Sponsored Lunch with Crowd4SDG

Sponsored Lunch with Crowd4SDG

Launching a Challenge for Young Citizen Scientists to Tackle Urban Water Resilience

Date and Time:
Monday, September 7th, 2020
13.30 - 14.30 CEST

Location: Event Room on Zoom

Organized by the EC project Crowd4SDG: Citizen Science for the Sustainable Development Goals

Description: The impacts of climate change include more extreme weather, leading to floods and drought that are putting urban communities under increasing stress. How can crowdsourcing and citizen science be used to monitor and address Urban Water Resilience? In this ECSA Conference sponsored lunch, the Crowd4SDG project partners, together with the social network Goodwall, the LinkedIn for GenZ, will introduce the #Open17Water challenge that they are launching on this day. Young innovators ages 16-26 who pitch the most inspired ideas over the coming month will be selected for online and in-person coaching over a six-month period provided by the Crowd4SDG partners, to develop their idea into a sustainable project.

The best projects will be invited to Geneva to present their results to UN experts and impact investors. A winning team from a previous #Open17 challenge, Digital Water from Mexico, will present the status of their project today, to illustrate what inspired students can achieve. A Q&A session will focus on connecting the #Open17Water challenge with other citizen science projects and shaping similar citizen science challenges that the Crowd4SDG project will launch over the coming two years.

The Crowd4SDG partners are: University of Geneva, European Organization for Nuclear Research (CERN), Spanish National Research Council (CSIC), Politecnico di Milano, United Nations Institute for Training and Research (UNITAR), University of Paris. The #Open17Water challenge is a collaboration with Goodwall and Citizen Science Center Zurich.

For more information about Crowd4SDG:




To apply for the #Open17Water challenge: www.goodwall.io/tags/open17water

The Crowd4SDG project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 872944