more than a network visualization tool.
DATA
formats: use py2mappr to render fully interactive networks from simple csv files of node and links.
link generation: use tag2network to build a similarity network from a list of nodes with associated tags (e.g. documents with keywords)
media: nodes can host images, video, Twitter and Instagram feeds, and music streams
DESIGN
styling: easily style the appearance of nodes, links, and node labels
layouts: reveal patterns in network structure with scatterplot and geospatial layouts
slides: save custom views and layouts with seamless animated transitions between them.
publish: share a fully interactive network by publishing openmappr files to any static website.
DISCOVERY
search/filter: code-free data exploration with dynamic filters and search tools to query nodes by any combination of node characteristics.
perspective: effortlessly zoom in to node details and zoom out to see them in the broader network context.
understanding: see summaries of node characteristics - both for the entire network and selected groups of nodes.
openmappr is open-source
interested in networks, data visualization, or discovery interfaces?
we could sure use your help.
The Ecological Structure of Collaboration
(mappr’s 1st debut at TED 2014)
“Mappr” was originally created by Vibrant Data Inc. with Kaustuv DeBiswas, David Gurman, Rich Williams, Sundev Lohr, Aditya Vishwakarma, Bimal Kumar, Moumita Sen, and Eric Berlow. Vibrant Data was acquired by Rakuten in 2016 and mappr was subsequently open-sourced. Huge thanks to additional contributions from Yale Fox, Jesse Russell, and Mike Tulubaev with Sharp Developers.
Python scripts for rendering beautiful, interactive network maps from simple csv files of nodes and links can be found at py2mappr (many thanks to Kaustuv DeBiswas for leading this effort).
The output files of py2mappr leverage the public openmappr-player data interface which allows you to either view interactive networks locally or share them with others by publishing the data to any static web server (e.g. AWS S3 bucket).
Python scripts for generating a “similarity network” from items with associated tags (e.g., documents with keywords) can be found at Tag2Network (many thanks to Rich Williams for leading this effort).
