Results for “science data processing” — Page 42

DOE OSTI2017

Nanosurveyor: a framework for real-time data processing

Not Available

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

16S and ITS Amplicon Sequencing Fastq files and metadata from PARCHED Panama Tropical Forest soils, 2019-2020,

Model projections predict tropical forests will experience longer periods of drought and more intense precipitation cycles under a changing climate. Such transitions have implications for structure-function relationships within microbial communities. We examine how chronic drying might reshape prokaryotic and fungal communities across four lowland forests in Panama with a wide variation in mean annual precipitation and soil fertility. Four sites were established across a 1000 mm span in mean annual precipitation (2335 to 3300 mm). We expected microbial communities at sites with lower MAP to be less sensitive to chronic drying than sites with higher MAP; while fungal communities to be more resistant to disturbance than prokaryotes. At each location, partial throughfall exclusion structures were established over 10 x 10 m plots to reduce direct precipitation input. Raw demultiplexed sequences (bacteria, archaea, fungal) from soil samples taken from PARCHED Panama Tropical Forest throughfall exclusion experiments. Files that contain 16S are sequences from prokaryotes, ITS indicates sequences from fungi. Compressed fastq files are contained in Field_PARCHED_ITS_fastq_2020.zip, Field_PARCHED_ITS_fastq_2019.zip, Field_PARCHED_16S_fastq_2020.zip, Field_PARCHED_16S_fastq_2019.zip.There are four sites across the isthmus of Panama, each with 4 control and 4 exclusion plots. Throughfall exclusion shelters were built to intercept 50% of throughfall precipitation that hits the soil. Samples were taken on May 2019 and Jan 2020 from 0-10 cm and 10-20 cm depth approximately 9 and 18 months after shelter installation. Metadata and sample IDs for fastq files for 2019 sampling are within Field_PARCHED_Metadata_2019_ITS.csv and Field_PARCHED_Metadata_2019_16S.csv. The metadata for both 16S and ITS fastq files for January 2020 is included in Field_PARCHED_Metadata_2020.csv. No data processing or QA/QC was done on the raw data. Data processing example provided in R notebook file.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

A Model-Based Approach to Scintillator/Photomultiplier System Characterization

Scintillator/photomultiplier systems are prevalent in many instruments to measure a variety on nuclear events. When gamma energy from a nuclear event irradiates the scintillator, event radiation interacts with the scintillator material generating photons which are detected by the photomultiplier tube (PMT). The PMT photoelectrons or photocathode current is amplified in the PMT by a number of dynode stages producing a total charge out for a given radiation flux into scintillator. The resulting current is converted to a voltage when passed through a typical load impedance. This voltage is attenuated and provided as input to a digitizer through a variety of signal conditioners (filters, amplifiers, receivers) before analog-to-digital conversion. In this report, we concentrate on the development of analytic models for the scintillator/photomultiplier system and develop both a (1) statistical simulator; and (2) a model-based signal processor to extract the desired signal information while rejecting the noise and uncertainty. We develop the model analytically, transform it to the model-based framework (state-space), and then use actual experimental data to fit the model and apply it to our processing problem.

42 ENGINEERING↗

DOE OSTI2017

IoGET: Internet of Geophysical and Environmental Things

The objective of this project is to provide novel and fast reduced-order models for onboard computation at sensor nodes for real-time analysis. The approach will require that LANL perform high-fidelity numerical simulations, construct simple reduced-order models (ROMs) using machine learning and signal processing algorithms, and use real-time data analysis for ROMs and compressive sensing at sensor nodes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Research Priorities to Incorporate Terrestrial-Aquatic Interfaces in Earth System Models. Workshop Report, September 7-9, 2016

The terrestrial-aquatic interface (TAI) is a highly dynamic component of the Earth system, developed from a near balance between terrestrial and aquatic conditions and forming unique processes and community assemblages. Furthermore, TAIs are known to play a critical role in carbon biogeochemical cycling and have the potential to provide major feedbacks to the Earth system (e.g., methane production). However, there is a lack of basic data and multiscale models to adequately describe how a changing climate can influence the key processes (also an unknown) related to Earth system–relevant feedbacks in these unique, ubiquitous ecosystems. In general, Earth system models (ESMs) have excluded TAI ecosystem processes, thus creating tremendous uncertainty as to how TAIs will influence climate feedbacks across a range of scales, spanning from local to global. For example, ESMs represent wetlands very simplistically at best (e.g., the Accelerated Climate Modeling for Energy project) and include only a static fraction of dry land or open water, entirely lacking key processes in these hybrid areas and critical climate and biological feedbacks between land and water. This new area of research, therefore, must integrate a variety of important research topics—plants, soil, hydrology, reactive transport, microbiology, genomics, and modeling—into a systems-level understanding that can be extended to improve predictive modeling capabilities. The goal of the September 2016 Research Priorities to Incorporate Terrestrial-Aquatic Interfaces in Earth System Models Workshop was to engage the scientific community in an open discussion about critical scientific gaps. These gaps and research topics demand immediate field investigations to gather data for representing these important, yet understudied and underrepresented, ecosystems in ESMs. Workshop results will inform the U.S. Department of Energy’s Office of Biological and Environmental Research (BER) as it plans and prepares for future research efforts that address the TAI gap through model-informed and model-inspired field studies. The resulting data will enable iterative refinement of high-resolution, next-generation ESMs. Specifically, the workshop (1) summarized past and current field, process, and modeling TAI research; (2) identified critical sensitivities and uncertainties in the systems; (3) identified key processes, traits, existing data, and environmental variables needed to adequately characterize these systems; and (4) discussed idealized strategies that couple models and experiments to advance the state of the science in TAI modeling, including potential experiments that would test and improve land model fidelity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Fission In R-process Elements - FY19 Q4 Quarterly Report

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities receive funds from DOE Office of Science, while national laboratories receive funds directly from NA221.