Joint Research Centre
Digital Earth Lab

At the current stage, our work addresses the four main areas that are listed below.

Citizen Science Data and Service Infrastructures

We help to advance the interconnectivity between Citizen Science data, tool and apps with knowledge systems of the public sector. In this way, we intend to support the development of a methodology and accompanying toolbox to support knowledge co-creation for evidence-based policy making. This work includes investigations of the possibilities to provide long term access to the results of Citizen Science projects, with a particular emphasis on interoperability, the adoption of existing practices, and the connection of EU-funded Citizen Science projects to a wider research and innovation ecosystem.

Implications of Citizen Science for EU policy-making

Under the assumption that Citizen Science can help boosting the Better Regulation Agenda we test, demonstrate and reflect on the potentials, feasibility and possible pitfalls of applying Citizen Science methodologies in order to increase public engagement in EU-level policy-making and improving social well fair. Due to the history and state of play in Citizen Science – a recent JRC survey attracted 121 Citizen Science projects, of which 100 related themselves to environmental topics - environment-related policies appear as an ideal starting point for such investigations, before extending also to other policy areas.

Innovative use of global earth observation data

Digital transformation in economy and society will continue to produce more data than ever before. We investigate some of the mechanisms and potential barriers to simulate the uptake of these data sets and to enable new ways of value creation from (big) data in order to boost the European economy. Our investigaitons are rooted in the use of Open Data that is provided by the Global Earth Observation System of Systems (GEOSS).

Knowledge extraction from new data sources

Data and technology-driven transitions in society and economy are evident but difficult to assess and predict. Similarly, Big Data has been identified as a driver for innovation and growth and the potentials of Big Data to improve policy making has been recognized, but the methods and tools for appropriate data-handling still have to be established. New methods for providing scientific data-informed evidence to decision-making (e.g. use of new integrative models and indicators) need to be developed in areas including, but not restricted to official statistics, environmental monitoring, behavioral analytics, financial markets. We run a series of experiments in order to advance our understanding of the potentials and pitfalls in using newly emerging data sources.