Graph Commons workshops focus on the use of complex networks through mapping and visual analysis in order to expand your thinking about the network as a creative and critical medium. Workshop participants use the Graph Commons platform for collaborative mapping, analyzing, and publishing data networks. The workshop asks how to extract models from complex systems, how to read those networks with methods such as graph analysis, and also includes practice-based work sketching diagrams, drawing graphs, and more. As a workshop participant, you gain creative skills to answer your complex data questions, which would then inform your decisions.
Graph Commons workshops are designed and delivered by a team of experts with many years of experience creating, maintaining and supporting data projects around the world.
Please contact firstname.lastname@example.org for workshop inquiries.
Workshop needs: Projector, Internet connection, participant laptops, large paper, color pen, large drawing surface, and wall space to put up the drawings and presentations. Works best with max 15-20 participants.
New York, US, Feb 1-8, 2017
Middlesbrough, UK, Sep 9, 2016
Civic Use of Data Networks, United Nations Development Program (UNDP), Data for Development Results Conference
Istanbul, Turkey, Jun 8, 2016
Graph Commons Workshop. Dokuz8Haber
Izmir, Turkey, Apr 7, 2016
Berlin, Germany, Feb 5, 2016
Istanbul, Turkey, Jan 9-10, 2016
Paris, France, Jun 4, 2015
Graph Commons Workshop, 6th Moscow Biennial
Moscow, Russia, Sep 29, 2015
Istanbul, Turkey, Sep 12-13, 2015
Graph Commons Workshop, San Francisco Museum of Modern Art (SFMOMA)
San Francisco, CA, US, May 8, 2015
Kavala, Greece, Aug 28th, 2014
New York, June 5th, 2014
Marrakesh, Feb 28th, 2014
Liverpool, Feb 19th, 2014. Conducted by Ben Dalton
New York, Nov 19th, 2013
Izmit, May 8th, 2013
Sharjah, March 2013
Philadelphia, PA, March 2012
Istanbul, April 2012
Istanbul, February 2012
Network Mapping Workshop, AKD
Ankara, April 2010
Bodrum, June 2010
Network Mapping Workshop, Helsinki Citizens’ Assembly