The VAST Challenge 2019 presents three mini-challenges and a grand challenge for you to apply your visual analytics research and technologies to help a city grapple with the aftermath of an earthquake that damages their nuclear power plant. These challenges are open to participation by individuals...

The EyeSea underwater video dataset was assembled for developing algorithms for detecting fish in real world underwater video data. The data were recorded as part of environmental monitoring efforts at three different water power sites.

This project is an interdisciplinary collaboration supported by US DOE Office of Science's Scientific Discovery through Advanced Computing (SciDAC) program. The project addresses a crucial but largely overlooked source of error in the Energy Exascale Earth System Model (E3SM) and other atmosphere...

This data is a model of synthetic adversarial activity surrounded by noise and was funded by DARPA. The various versions include gradually more complex networks of activities.

The Biomedical Resilience & Readiness in Adverse Operating Environments (BRAVE) Project develop new capabilities to improve health and performance of first responders in adverse operating environments common to national defense. The BRAVE project analyze biological samples, collect physiological...

Last updated on 2023-02-23T19:37:46+00:00 by LN Anderson PerCon SFA Project Publication Experimental Data Catalog The Persistence Control of Engineered Functions in Complex Soil Microbiomes Project (PerCon SFA) at Pacific Northwest National Laboratory ( PNNL ) is a Genomic Sciences Program...

The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...

The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...

Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...

Predictive Phenomics is addressing the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems .

The Human Islet Research Network (HIRN) is a large consortia with many research projects focused on understanding how beta cells are lost in type 1 diabetics (T1D) with a goal of finding how to protect against or replace the loss of functional beta cells. The consortia has multiple branches of...

Michelle Davison is a microbiologist with a love of challenging, creative, and multidisciplinary projects. She is interested in the role phage play in environmental systems, as well as the myriad ways they can be harnessed as tools. She has experience isolating, developing and working with non-model...

Washington State University Distinguished Graduate Research Program Program: Chemical Engineering WSU-PNNL Advisor: Aaron Wright

Colin works in microbial ecology, focusing on informatics and visualization. Education: BS - Biology and Informatics - Juniata College

Publications: 2018 Khan NE, Y Maezato, RS McClure, CJ Brislawn, JM Mobberley, NG Isern, WB Chrisler, LM Markillie, BM Barney, HS Song, WC Nelson, and HC Bernstein. 2018. "Phenotypic responses to interspecies competition and commensalism in a naturally-derived microbial co-culture." Scientific...