We report the construction of a database of infrared spectra aimed at detecting the gases emitted by biomass burning. The project uses many of the methods of the Pacific Northwest National Laboratory (PNNL) infrared database, but the selection of the species and special experimental considerations...
Filter results
Category
- (-) Data Analytics & Machine Learning (5)
- (-) Chemistry (2)
- Scientific Discovery (73)
- Biology (50)
- Earth System Science (28)
- Human Health (25)
- Computational Research (11)
- Microbiome Science (10)
- Integrative Omics (9)
- National Security (7)
- Computing & Analytics (5)
- Materials Science (5)
- Energy Resiliency (3)
- Chemical & Biological Signatures Science (2)
- Computational Mathematics & Statistics (2)
- Renewable Energy (2)
- Weapons of Mass Effect (2)
- Atmospheric Science (1)
- Coastal Science (1)
- Data Analytics & Machine Learning (1)
- Energy Efficiency (1)
- Energy Storage (1)
- Plant Science (1)
- Solar Energy (1)
Project Type
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...
Datasets
0
The PNNL-SERDP database was constructed by PNNL to generate the quantitative infrared spectra of gases associated with biomass burning; the reference data are to allow detection and quantification of such gases via infrared absorption spectroscopy. Candidates for the database were selected based on...
Category
Datasets
2
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...
Datasets
1
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...
Datasets
1
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...
Datasets
3
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 .
Category
Datasets
0