Workshop aims: The aim of the workshop was to contribute to the co-creation of the EU-Citizen.Science online platform, which will serve as a mutual learning space where citizen science practitioners and participants can exchange experiences and successful strategies. Participants (see Annex I) were invited to share their expectations for the platform, and to contribute their expertise by identifying potential features and functionalities. The workshop also aimed to identify potential collaborations between the ongoing COST Action CA15212 and the EU-Citizen.Science platform, as well as exploring collaborations with current citizen science projects, networks and initiatives.
The ultimate goal of the workshop was to identify benefits, challenges and recommendations from a large number of different stakeholders (from the scientific, policy and citizens perspectives) - with a view to improve developments, promoting and accelerating the use of Citizens Science for (environment-related) policy making throughout Europe.
This document reports the structure and the main outputs of the training course “Where science meets society: citizen science as an emerging tool to expand research horizons” held in Erice (Italy) from the 26th November to the 1st December 2018.
Citizen Science Strategies in Europe preliminary findings from the pan-European Survey of Citizen Science Strategies and initiatives in Europe as part of a joint initiative of the COST ACTION 15212 and the JRC discussed in Cēsis, Latvia, June 4th 2019
Context and Aim of the Workshop: This workshop was organised by the JRC work packages DigiTranScope, CSData and HUMAINT, in collaboration with the COST Action Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe. The workshop was the first event within this COST Action devoted to the interaction between machine learning and action learning in citizen science, and aimed to raise awareness about opportunities and issues emerging from this interrelation.
The main objectives of the training school were to prepare the trainees to understand the main aspects of spatial data quality, experience the pros and cons of the available tools to collect and edit OpenStreetMap (OSM) data (by using both image interpretation and in the field experience), use available tools to help identify data quality issues and errors in existing OSM data, and subsequently how to fix these problems and learn how to organize and structure OSM mapping events which supports the collection of high-quality data.