Visualization   272835

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Graphviz and Org Mode | Irreal
Over at The Joy of Programming there’s a nice introductory post on using Graphviz’s dot command to draw graphs and how you can make the process almost interactive by calling dot from within an Org source block. The dot command language is pretty straightforward, at least for relatively simple graphs, so it’s easy to learn.
emacs  org-mode  graphviz  visualization  data 
14 hours ago by overbey
Qlik Playground
Qlik Playground is a free programming environment that allows you to use, explore, and quickly test your most complex and innovative data driven application ideas using Qlik technology. Built for developers by developers, Playground gives you one-of-a-kind access to Qlik’s Engine and APIs.
tools  data  visualization  qlik 
18 hours ago by cyberchucktx
After the flood | Projects | AtF Spark
"AtF Spark is a font that allows for the combination of text and visual data to show an idea and evidence in one headline. This builds on the principle of Sparklines defined by Edward Tufte and makes them easier to use. Sparklines are currently available as plugins or javascript elements. By installing the Spark font you can use them immediately without the need for custom code."
font  design  data  visualization 
19 hours ago by kasei
jebberjeb/viz.cljc: Generate images from Graphviz dot strings in Clojure and Clojurescript
This library provides one interface viz.core/image for both Clojure and Clojurescript. For Clojure, the dependency on the Graphviz dot binary is not necessary (as it is with other libraries). For Clojurescript, this library eliminates the need to separately include Viz.js.

Everything in this library is self contained, as it includes and uses Viz.js. This also ensures complete consistency (formatting nuances, etc) between Clojure and Clojurescript.
graphviz  programming  Clojure  library  visualization  ClojureScript 
yesterday by Vaguery
Accurate Genomic Prediction Of Human Height | bioRxiv
Stephen Hsu's compressed sensing application paper

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.
study  bio  preprint  GWAS  state-of-art  embodied  genetics  genomics  compressed-sensing  high-dimension  machine-learning  missing-heritability  hsu  scitariat  education  🌞  frontier  britain  regression  data  visualization  correlation  phase-transition  multi  commentary  summary 
yesterday by nhaliday

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