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Showing 1 - 9 of 9
Project
HIRN AI/ML Ready Datasets and Models

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...

Biology
Human Health
Data Analytics & Machine Learning

  1. Datasets

    0
Project
TEDDY AI/ML Ready Datasets and Models

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...

Biology
Human Health
Data Analytics & Machine Learning

  1. Datasets

    1
Project
DAISY AI/ML Ready Datasets and Models

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...

Biology
Human Health
Data Analytics & Machine Learning

  1. Datasets

    1
Project
AI/ML Ready Datasets for Type 1 Diabetes

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...

Scientific Discovery
Biology
Human Health
Data Analytics & Machine Learning

  1. Datasets

    3
Publication
Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Christine H Chang, William C Nelson, Abby Jerger, Aaron T Wright, Robert G Egbert, Jason E McDermott, Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding, Bioinformatics Advances , Volume 3, Issue 1, 2023, vbad005, https://doi.org/10.1093/bioadv/vbad005...

Scientific Discovery
Biology
Computational Research
Publication
Underwater fish detection using deep learning for water power applications
Clean energy from oceans and rivers is becoming a reality with the development of new technologies like tidal and instream turbines that generate electricity from naturally flowing water. These new technologies are being monitored for effects on fish and other wildlife using underwater video...
Computational Research
Project
EyeSea: Underwater Video Analysis
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.
Computational Research

  1. Datasets

    1
Project
Assessing and improving the numerical solution of atmospheric physics in E3SM

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...

Computational Research

  1. Datasets

    2
Publication
An objective and efficient method for assessing the impact of reduced-precision calculations on solution correctness

Recent studies have shown that reducing the precision of floating‐point calculations in an atmospheric model can improve the model's computational performance without affecting model fidelity, but code changes are needed to accommodate lower precision or to prevent undue round‐off error. For complex...

Computational Research