Working Groups | ESIIL

Building Innovative Teams

Working groups play a pivotal role in promoting the integrative approach ESIIL champions, pooling knowledge and expertise from various disciplines to tackle environmental issues with a data-driven perspective.

What are working groups?

Working groups are self-organized research teams focused on well-defined scientific questions that advance environmental data science and require insights from a broad group of researchers and other stakeholders. 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.

Updates

ESIIL will announce its next cohort of working groups in February.
The next request for proposals will open in August.

ESIIL's Second Cohort of Working Groups

Climate and Pop Variability

popClimVar: Climatic variability and fluctuating populations

Observing Unci Maka

Observing Unci Maka: Environmental Data Science on Tribal Lands

EnviroLLM Guidelines

Developing Best Practices for Large Language Models in Environmental Science

Urban Species Invasions

Integrating Urban Biodiversity Data on Non-native Plant Species with Environmental and Socioeconomic Factors in Cities A...

Dry Forest Phenology

VERDE: Vast Exploration and Research across Dry Forest Ecosystems

ESIIL's First Cohort of Working Groups

Worldwide FreshH20 Zoops

Synthesizing Patterns and Drivers of Zooplankton Community Dynamics Worldwide

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

AI for Natural Methane

Harmonizing Natural Methane Datasets using Knowledge Guided Machine Learning

Fungal Dispersal Traits

Advancing Fungal Dispersal Ecology through Traits and Data Harmonization