The field of environmental sciences has drastically changed over the past decades. A nature-society dichotomy in human-environment studies is increasingly giving way to interdisciplinary systems thinking, acknowledging the limitations of simplistic unidirectional relationships and accounting for interwoven interactions that change through space and time. This shift has coincided with, and was facilitated by, rapid methodological developments; Big (geographic) data has huge potential in contributing to the unravelling of complex questions at the hearth of human-environment interactions. A first wave of advancements occurred through the use of GIS and remote sensing: multidimensional representations of the world we live in allow us to better encompass complexities intrinsic to the environment. In recent years, pioneered by biogeographers, science also has become more inclusive, in the form of crowdsourcing and citizen science. Citizen science is starting to develop as a discipline on its own, aiming to do science in and with society.
Collectively, these evolutions have also shaped my own research. Over the past years, I have developed an expertise in environmental risk assessment. From geo-spatial modelling of natural hazards (D-SIRE and HARISSA projects) to understanding the spatiotemporal dynamics of vector borne diseases (ATRAP, SNIS) and most recently, the release of microfibers into the environment (META): the research I conduct on environmental risks is characterized by a transdisciplinary research design, integrating geo-spatial modelling with novel data sources, including very high resolution remote sensing and citizen science.

Citizen Science (CS) - the practice where members of the general public conduct or participate in scientific research - is increasingly recognized for its contributions to scientific advancements, addressing wicked problems, monitoring Sustainable Development Goals (SDGs), and influencing policy. These expectations stem from two perspectives: the Productivity view, emphasizing CS’s benefits for scientific practice (e.g., mobilizing resources, scaling data collection), and the Democratization view, highlighting its potential to bridge the science-society gap and benefit communities. Integrating these perspectives is key to tackling complex sustainability challenges.

However, this integration presents potential tradeoffs in research design. Yet, research on best practices remains rare, making it difficult to systematically advance the science of citizen science. For example, within the Productivity view critiques on data quality persist, while the Democratization view is challenged by the exclusive nature of some CS initiatives, limiting accessibility and inclusivity. Despite these challenges, limited literature addresses CS tradeoffs or its interdisciplinary methodological complexities. Many still perceive CS as supplementary rather than integral to scientific inquiry and societal change. However, CS holds substantial scientific and societal potential, requiring targeted research efforts that enhance methodological rigor, inclusivity, and impact measurement to fully realize its promise.

In my research I take on an interdisciplinary perspective on citizen science, with a particular focus on better understanding:

- data quality assessment methods and modelling implications

- citizen motivation: testing of methodological frameworks

- citizen science as a way to leverage sustainability transformations

- inclusive citizen science