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...

Coastal landscapes are increasingly exposed to seawater due to sea level rise and extreme weather events. The biogeochemical responses of these vulnerable ecosystems are poorly understood, limiting our ability to predict how their role in global biogeochemical cycles will shift under future...

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...

Sara Gosline received BA in Computer Science from Columbia University and spent two years working in software before returning to graduate school full time. She received her Masters and PhD in Computer Science from McGill University with a specialty in Bioinformatics and then moved to the...

Earth Scientist Emily Graham is an quantitative ecosystem ecologist in the Biological Sciences Group at PNNL and is part of the Ecosystem Science Team. She is co-principal investigator of the Subsurface Biogeochemical Research Science Focus Area (SFA) and a key member of PNNL’s soil microbiome SFA...

Biography Kelly is a senior data scientist in the Computational Biology group within the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). After earning a MS in Biostatistics from the University of Washington in 2012, she worked at a cancer research company for two years...

David Degnan is a biological data scientist who develops bioinformatic and statistical pipelines for multi-omics data, specifically the fields of proteomics, metabolomics, and multi-omics (phenotypic) data integration. He has experience with top-down & bottom-up proteomics analysis, genomics &...

The IONTOF TOF.SIMS 5 data source is a time-of-flight secondary ion mass spectrometer and powerful surface analysis tool used to investigate scientific questions in biological, environmental, and energy research. Among the most sensitive of surface analysis tools, it uses a high-vacuum technique...

Stanford Synchrotron Radiation Lightsource Experimental Station 14-3b is a bending magnet side station dedicated to X-Ray Imaging and Micro X-Ray Absorption Spectroscopy of biological, biomedical, materials, and geological samples. Station 14-3b is equipped with specialized instrumentation for XRF...

Rigaku Rapid II Microbeam is one of the most versatile micro-diffraction XRD system in materials analysis, using advanced imaging plate technology for measuring diffraction patterns and diffuse scattering from a wide range of materials. The RAPID™ II Curved Detector X-Ray Diffraction (XRD) System's...

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 Ocean Renewable Power Company (ORPC) data were recorded in...