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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.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optode Images, and Dissolved Oxygen Data from Sand and Sediment Flow-through Column Experiments associated with: "2-D Imaging of Dissolved Oxygen Concentration in Flow-Through Sediment Columns"

This data package is associated with the publication "2-D Imaging of Dissolved Oxygen Concentration in Flow-Through Sediment Columns" submitted to Frontiers in Water (Garayburu-Caruso et al., 2022). This work presents a novel application of planar optode technology to sediment columns that allows the study of well-constrained flow fields without the need to assume completely homogeneous flow. We introduce a flow-through column with the interior coated in an oxygen sensing film and a series of sampling ports as a unique tool capable of capturing changes in dissolved oxygen (DO) at high frequency (10 s) and spatial (< 1 mm) resolution across the length of a column. In addition, we provide custom scripts that allows image processing steps to be automated and consistently reproduced over a variety of experimental conditions. This dataset is comprised of four folders (1) 01_Riverbed_Sediment_Experiment, (2) 02_Sand_Experiment, (3) 01_Riverbed_Sediment_Raw_Images, and (4) 02_Sand_Experiment_Raw_Images. 01_Riverbed_Sediment_Experiment contains (1) a subfolder with csv files that contain statistical parameters from the linear regressions applied to each optode image, (2) a subfolder with csv files from DO data measured by in-line sensors, (3) a subfolder with csv files containing column and injection specific input parameters for the image processing script, and (4) a subfolder with scripts used to process the images and create associated figures. 02_Sand_Experiment contains (1) a subfolder with csv files containing column and injection-specific input parameters for the image processing script, and (2) a subfolder with scripts used to process the images and create associated figures. 01_Riverbed_Sediment_Raw_Images (spited in 3 parts due to size) contains a subfolder with raw red, and green images from the optode system during the MilliQ Injection, Vanillin Injection or Vanillin + Sampling Injection respectively. 02_Sand_Experiment_Raw_Images contains a subfolder with raw red, and green images from the optode system. Outside of the main folders there is a csv containing file-level metadata and a csv data dictionary defining column headers for all csv files contained in the data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data

High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm’s behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl .

Gorstein, Evan↗
DOE OSTI2023

Dual modality endocavity biopsy imaging system and method

A dual modality endocavity imaging and treatment system for detection of cancer and targeted biopsy and treatment procedures, comprising: a housing; a nuclear detector system housed within the housing and configured for detecting nuclear radiation imaging data; an ultrasound detector system housed within the housing for detecting ultrasound imaging data; a needle associated with the housing and adjustably positionable relative thereto; and a data processing module configured to receive the nuclear radiation imaging data and the ultrasound imaging data and to generate and output an image showing the relative position of the needle and an endocavity object of interest. The needle may include a distinct radiation signature to facilitate imaging thereof.

Lall, Terrence↗
DOE OSTI2021

A Proposed Geospatial Data Preservation Strategy for DOE's Office of Legacy Management

Identifying and planning preservation and curation activities associated with geospatial data will improve the ability of the U.S. Department of Energy Office of Legacy Management (LM) to support their core mission of protecting human health and the environment. This report documents the development LM's strategy for preserving and curating geospatial data within the context of LM's data-lifecycle-management framework. The strategy consists of preservation and curation elements, specific activities, and key enabling factors that ensure LM's geospatial data is maintained. Preservation elements enable the effective preservation of LM's geospatial data and recognizes that strategies need to be flexible to adapt to ongoing changes in scale, technology, and standards. Key enabling factors are intended to highlight critical data management responsibilities that must be addressed by LM to meet its preservation and curation objectives. A summary of best practices for geospatial data preservation is provided as part of the strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
DOE OSTI2022

Toward CO 2 Multimeasurement Geophysical Monitoring in the North Dakota CarbonSAFE Project

Paper presented at 16th International Conference on Greenhouse Gas Control Technologies (GHGT-16), October 23–27, 2022, Lyon, France. Overcoming data acquisition challenges associated with a lake in the study area, electrical infrastructure around the plant, noise from mine activities, complex near-surface conditions associated with reclaimed mine land, high wind speed, and extremely cold temperatures, data processing, modeling, and inversion of the baseline geophysical data demonstrated the importance of high-quality data for a CO 2 geophysical monitoring program. Implementing more advanced data acquisition and processing techniques in future monitoring surveys can further improve confidence in interpretations of geophysical survey data.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework

This report summarizes the activities performed under the data analytics Vertex in the Materials Characterization, Prediction and Control Project funded under laboratory directed research and development at Pacific Northwest National Laboratory. The data analytics Vertex developed models for associating global or local process parameters, microstructural features, and performance properties of friction-stir-processed 316L stainless steel plates. Statistical, machine learning, and deep learning models, as well as generative artificial intelligence approaches, were used to develop the associations between the process-structure-property data streams. These associations formed the basis for predicting global properties of parts manufactured under different process envelopes, providing a basis for predicting performance using data driven as well as physics-informed and physics-constrained approaches. Additionally, the associations were used to predict local process parameters and microstructural features of the product, predictive relationships that have the potential to form the basis of a control framework that could eventually modulate a friction-stir process to maintain product quality.

