Working Groups | ESIIL

CONDUCT TEAM SCIENCE

Working groups are self-organized research teams focused on well-defined scientific questions that advance environmental data science. A single working group may have up to 15 participants and a quorum (50% or more) shall meet in person up to 2 times over a 2 year period, with each meeting lasting between 3 and 5 days.

ESIIL's Second Cohort of Working Groups

popClimVar: Climatic variability and fluctuating populations

Climate and pop variability

Environmental conditions play a key role in shaping ecological patterns, from species diversity to the variability in sp...

Observing Unci Maka: Environmental Data Science on Tribal Lands

Observing Unci Maka

This working group brings together Tribal members, scientists, students and NASA remote-sensing researchers to formulate...

Developing Best Practices for Large Language Models in Environmental Science

EnviroLLM Guidelines

Environmental challenges are increasingly complex and pressing, requiring rigorous and rapid synthesis of broad bodies o...

Integrating Urban Biodiversity Data on Non-native Plant Species with Environmental and Socioeconomic Factors in Cities Across the Globe

Urban Species Invasions

Urbanized landscapes increasingly dominate the Earth’s surface and represent concentrations of human activity and impact...

VERDE: Vast Exploration and Research across Dry Forest Ecosystems

Dry Forest Phenology

We propose Vast Exploration and Research in Dry Forest Ecosystems (VERDE), a working group to harness environmental data...

ESIIL's First Cohort of Working Groups

EDS for Maka Sitomniya

Maka Sitomniya: Preserving Mother Earth by Asserting Lakota Sovereignty in Earth Data Science

Macrophenology

Linking Phenological Change to Range Change in North American Plant Species

BioViewPoint

Leveraging Biodiversity Data to Explore Ecosystem Dynamics

Modeling Extreme Wildfires

Interdisciplinary Approaches to Modeling Extreme Wildfire Events

Extreme wildfire events are increasing, driven by the expansion of urban communities closer to forests, land management ...

AI for Natural Methane

Harmonizing Natural Methane Datasets using Knowledge Guided Machine Learning

Atmospheric methane (CH4) is the second most powerful greenhouse gas after carbon dioxide and grew at the fastest rate e...

Fungal dispersal traits

Advancing Fungal Dispersal Ecology through Traits and Data Harmonization

Fungi have captured the imagination of the public for their pivotal roles in ecosystems, the parts they play in world fo...