SEARCH · Engineering Papers

Results for “Data Set”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Matminer: An open source toolkit for materials data mining

As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze these materials data sets and build predictive models is becoming more important. This manuscript introduces matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and predicting materials properties. Matminer provides modules for retrieving large data sets from external databases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science. It also provides implementations for an extensive library of feature extraction routines developed by the materials community, with 47 featurization classes that can generate thousands of individual descriptors and combine them into mathematical functions. Finally, matminer provides a visualization module for producing interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning and data analysis packages already developed and in use by the Python data science community. We explain the structure and logic of matminer, provide a description of its various modules, and showcase several examples of how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new methodologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A Climatology of Dust Deposition in the Upper Colorado River Basin for February-May 1980-2023

This data set is a long-term climatology of the average monthly total dust deposition, wet and dry, for the months of February-May 1980-2023 pulled from the MERRA-2 reanalysis data set over the Upper Colorado River Basin. This data set can be used to study the long-term spatiotemporal patterns of dust deposition, especially on snow. This data set is associated with the preprint article “A multi-decadal climatology of dust-on-snow from wet deposition in the Upper Colorado River Basin”.

dry_dust_deposition↗

DOE OSTI2020

Critical Infrastructure Classification via CNN-based Modeling and Image Analysis

With recent advances in the fields of satellite imagery and machine learning we now have the ability to develop explainable deep learning models that enhance critical infrastructure analysis. Funded through Idaho National Laboratory’s (INL) Laboratory Directed Research and Development (LDRD) office we are in the process of developing a deep learning model capable of identifying critical infrastructure facilities and embedded features within those facilities. Utilizing current limit of practice techniques in the machine learning areas of explainability and transfer learning our model, once complete, will have the capacity to be used on multiple different imagery data sets and produce results that not only classify critical infrastructure facilities, but also explain why a critical infrastructure facility was classified as a certain type of facility. These advancements eliminate the ‘black box’ approach deep learning models have had in the past, where a user will have to trust the conclusion of a model without understanding what reasoning when into the model’s classification process. They also expand a model’s usefulness, traditionally a deep learning model will have to use the same data set it was originally trained on. Given the long training times of deep learning models this is impractical in a number of scenarios. By utilizing transfer learning advancements, we are eliminating the need to train our model on the same data set it is then run on to classify critical infrastructure facilities. We are also enabling the analysis and classification of data sets that are potentially too small to be divided into a training and testing data set. Once completed this model can provide a foundation to enhanced critical infrastructure analysis, dependency analysis, and potential disaster relief efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Comparison of the structure function F 2 as measured by charged lepton and neutrino scattering from iron targets

A comparison study of world data for the structure function F 2 for Iron, as measured by both charged lepton and neutrino scattering experiments, is presented. Consistency of results for both charged lepton and neutrino scattering is observed for the full global data set in the valence regime. Consistency is also observed at low x for the various neutrino data sets, as well as for the charged lepton data sets, independently. However, data from the two probes exhibit differences on the order of 15% in the shadowing/anti-shadowing transition region where the Bjorken scaling variable x is < 0.15. This observation is indicative that neutrino probes of nucleon structure might be sensitive to different nuclear effects than charged lepton probes. Details and results of the data comparison are here presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2011

X-CHIP: an integrated platform for high-throughput protein crystallization and on-the-chip X-ray diffraction data collection

The X-CHIP (X-ray Crystallization High-throughput Integrated Platform) is a novel microchip that has been developed to combine multiple steps of the crystallographic pipeline from crystallization to diffraction data collection on a single device to streamline the entire process. The system has been designed for crystallization condition screening, visual crystal inspection, initial X-ray screening and data collection in a high-throughput fashion. X-ray diffraction data acquisition can be performed directly on-the-chip at room temperature using an in situ approach. The capabilities of the chip eliminate the necessity for manual crystal handling and cryoprotection of crystal samples, while allowing data collection from multiple crystals in the same drop. This technology would be especially beneficial for projects with large volumes of data, such as protein-complex studies and fragment-based screening. The platform employs hydrophilic and hydrophobic concentric ring surfaces on a miniature plate transparent to visible light and X-rays to create a well defined and stable microbatch crystallization environment. The results of crystallization and data-collection experiments demonstrate that high-quality well diffracting crystals can be grown and high-resolution diffraction data sets can be collected using this technology. Furthermore, the quality of a single-wavelength anomalous dispersion data set collected with the X-CHIP at room temperature was sufficient to generate interpretable electron-density maps. This technology is highly resource-efficient owing to the use of nanolitre-scale drop volumes. It does not require any modification for most in-house and synchrotron beamline systems and offers a promising opportunity for full automation of the X-ray structure-determination process.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements ( R 2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower ( R 2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. In conclusion, an extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Magnetotelluric Data Collected in 2016 over the San Emidio Geothermal Field in Nevada

