Metadata Search Example

{
  "request": {
    "method": "GET",
    "url": "https://ieeexploreapi.ieee.org/api/v1/search/articles",
    "description": "Search for articles about deep learning in computer vision, limited to journal publications, sorted by publication year descending, returning up to 5 records.",
    "parameters": {
      "querytext": "deep learning computer vision",
      "content_type": "Journals",
      "start_date": "20200101",
      "end_date": "20241231",
      "max_records": 5,
      "sort_field": "article_number",
      "sort_order": "desc",
      "start_record": 1,
      "apikey": "YOUR_API_KEY_HERE"
    },
    "curlExample": "curl -G 'https://ieeexploreapi.ieee.org/api/v1/search/articles' --data-urlencode 'querytext=deep learning computer vision' -d 'content_type=Journals&start_date=20200101&end_date=20241231&max_records=5&sort_order=desc&apikey=YOUR_API_KEY_HERE'"
  },
  "response": {
    "status": 200,
    "headers": {
      "Content-Type": "application/json"
    },
    "body": {
      "total_records": 3847,
      "total_searched": 6100000,
      "articles": [
        {
          "article_number": "9852847",
          "title": "Transformer-Based Deep Learning for Real-Time Object Detection in Autonomous Vehicles",
          "abstract": "This paper proposes a novel transformer-based architecture for real-time object detection applicable to autonomous driving scenarios. Our method achieves state-of-the-art performance on the KITTI and nuScenes benchmarks while maintaining inference speeds suitable for deployment on embedded hardware.",
          "doi": "10.1109/TIV.2022.3194782",
          "authors": {
            "authors": [
              {
                "full_name": "Chen, Liwei",
                "author_order": 1,
                "affiliation": "Tsinghua University",
                "author_url": "https://ieeexplore.ieee.org/author/38267491"
              },
              {
                "full_name": "Park, Sungjin",
                "author_order": 2,
                "affiliation": "KAIST",
                "author_url": "https://ieeexplore.ieee.org/author/29847123"
              }
            ]
          },
          "publication_title": "IEEE Transactions on Intelligent Vehicles",
          "publication_year": 2022,
          "publication_date": "June 2022",
          "volume": "7",
          "issue": "3",
          "start_page": "612",
          "end_page": "625",
          "content_type": "Journals",
          "issn": "2379-8858",
          "access_type": "Locked",
          "is_open_access": false,
          "publisher": "IEEE",
          "citing_paper_count": 142,
          "citing_patent_count": 3,
          "author_terms": [
            "object detection",
            "transformer architecture",
            "autonomous driving",
            "real-time inference"
          ],
          "ieee_terms": [
            "Object detection",
            "Autonomous vehicles",
            "Deep learning",
            "Transformers",
            "Computer vision"
          ],
          "pdf_url": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9852847",
          "html_url": "https://ieeexplore.ieee.org/document/9852847",
          "abstract_url": "https://ieeexplore.ieee.org/document/9852847",
          "rank": 1,
          "insert_date": "20220715"
        },
        {
          "article_number": "9734651",
          "title": "Self-Supervised Learning for Medical Image Segmentation via Deep Convolutional Networks",
          "abstract": "We present a self-supervised pre-training strategy for medical image segmentation that requires minimal labeled data. The approach leverages contrastive learning on unlabeled CT and MRI scans before fine-tuning on small annotated datasets, achieving competitive results against fully supervised baselines.",
          "doi": "10.1109/TMI.2022.3161804",
          "authors": {
            "authors": [
              {
                "full_name": "Nguyen, Anh Tuan",
                "author_order": 1,
                "affiliation": "Johns Hopkins University",
                "author_url": "https://ieeexplore.ieee.org/author/37088943"
              }
            ]
          },
          "publication_title": "IEEE Transactions on Medical Imaging",
          "publication_year": 2022,
          "publication_date": "May 2022",
          "volume": "41",
          "issue": "5",
          "start_page": "1203",
          "end_page": "1218",
          "content_type": "Journals",
          "issn": "0278-0062",
          "access_type": "Open Access",
          "is_open_access": true,
          "publisher": "IEEE",
          "citing_paper_count": 89,
          "citing_patent_count": 0,
          "author_terms": [
            "self-supervised learning",
            "medical image segmentation",
            "contrastive learning",
            "transfer learning"
          ],
          "ieee_terms": [
            "Image segmentation",
            "Medical imaging",
            "Deep learning",
            "Self-supervised learning",
            "Convolutional neural networks"
          ],
          "pdf_url": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9734651",
          "html_url": "https://ieeexplore.ieee.org/document/9734651",
          "abstract_url": "https://ieeexplore.ieee.org/document/9734651",
          "rank": 2,
          "insert_date": "20220510"
        }
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    }
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}