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glueviz v0.13.1: multidimensional data exploration

Robitaille, Thomas; Beaumont, Chris; Qian, Penny; Borkin, Michelle; Goodman, Alyssa


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1237692", 
  "title": "glueviz v0.13.1: multidimensional data exploration", 
  "issued": {
    "date-parts": [
      [
        2017, 
        2, 
        15
      ]
    ]
  }, 
  "abstract": "<p>Glue is a Python library to explore relationships within and among related datasets. Its main features include:</p>\n\n<ul>\n\t<li><strong>Linked Statistical Graphics.</strong>&nbsp;With Glue, users can create scatter plots, histograms and images (2D and 3D) of their data. Glue is focused on the brushing and linking paradigm, where selections in any graph propagate to all others.</li>\n\t<li><strong>Flexible linking across data.</strong>&nbsp;Glue uses the logical links that exist between different data sets to overlay visualizations of different data, and to propagate selections across data sets. These links are specified by the user, and are arbitrarily flexible.</li>\n\t<li><strong>Full scripting capability.</strong>&nbsp;Glue is written in Python, and built on top of its standard scientific libraries (i.e., Numpy, Matplotlib, Scipy). Users can easily integrate their own python code for data input, cleaning, and analysis.</li>\n</ul>\n\n<p>In addition to the authors above, the full list of contributors to glue can be found at https://github.com/glue-viz/glue/graphs/contributors</p>", 
  "author": [
    {
      "family": "Robitaille, Thomas"
    }, 
    {
      "family": "Beaumont, Chris"
    }, 
    {
      "family": "Qian, Penny"
    }, 
    {
      "family": "Borkin, Michelle"
    }, 
    {
      "family": "Goodman, Alyssa"
    }
  ], 
  "version": "0.13.1", 
  "type": "article", 
  "id": "1237692"
}
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