PNNL partners with the Department of Homeland Security, industry, and the first-responder community to improve the effectiveness and safety during biodetection responses.

The Community Emissions Data System (CEDS) provides historical emissions data are used both for general analysis and assessment and also for model validation through comparisons with observations.

PNNL is contributing analytics expertise to a first-of-its-kind electric vehicle carsharing program in rural Hood River, Oregon.

The Data-Model Convergence (DMC) Initiative is a multidisciplinary effort to create the next generation of scientific computing capability through a software and hardware co-design methodology.

GODEEEP is an internal PNNL Agile investment inspired that addresses U.S. priorities related to clean energy and environmental and energy equity.

The Joint Global Change Research Institute conducts research to advance fundamental understanding of human and Earth systems and provide decision-relevant information for management of emerging global risks and opportunities.

The Molecular Observation Network is a national open science network designed to produce a comprehensive database of molecular and microstructural information on soil, water, microbial communities, and biogenic emissions.

PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.

The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.

The SEAHORCE project aims to improve the ability of the Energy Exascale Earth System Model to model oceans at higher fidelity than currently possible.

PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.

Visual Sample Plan (VSP) is a software tool that supports the development of a defensible sampling plan based on statistical sampling theory and the statistical analysis of sample results to support confident decision making.

The Water Cycle and Climate Extremes Modeling (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change through foundational research using models, observations, and novel numerical experiments.