This data set includes the magnetotelluric (MT) data collected from October 21 to November 9, 2016 over the San Emidio geothermal field in Nevada by Quantec Geoscience USA Inc. on behalf of US Geothermal Inc. as part of a project entitled "A Novel Approach to Map Permeability Using Passive Seismic Emission Tomography". This data set includes descriptions of the instrumentation, data acquisition and processing procedures, as well as the final processed data and digital archive formats. A total of 81 MT locations were surveyed (52 profile sites, and 29 MT sites). Data were processed and inspected for quality assurance on site, and reviewed daily by the geophysicist in charge of the project.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

The connection between natural gas hydrate and bottom-simulating reflectors

Bottom-simulating reflectors (BSRs) on marine seismic data are commonly used to identify the presence of natural gas hydrate in marine sediments, although the exact relationship between gas hydrate and BSRs is undefined. In this paper, to clarify this relationship we compile a data set of probable gas hydrate occurrence as appraised from well logs of 788 industry wells in the northern Gulf of Mexico. We combine the well log data set with a data set of BSR distribution in the same area identified from 3-D seismic data. We find that a BSR increases the chances of finding gas hydrate by 2.6 times as opposed to drilling outside a BSR and that the wells within a BSR also contain thicker and higher resistivity hydrate accumulations. Finally, even so, over half of the wells drilled through BSRs have no detectable gas hydrate accumulations and gas hydrate occurrences and BSRs do not coincide in most cases.

58 GEOSCIENCES↗

DOE OSTI2017

The coastal streamflow flux in the Regional Arctic System Model

The coastal streamflow flux from the Arctic drainage basin is an important driver of dynamics in the coupled ice-ocean system. Comprising more than one third of the total freshwater flux into the Arctic Ocean, streamflow is a key component of the regional and global freshwater cycle. To better represent the coupling of the streamflow flux to the ocean, we have developed and applied the RVIC streamflow routing model within the Regional Arctic System Model (RASM). The RASM is a high-resolution regional Earth System Model whose domain includes all of the Arctic drainage basin. Here in this paper, we introduce the RVIC streamflow routing model, detailing its application within RASM and its advancements in terms of representing high-resolution streamflow processes. We evaluate model simulated streamflow relative to in situ observations and demonstrate a method for improving model performance using a simple optimization procedure. We also present a new, spatially and temporally consistent, high-resolution data set of coastal freshwater fluxes for the Arctic drainage basin and surrounding areas that is based on a fully coupled RASM simulation and intended for use in Arctic Ocean modeling applications. This data set is evaluated relative to other coastal streamflow data sets commonly used by the ocean modeling community. We demonstrate that the RASM-simulated streamflow flux better represents the annual cycle than existing data sets, especially in ungauged areas. Finally, we assess the impact that streamflow has on the coupled ice-ocean system, finding that the presence of streamflow leads to reduced sea surface salinity, increased sea surface temperatures, and decreased sea ice thickness.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

DEPRECATED - Sharing the Sun Community Solar Project Data (December 2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2022. In addition, this dataset updated the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects, based on the most recent program data we collected as of July 2023. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DOE OSTI2024

DEPRECATED - Sharing the Sun Community Solar Project Data (December 2023)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2023. In addition, this dataset updated the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects, based on the most recent program data we collected as of March 2024. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DOE OSTI2021

DEPRECATED - Sharing the Sun Community Solar Project Data (Dec 2020, Revision)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top. The revision dataset included projects with a total installed capacity in 2020 under the state-level program but missing the project level details.

14 SOLAR ENERGY↗

DOE OSTI2024

HERO WEC 2024 Hydraulic Configuration Deployment Data

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗

DOE OSTI2013

Global analysis of night marine air temperature and its uncertainty since 1880

An updated version of the Met Office Hadley Centre's monthly night marine air temperature data set is presented. It is available on a 5° latitude-longitude grid from 1880 as anomalies relative to 1961–1990 calendar-monthly climatological average night marine air temperature (NMAT). Adjustments are made for changes in observation height; these depend on estimates of the stability of the near surface atmospheric boundary layer. In previous versions of the data set, ad hoc adjustments were also made for three periods and regions where poor observational practice was prevalent. These adjustments are re-examined. Estimates of uncertainty are calculated for every grid box and result from measurement errors, uncertainty in adjustments applied to the observations, uncertainty in the measurement height, and under-sampling. The new data set is a clear improvement over previous versions in terms of coverage because of the recent digitization of historical observations from ships' logbooks. However, the periods prior to about 1890 and around World War II remain particularly uncertain, and sampling is still sparse in some regions in other periods. A further improvement is the availability of uncertainty estimates for every grid box and every month. Previous versions required adjustments that were dependent on contemporary measurements of sea surface temperature (SST); to avoid these, the new data set starts in 1880 rather than 1856. Overall agreement with variations of SST is better for the updated data set than for previous versions, supporting existing estimates of global warming and increasing confidence in the global record of temperature variability and change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

