Figures for publication: Data demands to be presented beautifully
Science cannot do without diagrams and figures. Getting better visualization of scientific data provides better graphical interface between reader and the data.One should tailor the images to the audience, i-e not depict only few direct steps and skip details if not intending it for experts only. Fine tune images with a suitable colour choice. Scaling should be checked. Avoid chartjunk or visual pollution. One can break image into chunks to make convenient for audience to capture details.Right selection of tools is of utmost importance. captions should be a must and resolution adjust to the best dpi possible. Most journals demand from 600 dpi-1000 dpi.
Some journals even provide tools for graphical check before submission as RapidInspector (http://rapidinspector.cadmus.com/zwi/index.jsp) by the Journal 'Protein: structure, function and bioinformatics.
Some resources of my choice are mentioned below:
D3.js: D3 for Data-Driven Documents is a JavaScript library that helps create and control interactive data-based graphical forms that can be run in web browsers, examples are shown in the gallery at http://github.com/mbostock/d3/wiki/Gallery.
GIMP:GNU Image Manipulation Program is an excellent application for tasks as photo retouching, image composition, and image authoring.
Matplotlib:a python plotting library, primarily for 2-D plotting but with some 3-D support, produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. It comes with a huge gallery of examples that cover virtually all scientific domains (http://matplotlib.org/gallery.html).
R : a powerful language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.
Inkscape:professional vector graphics editor. It allows you to design complex figures and can be used, for example, to improve a script-generated figure or to read a PDF file in order to extract figures and transform them any way you like.
Some journals even provide tools for graphical check before submission as RapidInspector (http://rapidinspector.cadmus.com/zwi/index.jsp) by the Journal 'Protein: structure, function and bioinformatics.
Some resources of my choice are mentioned below:
D3.js: D3 for Data-Driven Documents is a JavaScript library that helps create and control interactive data-based graphical forms that can be run in web browsers, examples are shown in the gallery at http://github.com/mbostock/d3/wiki/Gallery.
GIMP:GNU Image Manipulation Program is an excellent application for tasks as photo retouching, image composition, and image authoring.
Matplotlib:a python plotting library, primarily for 2-D plotting but with some 3-D support, produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. It comes with a huge gallery of examples that cover virtually all scientific domains (http://matplotlib.org/gallery.html).
R : a powerful language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.
Inkscape:professional vector graphics editor. It allows you to design complex figures and can be used, for example, to improve a script-generated figure or to read a PDF file in order to extract figures and transform them any way you like.
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