visualisation   29823

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Semiotic Data Visualization
Semiotic is a data visualization framework for React (ed: and d3). It provides three types of frames (XYFrame, ORFrame, NetworkFrame) which allow you to deploy a wide variety of charts that share the same rules for how to display information. By adjusting the settings of a frame, you can produce very different looking charts that despite their visual difference are the same in the way they model information.
chart  d3  javascript  library  visualization  dataviz  visualisation  data  react  web 
yesterday by llimllib
Remove the legend to become one
To accelerate that understanding, upgrade your line graphs to be efficient and truthful. Some broadly applicable principles should guide you to the right neighborhood. To recap:

● Don't include a legend; instead, label data series directly in the plot area. Usually labels to the right of the most recent data point are best. Some people argue that a legend is okay if you have more than one data series. My belief is that they're never needed on any well-constructed line graph.
● Use thousands comma separators to make large figures easier to read
● Related to that, never include more precision than is needed in data labels. For example, Excel often chooses two decimal places for currency formats, but most line graphs don't need that, and often you can round to 000's or millions to reduce data label size. If you're measuring figures in the billions and trillions, we don't need to see all those zeroes, in fact it makes it harder to read.
● Format axis labels to match the format of the figures being measured; if it's US dollars, for example, format the labels as currency.
● Look at the spacing of axis labels and increase the interval if they are too crowded. As Tufte counsels, always reduce non-data-ink as much as possible without losing communicative power.
● Start your y-axis at zero (assuming you don't have negative values)
Try not to have too many data series; five to eight seems the usual limit, depending on how closely the lines cluster. On rare occasion, it's fine to exceed this; sometimes the sheer volume of data series is the point, to show a bunch of lines clustered. These are edge cases for a reason, however.
● If you have too many data series, consider using small multiples if the situation warrants, for example if the y-axes can match in scale across all the multiples.
● Respect color blind users and those who may not be able to see your charts with color, for example on a black and white printout, and have options for distinguishing data series beyond color, like line styles. At Amazon, as I dealt with so many figures, I always formatted negative numbers to be red and enclosed in parentheses for those who wouldn't see the figures in color.
● Include explanations for anomalous events directly on the graph; you may not always be there in person to explain your chart if it travels to other audiences.
● Always note, usually below the graph, the source for the data.

Some other suggestions which are sometimes applicable:

● Display actual data values on the graph if people are just going to ask what the figures are anyway, and if they fit cleanly. If you include data labels, gridlines may not be needed. In fact, they may not be needed even if you don't include data labels.
● Include targets for figures as asymptotes to help audiences see if you're on track to reach them.
by:EugeneWei  visualisation  EdwardTufte 
yesterday by owenblacker
Why do eggs have so many shapes?
Not all eggs are shaped like a chicken's--now we know why
biology  data  eggs  science  visualisation  inspiration 
2 days ago by garrettc
Open source tools to visualise data without code.
art  data  programming  software  visualisation  graphics  opensource 
2 days ago by garrettc
Making of Off the Staff
In depth look at the tools and ideas that went into making visualisations of music scores.
music  data  visualisation  designthinking  inspiration 
2 days ago by garrettc
jsPlumb Toolkit - build Flowcharts, Diagrams and connectivity based applications fast
The jsPlumb Toolkit is an advanced, standards-compliant and easy to use library for building Javascript connectivity based applications, such as flowcharts, process flow diagrams, sequence diagrams, organisation charts - anything you can think of. Easily integrate with Angular, React or Vue, or just use Vanilla JS.
javascript  graph  visualisation  bezier 
3 days ago by garrettc
Kumu is a powerful data visualization platform that helps you organize complex information into interactive relationship maps.
visualisation  software 
4 days ago by ssorc
Prototyping Interactive Data Viz: Lessons Learned in FY17 | Moosha Moosha Mooshme
Data visualization provides a natural opportunity for engaging visitors with authentic science content and cutting-edge technology
Researchers across the natural sciences (and across the Museum) are creating digital content that we can leverage to serve AMNH’s mission and generate interest and excitement among visitors and staff.
American_Museum_of_Natural_History  interactive  visualisation  data  science 
6 days ago by stacker
OII Network Visualisation Example
Publically viewable AS Paths between ASNs in the ARIN service region. This data was produced using RIBs from the routeviews project. Created by Dean Pemberton
BGP  visualisation  map 
7 days ago by coffeebucket
A framework for 3D geospatial visualization in the browser
visualization  visualisation  city  generator 
7 days ago by vrt

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