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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 IONTOF TOF.SIMS 5 data source is a time-of-flight secondary ion mass spectrometer and powerful surface analysis tool used to investigate scientific questions in biological, environmental, and energy research. Among the most sensitive of surface analysis tools, it uses a high-vacuum technique...

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...
Earth Scientist Emily Graham is an quantitative ecosystem ecologist in the Biological Sciences Group at PNNL and is part of the Ecosystem Science Team. She is co-principal investigator of the Subsurface Biogeochemical Research Science Focus Area (SFA) and a key member of PNNL’s soil microbiome SFA...

Metatranscriptomic data from MSC-2 12 fastq files (6 forward read, 6 reverse read) describing transcriptomic response of MSC-2 during chitin growth. 1 csv file showing normalized gene expression values

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...

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...