THE GRISM LENS-AMPLIFIED SURVEY FROM SPACE (GLASS). V. EXTENT AND SPATIAL DISTRIBUTION OF STAR FORMATION IN z ∼ 0.5 CLUSTER GALAXIES

We present the first study of the spatial distribution of star formation in z ∼ 0.5 cluster galaxies. The analysis is based on data taken with the Wide Field Camera 3 as part of the Grism Lens-Amplified Survey from Space (GLASS). We illustrate the methodology by focusing on two clusters (MACS 0717.5+3745 and MACS 1423.8+2404) with different morphologies (one relaxed and one merging) and use foreground and background galaxies as a field control sample. The cluster+field sample consists of 42 galaxies with stellar masses in the range 10{sup 8}–10{sup 11} M{sub ⊙} and star formation rates in the range 1–20 M{sub ⊙} yr{sup −1}. Both in clusters and in the field, Hα is more extended than the rest-frame UV continuum in 60% of the cases, consistent with diffuse star formation and inside-out growth. In ∼20% of the cases, the Hα emission appears more extended in cluster galaxies than in the field, pointing perhaps to ionized gas being stripped and/or star formation being enhanced at large radii. The peak of the Hα emission and that of the continuum are offset by less than 1 kpc. We investigate trends with the hot gas density as traced by the X-ray emission, and with the surface mass density as inferred from gravitational lens models, and find no conclusive results. The diversity of morphologies and sizes observed in Hα illustrates the complexity of the environmental processes that regulate star formation. Upcoming analysis of the full GLASS data set will increase our sample size by almost an order of magnitude, verifying and strengthening the inference from this initial data set.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2011

Reference Undulator Measurement Results

The LCLS reference undulator has been measured 22 times during the course of undulator tuning. These measurements provide estimates of various statistical errors. This note gives a summary of the reference undulator measurements and it provides estimates of the undulator tuning errors. We measured the reference undulator many times during the tuning of the LCLS undulators. These data sets give estimates of the random errors in the tuned undulators. The measured trajectories in the reference undulator are stable and straight to within {+-}2 {micro}m. Changes in the phase errors are less than {+-}2 deg between data sets. The phase advance in the cell varies by less than {+-}2 deg between data sets. The rms variation between data sets of the first integral of B{sub x} is 9.98 {micro}Tm, and the rms variation of the second integral of B{sub x} is 17.4 {micro}Tm{sup 2}. The rms variation of the first integral of B{sub y} is 6.65 {micro}Tm, and the rms variation of the second integral of B{sub y} is 12.3 {micro}Tm{sup 2}. The rms variation of the x-position of the fiducialized beam axis is 35 {micro}m in the final production run This corresponds to an rms uncertainty in the K value of {Delta}K/K = 2.7 x 10{sup -5}. The rms variation of the y-position of the fiducialized beam axis is 4 {micro}m in the final production run.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

The Medical Science DMZ

Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbpsþ) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

Reference-free ground truth metric for metal artifact evaluation in CT images

Purpose: In computed tomography (CT), metal objects in the region of interest introduce data inconsistencies during acquisition. Reconstructing these data results in an image with star shaped artifacts induced by the metal inconsistencies. To enhance image quality, the influence of the metal objects can be reduced by different metal artifact reduction (MAR) strategies. For an adequate evaluation of new MAR approaches a ground truth reference data set is needed. In technical evaluations, where phantoms can be measured with and without metal inserts, ground truth data can easily be obtained by a second reference data acquisition. Obviously, this is not possible for clinical data. Here, an alternative evaluation method is presented without the need of an additionally acquired reference data set. Methods: The proposed metric is based on an inherent ground truth for metal artifacts as well as MAR methods comparison, where no reference information in terms of a second acquisition is needed. The method is based on the forward projection of a reconstructed image, which is compared to the actually measured projection data. Results: The new evaluation technique is performed on phantom and on clinical CT data with and without MAR. The metric results are then compared with methods using a reference data set as well as an expert-based classification. It is shown that the new approach is an adequate quantification technique for artifact strength in reconstructed metal or MAR CT images. Conclusions: The presented method works solely on the original projection data itself, which yields some advantages compared to distance measures in image domain using two data sets. Beside this, no parameters have to be manually chosen. The new metric is a useful evaluation alternative when no reference data are available.

62 RADIOLOGY AND NUCLEAR MEDICINE↗