316L stainless steel↗
DOE OSTI2024

A framework for inferring and analyzing pharmacotherapy treatment patterns

Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

CONSTAX2: improved taxonomic classification of environmental DNA markers

Abstract Summary CONSTAX—the CONSensus TAXonomy classifier—was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command-line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. Availability and implementation CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. Data and scripts associated with the manuscript are available at https://github.com/liberjul/CONSTAXv2_ms_code. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bayesian Approach to Estimation of Water Table Elevations Using Historical Rasters as Prior Information 2019 - 20430

In cases of complex but only partially known geology and a lack of spatial control in observation well locations, water table elevation estimation is very challenging. In some cases, auxiliary information, such as observations of the movement of tracers, operation of injection and extraction wells, and calibration of groundwater models against the historical elevation data, can be combined with expert judgement to estimate flow directions in areas of sparse data and to aid in the production of more reliable contour maps (and associated rasters) than could be produced by relying on sparse well elevation data alone. Given a historical sequence of these raster maps, the question arises how to automate, to the extent possible, the process of producing new raster maps to reflect data from previous times, the current data and the operation of expert judgement. One solution is to adopt a Bayesian point of view and to regard the historical well elevation data, auxiliary information and historical raster maps as prior information. The well elevations for water table wells, as well as those for injection/extraction wells and the data associated with other relevant variables, can be viewed as predictors for the raster surface. From this prior information, we can, conditional on the values of these predictors for a new time period, compute an expected value map and a standard deviation map for the new raster. These then can be taken to specify a prior predictive distribution for the pixels in the new raster map. Then we condition the pixels, corresponding to water level observation wells within the raster, on the observed values in those wells (which in general will differ from the regression estimate) for the new time period. Given the smoothness of the water table surface, we then smooth the surface of deviations from the mean surface, based on the variograms of the historical rasters, and add this smoothed surface to the regression mean surface. The error structure of the produced raster map is defined by the regression error structure and the error due to smoothing based on the estimated variograms. This methodology has been developed and is being further refined for groundwater monitoring and remediation at LANL. It is a very flexible method that can also be applied with a variety of other predictors applied to model the water level wells in the area of interest over the historical record. The smoothness of the spatial process and its possible evolution over time can then be estimated from the residuals from this regression. This can be augmented by expert hydrogeological opinion based on site topography and hydrogeology. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly available remote sensing products lack the necessary temporal resolution to assess fine-scale growing season dynamics. Near-surface sensing methods provide a middle ground with high temporal frequency and tractable spatial scales (from sub-meter to hillslope scale). This data package includes csv files of Normalized Difference Vegetation Index (NDVI) timeseries (SM_NDVI_dailymax.csv) and phenological thresholds (SM_NDVI_summary.csv) for sub-plots (1m2) and Green Chromatic Coordinate (GCC) phenological thresholds (SM_GCC_summary.csv) at the plot scale (10m x 14m). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020” and “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Model Inputs, Outputs, and Scripts associated with: “Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin”

This data package is associated with the publication “Combined effects of stream hydrology and land use on basin‐scale hyporheic zone denitrification in the Columbia River Basin”, published in Water Resource Research (Son et al.2022) available at https://doi.org/10.1029/2021WR031131. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) denitrification at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions, including two-step denitrification and aerobic respiration, are considered within the HZ. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values. This study uses the RCM to explore the spatial patterns of HZ denitrification across reaches with different sizes and land use in the CRB. Our main objective is to use the RCM as a virtual reality model, and the machine-learning models as surrogates that encapsulate the complexities of the physics-based model while identifying the importance of different variables that are not evident in the model conceptualization. We do not include a direct comparison of the modeled HZ denitrification and measurements; however, the RCM can capture the overall spatial patterns of the HZ denitrification because the model inputs and its reaction networks are based on well-established theory and a physical-based model. The combination of the model-based predictions and a machine-learning approach (e.g., random forest) is used to improve our understanding of what variables of the model are associated with spatial patterns of the modeled denitrification across reaches with different sizes and land uses, and to develop a proxy model using measurable variables to reproduce the simulated patterns.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Remote sensing images, DEM, and point clouds associated with “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds”

