<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Datasets - Category - Zhiverse</title><link>https://yao-chih.netlify.app/en/categories/datasets/</link><description>Datasets - Category - Zhiverse</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>yaochihh@gmail.com (Josh)</managingEditor><webMaster>yaochihh@gmail.com (Josh)</webMaster><copyright>Zhiverse</copyright><atom:link href="https://yao-chih.netlify.app/en/categories/datasets/" rel="self" type="application/rss+xml"/><item><title>Historical Typhoon Data Download for Taiwan</title><link>https://yao-chih.netlify.app/en/historical-typhoon-data-download-for-taiwan/</link><pubDate>Mon, 13 Jul 2026 11:03:14 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/historical-typhoon-data-download-for-taiwan/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://Josh-test-lab.github.io/posts/Historical%20Typhoon%20Data%20Download%20for%20Taiwan/cover%20image.jpg" referrerpolicy="no-referrer">
            </div><p>The cover image is a downloaded typhoon satellite image. It is a satellite water vapor cloud image produced by the Central Weather Administration (CWA) using CCU/SSL. The image was captured at 07:10 on July 11, 2026, and was obtained from the Weather Image Archive of the Department of Atmospheric Sciences at Chinese Culture University (Atmospheric Science Research and Application Databank).</p>
<p>This is a small program that was originally written to download typhoon satellite images and related data. Its initial purpose was to collect image data for image recognition research. Since the program was developed at an earlier stage, it still contains several relatively immature implementation approaches. Therefore, this article serves only as a record and reference for the data download process, and does not provide an in-depth discussion of the program architecture or optimization methods. The programming language used in this project is Python.</p>]]></description></item><item><title>Global Spatiotemporal Data: Exploring and Downloading Resources from NASA GES DISC</title><link>https://yao-chih.netlify.app/en/global-spatiotemporal-data-exploring-and-downloading-resources-from-nasa-ges-disc/</link><pubDate>Sat, 29 Nov 2025 21:53:36 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/global-spatiotemporal-data-exploring-and-downloading-resources-from-nasa-ges-disc/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Global%20Spatiotemporal%20Data%20Exploring%20and%20Downloading%20Resources%20from%20NASA%20GES%20DISC/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>GES DISC is a scientific data center under NASA that preserves Earth science–related data and provides free access to all users, making it an excellent source for obtaining spatiotemporal data.</description></item></channel></rss>