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 long-term goals of this scientific focus area (SFA) are to develop flexible and extensible modeling capabilities that capture the dynamic multiscale interactions among climate, energy, water, land, socioeconomics, critical infrastructure, and other sectors and to use these capabilities to study...

The long term goal of this scientific focus area (SFA) is to transform our understanding of climate-relevant processes and provide more robust model representations of the climate system through the integration of new knowledge on cloud and aerosol populations, and their interactions with each other...

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...

OSU-PNNL Superfund Research Program Center is part of the Superfund Research Program (SRP) at Oregon State University, directed by Dr. Robyn Tanguay, bringing together a multidisciplinary team of experts with extensive experience in polycyclic aromatic hydrocarbons (PAHs) research. Using state-of...

The VAST Contest is a participation category of the IEEE VAST Annual Symposium. It continues in the footsteps of the VAST 2006 contest as its purpose is to promote the development of benchmark data sets and metrics for visual analytics, and to establish a forum to advance visual analytics evaluation...

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.

The objective of Terrestrial-Aquatic Interface (TAI) research in PREMIS is to understand the factors governing C and nutrient movement and transformation through the TAI, and their sensitivities to inundation and salinity within coastal watersheds.

Accurate characterization of the global downward shortwave (SW) and photosynthetically active radiation (PAR) is fundamental for Earth system modeling and global change research. Combined with a machine-learning method, we used the Earth Polychromatic Imaging Camera (EPIC) data onboard the Deep...

This project will focus on developing, evaluating, and using a range of modeling tools to systematically analyze coastal processes, stressors, responses, and uncertainties, with an emphasis on: Interactions across different parts of coastal systems; Long-term changes in the coastal environment; and...

Multiple federal government agencies have mission elements that address national needs related to water and advance the underlying science. These diverse mission and scientific needs have engendered a large base of water-related data and modeling capabilities that, while useful for their intended...

Spruce and Peatlands Responses Under Changing Environments (SPRUCE) site is the 8.1-ha S1 bog, a Picea mariana [black spruce] – Sphagnum spp. ombrotrophic bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). Two field research...

The Community Emissions Data System (CEDS) produces emissions anthropogenic aerosol and precursor compounds over the entire industrial era, from 1750 to the present for use in global Earth system models and Earth system research more broadly. Emissions are produced at the country level by fuel and...

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...

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...