28 Matching Annotations
  1. Jun 2022
    1. Most of us are familiar with data visualization: charts, graphs, maps and animations that represent complex series of numbers. But visualization is not the only way to explain and present data. Some scientists are trying to sonify storms with global weather data. That could be easier to get a sense of interrelated storm dynamics by hearing them.

  2. Feb 2022
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  4. Aug 2021
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  6. Mar 2021
    1. A cool concept of displaying your life in story: Life in weeksI am reminded of this site, where I first encountered the ‘life in weeks’ idea.Other apps/sites that help you visualize or track your life:https://zrxj5vvjvl.codesandbox.io/https://jhornitzky.github.io/yolograph/demo/ - shows you what percentage of years you lived based on an average lifespan of 70 yearshttp://pewu.github.io/life-in-weeks/ - customizablehttps://lifecal.me/ - an apphttps://entire.life/ - a webapp?
  7. Oct 2020
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  11. Mar 2020
    1. An exploratory plot is all about you getting to know the data. An explanatory graphic, on the other hand, is about telling a story using that data to a specific audience.

      Exploratory vs Explanatory plot

  12. Nov 2019
  13. Mar 2019
    1. At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes — and other people may be able to learn from them, too.

      This is, factually and literally speaking, laudable in the extreme.

      Anybody can make mistakes; the best one can do is to admit that one does, and publicly learn from them - if one is a magazine. This is beauteously done.

  14. Oct 2018
    1. This page shows a graph of philosophical authors, important statements of them and with whom they agree or disagree by those.

  15. Oct 2017
  16. Sep 2017
    1. Spectral Python (SPy) is a pure Python module for processing hyperspectral image data. It has functions for reading, displaying, manipulating, and classifying hyperspectral imagery. It can be used interactively from the Python command prompt or via Python scripts
  17. Jul 2017
  18. May 2016
    1. From Bits to Narratives: The Rapid Evolution of Data Visualization Engines

      It was an amazing presentation by Mr Cesar A Hidalgo, It was an eye opener for me in the area of data visualisation, As the national level organisation, we have huge data, but we never thought about data visualisation. You projects particularly pantheon and immersion is marvelous and I came to know that, you are using D3. It is a great job