Complete replicate terabase metagenome (TmG.2.0) of grassland soil microbiome collections from KPBS field site in Manhattan, KS. Metagenome (unclassified soil sequencing) Data DOI Package, version 2.0.
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Complete replicate terabase metagenome (TmG.2.0) of grassland soil microbiome collections from COBS field site in Boone County, IA. Metagenome (unclassified soil sequencing) Data DOI Package, version 2.0.
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Complete replicate terabase metagenome (TmG.2.0) of grassland soil microbiome collections from IAREC field site in Prosser, WA. Metagenome (unclassified soil sequencing) Data DOI Package, version 2.0.
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Viral communities detected from three large grassland soil metagenomes with historically different precipitation moisture regimes.
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A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery...
HDF5 file containing 10,000 hydraulic transmissivity inputs and the corresponding hydraulic pressure field outputs for a two-dimensional saturated flow model of the Hanford Site. The inputs are generated by sampling a 1,000-dimensional Kosambi-Karhunen-Loève (KKL) model of the transmissivity field...
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.
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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.
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Datasets
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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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Last updated on 2024-02-11T22:41:43+00:00 by LN Anderson Omics-LHV Profiling of Host Response to West Nile Virus Infection Background West Nile virus ( WNV ) belongs to the mosquito-borne Flaviviridae family and is classified as a Category A priority pathogen by the National Institute of Allergy 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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Last updated on 2024-02-11T22:41:43+00:00 by LN Anderson PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection Background The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host...
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Datasets
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