Dario Papale is Associate Professor of Forest Ecology at the University of Tuscia (Viterbo, Italy), affiliated with the National Research Council of Italy, the euroMediterranean Center on Climate Change and the Lawrence Berkeley National Laboratory in USA. His main interests are in ecosystem monitoring, in particular in relation to energy and gases exchanges at ecosystem level, and in data-model integrations in particular using empirical and data oriented models and remote sensing data. Since 2004 he is scientific responsible of the European Eddy Covariance fluxes databases cluster where he mainly works in the eddy covariance data standardization, quality control and uncertainty estimation, data policy and database services development.
In the last years his main activity and interest has been the development and coherent growth of the global network of eddy covariance sites (FLUXNET). In this context, he was one of the organizer and member of the Steering Committee of the LaThuile FLUXNET and the FLUXNET2015 synthesis activities. Since then, he continued to work on the development of new methods and techniques to enhance the quality of the eddy covariance measurements in particular in Europe and US. He is member of the AmeriFlux Management Project Team and Science Steering Committee and the National Ecological Observatory Network (NEON) Technical Working Groups. Since 2013 he is the Director of the Ecosystem Thematic Centre of the Integrated Carbon Observation System (ICOS) ESFRI Research Infrastructure in Europe.
Involved in several European and Italian research projects with responsibility roles, he is currently involved in projects focused on the FAIRness improvement in environmental data, collaboration and cross-interaction among environmental Research Infrastructure for a multi-factorial evaluation of the ecosystems and on the expansion of the environmental monitoring capabilities to urban environments.
ORCID ID: orcid.org/0000-0001-5170-8648
Research areas: ecosystem fluxes, ecosystem monitoring, remote sensing, machine learning, eddy covariance, data sharing