[
{
"id": "crossref-research-citations-api_1000",
"service": "crossref-research-citations-api",
"title": "CROSSREF RESEARCH CITATIONS Record #1",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for crossref-research-citations-api",
"inputReceived": {
"query": "artificial intelligence neural networks",
"maxItems": 20,
"filterYear": "sample_value"
},
"data": {
"index": 1,
"relevanceScore": 0.95,
"summary": "High-fidelity structured response for CROSSREF RESEARCH CITATIONS query parameter extraction.",
"attributes": {
"region": "Global",
"category": "CROSSREF",
"timestamp": "2026-10-12T05:46:16.249Z"
}
},
"timestamp": "2026-10-12T05:46:16.249Z"
},
{
"id": "crossref-research-citations-api_1001",
"service": "crossref-research-citations-api",
"title": "CROSSREF RESEARCH CITATIONS Record #2",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for crossref-research-citations-api",
"inputReceived": {
"query": "artificial intelligence neural networks",
"maxItems": 20,
"filterYear": "sample_value"
},
"data": {
"index": 2,
"relevanceScore": 0.8999999999999999,
"summary": "High-fidelity structured response for CROSSREF RESEARCH CITATIONS query parameter extraction.",
"attributes": {
"region": "Global",
"category": "CROSSREF",
"timestamp": "2026-10-12T05:46:16.249Z"
}
},
"timestamp": "2026-10-12T05:46:16.249Z"
},
{
"id": "crossref-research-citations-api_1002",
"service": "crossref-research-citations-api",
"title": "CROSSREF RESEARCH CITATIONS Record #3",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for crossref-research-citations-api",
"inputReceived": {
"query": "artificial intelligence neural networks",
"maxItems": 20,
"filterYear": "sample_value"
},
"data": {
"index": 3,
"relevanceScore": 0.85,
"summary": "High-fidelity structured response for CROSSREF RESEARCH CITATIONS query parameter extraction.",
"attributes": {
"region": "Global",
"category": "CROSSREF",
"timestamp": "2026-10-12T05:46:16.249Z"
}
},
"timestamp": "2026-10-12T05:46:16.249Z"
},
{
"id": "crossref-research-citations-api_1003",
"service": "crossref-research-citations-api",
"title": "CROSSREF RESEARCH CITATIONS Record #4",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for crossref-research-citations-api",
"inputReceived": {
"query": "artificial intelligence neural networks",
"maxItems": 20,
"filterYear": "sample_value"
},
"data": {
"index": 4,
"relevanceScore": 0.7999999999999999,
"summary": "High-fidelity structured response for CROSSREF RESEARCH CITATIONS query parameter extraction.",
"attributes": {
"region": "Global",
"category": "CROSSREF",
"timestamp": "2026-10-12T05:46:16.249Z"
}
},
"timestamp": "2026-10-12T05:46:16.249Z"
},
{
"id": "crossref-research-citations-api_1004",
"service": "crossref-research-citations-api",
"title": "CROSSREF RESEARCH CITATIONS Record #5",
"status": "VERIFIED_ACTIVE",
"result": "Processed query successfully for crossref-research-citations-api",
"inputReceived": {
"query": "artificial intelligence neural networks",
"maxItems": 20,
"filterYear": "sample_value"
},
"data": {
"index": 5,
"relevanceScore": 0.75,
"summary": "High-fidelity structured response for CROSSREF RESEARCH CITATIONS query parameter extraction.",
"attributes": {
"region": "Global",
"category": "CROSSREF",
"timestamp": "2026-10-12T05:46:16.249Z"
}
},
"timestamp": "2026-10-12T05:46:16.249Z"
}
]
curl --location --request POST 'https://zylalabs.com/api/14145/crossref+scientific+papers+and+doi+citations+scraper+api/35189/execute+crossref+scientific+papers+and+doi+citations+scraper+api' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{"query":"artificial intelligence neural networks","maxItems":20,"filterYear":"sample_value"}'
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 Crossref科学论文和DOI引用抓取器 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 🎉)
轻松从Crossref提取学术研究文章 DOI引用 作者所属单位 出版社信息 出版日期和期刊元数据 使用这个全面的爬虫API
该端点返回科学论文的详细书目信息和引用数据,包括标题、作者、出版日期、摘要、引用次数和出版商详情
响应中的关键字段包括“id”“标题”“状态”“结果”“接收的输入”和“数据”其中包含“相关性分数”“摘要”和“时间戳”等属性
响应数据结构为JSON数组,每个条目包含关于特定论文的元数据,包括其属性和相关评分,以便于解析和分析
该端点提供有关学术文章的信息,包括DOI引用、作者隶属、出版细节和期刊元数据,方便进行全面研究
用户可以通过指定“查询”、“最大项数”和“过滤年份”等参数来自定义请求,以根据他们的具体需求调整数据提取
数据来源于Crossref,这是一个广泛认可的学术内容存储库,确保所提供信息的高度准确性和可靠性
典型的用例包括将数据集成到学术数据库中 增强引用分析工具 和自动化研究项目的文献综述
用户可以分析返回的数据以识别研究中的趋势跟踪引用次数并编译书目使其成为学术和研究目的的宝贵资源