This data package is associated with the publication “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds” published in Frontiers in Environmental Science, Environmental Informatics and Remote Sensing (Bao et al., 2026; doi: 10.3389/fenvs.2026.1725258). This data package includes the drone photos for a section of Umtanum Creek in Washington, Unted States. The photos were used to reconstruct the 3-dimensional (3D) digital elevation model (DEM) of the riverbed for the investigated stream section. The reconstruction results from four approaches are provided: (1) unoccupied aerial vehicle (UAV, colloquially known as drone) imagery-based Structure-from-Motion (SfM), (2) a machine learning-based 3D reconstruction model, Visual Geometry Grounded Deep Structure from Motion (VGGSfM), (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The ground truth measurements by tripod-mounted optical level kit and ground control points GPS locations for evaluating the accuracy of the four reconstruction approaches are also provided in this data package. A preliminary version of this data package was published in October 2025 at the time of manuscript submission. It was updated in March 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) 8 folders; (2) the detailed flight configuration html files; (3) field metadata; (4) a readme; (5) a data dictionary; and (6) file-level metadata. The folders “2024_10_18_d01” and “2024_10_18_d02” contain the original drone photos for the two drone flights (d01 and d02) on October 18, 2024. The reconstruction results from each of the approaches are in the folders called “ODM_SfM”, “VGGSfM”, “VGGTLong”, and “LiDAR”. The ground truth measurements are in the folder called “optical_level_kit”. Lastly, results comparing the different approaches are in the folder called “comparisons”. All files are .csv, .html, .jpg, .obj, .txt, and .npy. For information on using the .obj and .npy files, see the readme files within the same folder as the files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗
DOE OSTI2025

Driver Identification Dataset

The ORNL Driver Identification Dataset was created to collect and analyze driving behavior data from 50 different drivers. Each driver operated a 2014 Kenworth T270 Class 6 truck around Fort Collins, Colorado while various data sources recorded their driving behavior and vehicle performance. The dataset includes CANbus (Controller Area Network) data, GPS data, inertial measurement data, and biometric data from a heart rate monitor. A cyberattack was executed during each drive, which caused multiple dashboard warning lights to illuminate and set the tachometer and speedometer to zero, regardless of actual speed. The attack was stopped either after one minute or if the driver pulled over. By downloading the dataset, you agree to the following: 1) I will not use or disclose the data for any purpose other than Research as that term is defined in 10 CFR 745.102. 2) I will not, under any circumstances, request or accept private or linking identifiers for the data used. 3) I will not attempt to determine the identity of the individuals associated with the data. 4) I will use appropriate safeguards to prevent the use or disclose of the data for any purpose other than Research.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado

The soil thickness maps were created by using a hybrid model-data approach. Field sampling and remote sensing data of the spatial distribution of two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S. The data package includes the geospatial data of the soil thickness maps at two hillslopes near the pumphouse, and the associated remote sensing data, including lidar DEM and a shape file of the boundary of the study area. The data can be viewed in GIS software such as QGIS or ArcGIS desktop. The geospatial data can also be viewed in Python or Matlab. The data were generated for the purpose of modeling surface hydrology and near-surface chemistry. This work shows how to combine sampling data and a process-based model to predict one of the highest uncertainty in the land surface process, the soil thickness.

2D Geospatial Maps↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

FTICR-MS, Sensor, and Environmental Data from 5 Streams Impacted by the 2020 Holiday Farm Fire Associated with: "Spatiotemporal controls on the delivery of dissolved organic matter to streams following a wildfire"

This data package is associated with the publication "Spatiotemporal Controls on the Delivery of Dissolved Organic Matter to Streams Following a Wildfire" submitted to Geophysical Research Letters (Roebuck et al., 2022). The study aims to understand storm induced transport of pyrogenic materials to streams impacted by varying degrees of burn severity. Time series samples (24 samples in 1-hour intervals) were collected at 5 sites within the McKenzie River Watershed (Oregon, USA) whose catchment were each completely engulfed by the 2020 Holiday Farm Fire. The samples were collected in November 2020 during the first major storm pulse following the conclusion of the wildfire. Samples were characterized for dissolved organic carbon, total dissolved nitrogen, and by ultra-high resolution mass spectrometry. In situ turbidity data also collected.This data package contains 4 primary folders that include the following: 1) Metadata, 2) EnvData (Environmental Data), 3) SensorData, and 4) FTICR_SupportingData. The package contains a single file-level metadata (flmd) file. Each primary folder also contains individual data dictionaries (dd) to define and provide descriptors of column/row headers and data flags. The FTICR_SupportingData, folder 4, contains raw, unprocessed FTICR-MS Data files in addition to a csv containing processed FTICR-MS data. This package contains the following file types: csv, xml, pdf.

54 ENVIRONMENTAL SCIENCES↗