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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)...

Dr. Gao obtained her Ph.D degree in Chemistry from institute of chemistry, Chinese Academy of Science. His Ph.D research focused on multiscale modeling of morphology and properties of polymeric materials, polymer processing and unveiling the process–properties relationships. (atomic to coarse...

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...

ProxyTSPRD profiles are collected using NVIDIA Nsight Systems version 2020.3.2.6-87e152c and capture computational patterns from training deep learning-based time-series proxy-applications on four different levels: models (Long short-term Memory and Convolutional Neural Network), DL frameworks...

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over...
The dataset consists of model outputs (CLM45BGC, CLM5BGC, and CLM5SP) There are three CLM simulations associated with Cheng et al. 2020, namely CLM4.5 in biogeochemistry mode (CLM45BGC), CLM5 in biogeochemistry mode (CLM5BGC), and CLM5in satellite phenology mode (CLM5SP). The monthly, daily and...

Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through...

Software
EyeSea software is available at https://github.com/pnnl/EyeSea
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...
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...
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.
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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...

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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...