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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...

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The Phenotypic Response of the Soil Microbiome to Environmental Perturbations Project (Soil Microbiome SFA) at Pacific Northwest National Laboratory is a Genomic Sciences Program Science Focus Area (SFA) Project operating under the Environmental Microbiome Science Research Area. The Soil Microbiome...

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Fusarium sp. DS682 Proteogenomics Statistical Data Analysis of SFA dataset download: 10.25584/KSOmicsFspDS682/1766303 . GitHub Repository Source: https://github.com/lmbramer/Fusarium-sp.-DS-682-Proteogenomics MaxQuant Export Files (txt) Trelliscope Boxplots (jsonp) Fusarium Report (.Rmd, html)...

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...
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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...

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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...

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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...

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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...

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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...
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Project

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...

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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 .

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Biomedical Resilience & Readiness in Adverse Operating Environments (BRAVE) Project: Exhaled Breath Condensate (EBC) TMT Proteomic Transformation Data Exhaled breath condensate (EBC) represents a low-cost and non-invasive means of examining respiratory health. EBC has been used to discover and...

The year 2014 solution files are based on model calibration effort based on inputs and data from Year 2014 as described in Khangaonkar et al. 2018). It includes water surface elevation, currents, temperature and salinity at an hourly interval. These netcdf files with 24 hourly records include...

This data set provides the 16S microbial community composition via DNA sequence analysis from ingrowth peat and sand cores at the South End bog in 2013. These samples were collected outside the experimental enclosures and are pre-treatment with no experimental manipulation. These are part of the...

This data set provides the ITS fungal community composition via DNA sequence analysis from sand and peat ingrowth cores at the South End bog in 2013. These samples were collected outside the experimental enclosures and are pre-treatment with no experimental manipulation. These are part of the Spruce...