This 10-day ICOS-NEON greenhouse gas data training workshop will train early career scientists (including advanced PhD students, postdocs, and Junior Faculty) in the discovery and use of in-situ data to address emerging issues in carbon cycle science including atmospheric science, biogeochemistry and ecosystem science. World-class scientists will provide hands-on instruction in the use of ‘big data' from the ICOS and NEON observatories while discussing the frontier of carbon science and promoting the discovery of new research opportunities.

ICOS and NEON research infrastructures are in-situ observation networks providing research data on greenhouse gas fluxes from ecosystems to the atmosphere. Together, ICOS and NEON aim to make these data available without technical, scientific or political barrier. These data typically include greenhouse gas (GHG) concentration, carbon and energy flux observations, and the surface micrometeorology surrounding these measurements. NEON is solely funded by the U.S. National Science Foundation (NSF).

Speakers include:

Steve Wofsy, Harvard, US
Euan Nisbet, RHUL, UK
David Lowry, RHUL, UK
Hank Loescher, Neon Inc., US
Marcel van Oijen, CEH, UK
Eric Ceschia, CESBIO UPS, France
Phil DeCola, Sigma Space, US
Andy Fox, NEON, US
Christina Staudhammer, University of Alabama, US
Greg Starr, University of Alabama, US
Felix Vogel, LSCE, France
Rebecca Koskela, DataONE, US
Philippe Peylin, LSCE, France
Sara Vicca, Uni Antwerp, Belgium

Workshop date: June 2-12, 2015

Registration Deadline (extended): 22 April 2015, with statement of interest plus CV and letter of support

- Please read the section "How to apply?" before starting the registration process. -

Costs: no fee, shared lodging and meals included

Language: English

Prerequisites for participation:
Knowledge of computer environment, at least one scientific programming language (e.g. R, Matlab, Fortran, ...) and elementary knowledge of the present-day carbon cycle. Participants need to be sufficiently proficient in English.

Online user: 1