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The National Socio-Environmental Synthesis Center (SESYNC) brings together the science of the natural world with the science of human behavior and decision-making to find solutions to complex environmental problems. We convene science teams to work on broad issues of national and international relevance, such as water resources management, land management, agriculture, species protection, among other areas of study. By supporting interdisciplinary science teams and researchers with diverse skills, data, and perspectives, SESYNC seeks to lead in-depth research and scholarship that will inform decisions and accelerate scientific discovery. SESYNC is funded by an award to the University of Maryland from the National Science Foundation. Learn more about SESYNC.

Plants & Pests

Check out our newest documentary video, Plants & Pests, part of SESYNC's Research in Action video series.


Networks-of-Networks Workshop

Pre-register now to join us via livestream for the International Networks-of-Networks Workshop on Spetember 12-13, 2019

SESYNC Networks-of-Networks Workshop Rescheduled

The National Socio-Environmental Synthesis Center has rescheduled the 1.5-day workshop for September 12-13, 2019. The workshop will be held at SESYNC’s facilities in Annapolis, Maryland. SESYNC will provide funding for travel, lodging, and meals for on-site participants, and the workshop will also be web streamed for additional remote participants.

You were accepted to attend the workshop before it was rescheduled.

SESYNC Invites Proposals for Interdisciplinary Team-Based Research


SESYNC Invites Proposals for Collaborative & Interdisciplinary Team-Based Research Projects

Applications for both Pursuits and Workshops are due May 15, 2019 at 5pm ET.

The National Socio-Environmental Synthesis Center (SESYNC) requests proposals for collaborative and interdisciplinary team-based research projects under two programs: Pursuits and Workshops.

2019 Bayesian Modeling for Socio-Environmental Data Short Course


Solutions to pressing environmental problems require understanding connections between human and natural systems. Analysis of these systems requires a model that can deal with complexity, is able to exploit data from multiple sources, and is honest about the uncertainty from multiple sources. Synthesis of results from multiple studies is often required. Bayesian hierarchical models provide a powerful approach to analysis of socio-environmental problems.


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