请求。
(年度计费可节省 2 个月 🎉)
OpenAlex API 返回科学研究论文的全面书目元数据,包括作者信息 DOI 引用 发表日期 引用次数 机构隶属关系以及开放获取 PDF 文件的链接
响应中的关键字段包括“id”“标题”“状态”“结果”“输入接收”和“数据”其中包含“相关性评分”“摘要”和“时间戳”等属性
用户可以使用“searchQuery”“publicationYear”“hasOpenAccess”和“maxPapers”等参数自定义他们的请求,以筛选和限制结果
响应数据结构为JSON数组,每个条目包含有关特定研究论文的元数据,包括其属性和相关分数,使其易于解析和分析
数据来源于OpenAlex知识图谱,该图谱从广泛的学术作品中整理书目元数据,确保了数据集的全面性和可靠性
典型的使用案例包括进行文献综述执行引文分析评估研究趋势以及评估特定学术作品对研究人员和机构的影响
用户可以分析“相关性得分”以优先考虑发现使用“摘要”以快速获取见解,并访问“链接”以打开公共访问的 PDF 进行进一步阅读,从而促进高效的研究工作流程
通过在OpenAlex知识图谱中定期更新和质量检查,维护数据的准确性,确保信息反映最新的学术出版物和引用