Tanzania Science News API

Get the live top science headlines from Tanzania with one request to the Top Headlines endpoint of GNews API. Headlines are based on Google News rankings and returned as JSON.

Get API key

Request and live response

This request returns the current top science headlines from Tanzania. The response below is live: it was fetched when this page loaded. Every parameter is described in the Top Headlines endpoint documentation.

GET https://gnews.io/api/v4/top-headlines?category=science&country=tz&apikey=YOUR_API_KEY

curl --get "https://gnews.io/api/v4/top-headlines" \
  --data-urlencode "category=science" \
  --data-urlencode "country=tz" \
  --data-urlencode "apikey=YOUR_API_KEY"

import requests

API_KEY = "YOUR_API_KEY"

response = requests.get(
    "https://gnews.io/api/v4/top-headlines",
    params={"category": "science", "country": "tz", "apikey": API_KEY},
    timeout=10,
)
response.raise_for_status()

for article in response.json()["articles"]:
    print(article["title"])

const API_KEY = "YOUR_API_KEY";

const params = new URLSearchParams({
    category: "science",
    country: "tz",
    apikey: API_KEY,
});

const response = await fetch(`https://gnews.io/api/v4/top-headlines?${params}`);
if (!response.ok) {
    throw new Error(`Request failed: ${response.status}`);
}

const { articles } = await response.json();
for (const article of articles) {
    console.log(article.title);
}

On the Free plan, browsers can only call the API from localhost: see Using the API in the FAQ.

<?php

$apiKey = "YOUR_API_KEY";

$params = http_build_query([
    "category" => "science",
    "country"  => "tz",
    "apikey"   => $apiKey,
]);

$response = file_get_contents("https://gnews.io/api/v4/top-headlines?" . $params);
if ($response === false) {
    throw new RuntimeException("Request failed");
}

$data = json_decode($response, true);
foreach ($data["articles"] as $article) {
    echo $article["title"] . PHP_EOL;
}

using System.Net.Http.Json;

const string ApiKey = "YOUR_API_KEY";

using var client = new HttpClient();

var url = $"https://gnews.io/api/v4/top-headlines?category=science&country=tz&apikey={ApiKey}";
var data = await client.GetFromJsonAsync<ApiResponse>(url)
           ?? throw new InvalidOperationException("Empty response");

foreach (var article in data.Articles)
{
    Console.WriteLine(article.Title);
}

record ApiResponse(List<Article> Articles);
record Article(string Title);

200 OK · Live JSON response

{
    "totalArticles": 2863,
    "articles": [
        {
            "id": "435c00c24d9968e20f40af2a92671765",
            "title": "A geospatial approach to measuring economic activity",
            "description": "We introduce a methodology to detect and measure economic activity using geospatial data and apply it to steel production, a major industrial pollution source worldwide. Combining plant output data with geospatial data, such as ambient air pollutants, nighttime lights, and temperature, we train machine learning models to predict plant locations and output. We identify about 88% (94%) of plants missing from the training sample within a 1 km (5 km) radius and achieve R2 above 0.8 for output prediction at a 1 km grid and at the plant level, as well as both regional and time series validations. We use the model trained on Chinese steel data to reliably detect the location and measure the total annual output of steel plants in South Korea and Iran. Our approach can be adapted to other industries and regions, and used by policymakers and researchers to track and measure industrial activity in near real time. Satellite signals of pollution, heat and night lights reveal where steel plants operate and how much they produce. Trained on Chinese plants, the method transfers to other countries, tracking industry in near real time.",
            "content": "We thank Andrew Bernard, Adel Daoud, Tianyu Fan, Kei Irizawa, Amit Khandelwal, Yuansen Li, Jennifer Marlon, Mushfiq Mobarak, Lance Pangilinan, Joseph Shapiro, Sam Kortum, Tillmann von Carnap, and Hyunjoo Yang, as well as seminar participants at Yale ... [1784 chars]",
            "url": "https://www.nature.com/articles/s41467-026-78786-1?error=cookies_not_supported&code=3c018b65-beb9-4afa-9f0f-3c8fcd77b171",
            "image": "https://www.nature.com/oscar-static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-11-03T18:48:25Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        },
        {
            "id": "65c4c0ea269f0920d39b38b1f7329b0a",
            "title": "Scientists say comet theory on the origin of life might be wrong",
            "description": "The popular theory that comets were crucial in kick-starting life on Earth billions of years ago may not hold up, according to new computer simulations.",
            "content": "The popular theory that comets were crucial in kick-starting life on Earth billions of years ago may not hold up, according to new computer simulations that counter decades of scientific thinking.\nRelated video above: NASA identifies 3,000 comets ove... [6412 chars]",
            "url": "https://www.gulfcoastnewsnow.com/article/scientists-say-decades-old-theory-on-the-origin-of-life-might-be-wrong-space/74323073",
            "image": "https://kubrick.htvapps.com/htv-prod/images/b02b1456-4f0f-475c-a33d-7257f8af37dc.jpg?crop=1.00xw%3A0.769xh%3B0%2C0.0852xh&resize=1200%3A*",
            "publishedAt": "2026-11-03T18:45:00Z",
            "lang": "en",
            "source": {
                "id": "9dbbd74291cee645783f56df0d2d5cb7",
                "name": "Gulf Coast News and Weather",
                "url": "https://www.gulfcoastnewsnow.com"
            }
        },
        {
            "id": "14cd56977193bfbe865d74bbca971483",
            "title": "A geospatial approach to measuring economic activity",
            "description": "We introduce a methodology to detect and measure economic activity using geospatial data and apply it to steel production, a major industrial pollution source worldwide. Combining plant output data with geospatial data, such as ambient air pollutants, nighttime lights, and temperature, we train machine learning models to predict plant locations and output. We identify about 88% (94%) of plants missing from the training sample within a 1 km (5 km) radius and achieve R2 above 0.8 for output prediction at a 1 km grid and at the plant level, as well as both regional and time series validations. We use the model trained on Chinese steel data to reliably detect the location and measure the total annual output of steel plants in South Korea and Iran. Our approach can be adapted to other industries and regions, and used by policymakers and researchers to track and measure industrial activity in near real time. Satellite signals of pollution, heat and night lights reveal where steel plants operate and how much they produce. Trained on Chinese plants, the method transfers to other countries, tracking industry in near real time.",
            "content": "We thank Andrew Bernard, Adel Daoud, Tianyu Fan, Kei Irizawa, Amit Khandelwal, Yuansen Li, Jennifer Marlon, Mushfiq Mobarak, Lance Pangilinan, Joseph Shapiro, Sam Kortum, Tillmann von Carnap, and Hyunjoo Yang, as well as seminar participants at Yale ... [1784 chars]",
            "url": "https://www.nature.com/articles/s41467-026-78786-1?error=cookies_not_supported&code=ebdbe109-7a3e-422f-b4e5-6fcf09315117",
            "image": "https://www.nature.com/oscar-static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-11-03T18:45:00Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        }
    ]
}

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Tanzania Science News API

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Request and live response