Search science topics

Code snippets

curl "https://api.oanor.com/openalex-api/v1/topics" \
  -H "x-oanor-key: oanor_test_..."

await fetch("https://api.oanor.com/openalex-api/v1/topics", {
  headers: { "x-oanor-key": "oanor_test_..." }
});

$ch = curl_init("https://api.oanor.com/openalex-api/v1/topics");
curl_setopt($ch, CURLOPT_HTTPHEADER, ["x-oanor-key: oanor_test_..."]);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$out = curl_exec($ch);

import requests
requests.get(
    "https://api.oanor.com/openalex-api/v1/topics",
    headers={"x-oanor-key": "oanor_test_..."}
)

Example response

A real response from this endpoint, captured by the latest health check.

{
    "data": {
        "page": 1,
        "count": 20,
        "query": "machine learning",
        "total": 118,
        "topics": [
            {
                "id": "T11948",
                "name": "Machine Learning in Materials Science",
                "field": "Materials Science",
                "domain": "Physical Sciences",
                "subfield": "Materials Chemistry",
                "description": "This cluster of papers focuses on the application of materials informatics, machine learning, and high-throughput computational techniques to accelerate materials innovation. It encompasses topics such as property predictions, crystal structures, molecular dynamics, and data mining in the context of materials science and engineering.",
                "works_count": 138533,
                "cited_by_count": 1017249
            },
            {
                "id": "T12254",
                "name": "Machine Learning in Bioinformatics",
                "field": "Biochemistry, Genetics and Molecular Biology",
                "domain": "Life Sciences",
                "subfield": "Molecular Biology",
                "description": "This cluster of papers focuses on the prediction of protein subcellular localization using various computational methods such as amino acid composition, machine learning algorithms like support vector machines, and the analysis of signal peptides and transmembrane topology. The research aims to improve the accuracy and reliability of predicting the subcellular location of proteins, which has significant implications for understanding protein function and cellular processes.",
                "works_count": 277495,
                "cited_by_count": 714356
            },
            {
                "id": "T12072",
                "name": "Machine Learning and Algorithms",
                "field": "Computer Science",
                "domain": "Physical Sciences",
                "subfield": "Artificial Intelligence",
                "description": "This cluster of papers revolves around the topic of active learning in machine learning research. It covers various aspects such as semi-supervised learning, deep learning, Gaussian processes, image classification, text categorization, batch mode active learning, statistical guarantees, and human-in-the-loop approaches.",
                "works_count": 35936,
                "cited_by_count": 661075
            },
            {
                "id": "T12535",
                "name": "Machine Learning and Data Classification",
                "field": "Computer Science",
                "domain": "Physical Sciences",
                "subfield": "Artificial Intelligence",
                "description": "This cluster of papers focuses on the challenges and techniques for learning with noisy labels in machine learning, including methods for hyperparameter optimization, instance selection, robust learning, and automated machine learning. It also explores the use of meta
…