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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Solar PV Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗
DOE OSTI2024

Wind Turbine Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a median setback equivalent to 1.1 times the turbine tip-height, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗
DOE OSTI2024

Offshore Wind Composite Siting Exclusions Scenarios

This dataset consolidates criteria that restrict the siting of offshore wind energy installations, an update from Zuckerman et al. (2023). All scenarios exclude area from wind energy development due to legal or administrative restrictions (e.g., Marine Protected Areas), existing infrastructure, and technical constraints (e.g., 1,300 m bathymetry limit). A TIF data file and a PNG cartographic map of the data are provided for three scenarios ("Open", "Reference", and "Limited"), showing areas where offshore wind energy is restricted across the contiguous United States. The "Open" scenario only excludes areas with known preclusions for offshore wind deployment. The "Reference" scenario excludes all areas from the "Open" scenario, as well as additional best management practices such as known proposed protected areas, and state waters. The "Limited" scenario excludes all area from the "Reference" scenario. Additionally, it has more stringent siting criteria such as larger setbacks from shore and infrastructure, as well as shallower depth limits on floating technology. For further details and citation, please refer to the publication linked below: Zuckerman, Gabriel R., Anthony Lopez, Travis Williams, Rebecca Green, and Grant Buster. 2023. Impacts of Siting Considerations on Offshore Wind Technical Potential in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20- 85088.

Array↗
DOE OSTI2025

Wind Turbine Sound Setbacks and Supply Curves: Ordinances and Extrapolated Trends, 110 Hub Height, 130 Rotor Diameter

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: 1. Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. 2. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. 3. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

Array↗
DOE OSTI2024

Porous Nanographenes, Graphene Nanoribbons, and Nanoporous Graphene Selectively Synthesized from the Same Molecular Precursor

In this study, we demonstrate a family of molecular precursors based on 7,10-dibromo-triphenylenes that can selectively produce different varieties of atomically precise porous graphene nanomaterials through the use of different synthetic environments. Upon Yamamoto polymerization of these molecules in solution, the free rotations of the triphenylene units around the C–C bonds result in the formation of cyclotrimers in high yields. In contrast, in on-surface polymerization of the same molecules on Au(111) these rotations are impeded, and the coupling proceeds toward the formation of long polymer chains. These chains can then be converted to porous graphene nanoribbons (pGNRs) by annealing. Correspondingly, the solution-synthesized cyclotrimers can also be deposited onto Au(111) and converted into porous nanographenes (pNGs) via thermal treatment. Thus, both processes start with the same molecular precursor and end with a porous graphene nanomaterial on Au(111), but the type of product, pNG or pGNR, depends on the specific coupling approach. We also produced extended nanoporous graphenes (NPGs) through the lateral fusion of highly aligned pGNRs on Au(111) that were grown at high coverage. The pNGs can also be synthesized directly in solution by Scholl oxidative cyclodehydrogenation of cyclotrimers. We demonstrate the generality of this approach by synthesizing two varieties of 7,10-dibromo-triphenylenes that selectively produced six nanoporous products with different dimensionalities. The basic 7,10-dibromo-triphenylene monomer is amenable to structural modifications, potentially providing access to many new porous graphene nanomaterials. We show that by constructing different porous structures from the same building blocks, it is possible to tune the energy band gap in a wide range.

25 ENERGY STORAGE↗
DOE OSTI2024

Primordial non-Gaussianities with weak lensing: information on non-linear scales in the Ulagam full-sky simulations

Abstract Primordial non-Gaussianities (PNGs) are signatures in the density field that encode particle physics processes from the inflationary epoch. Such signatures have been extensively studied using the Cosmic Microwave Background, through constraining their amplitudes,f X NL , with future improvements expected from large-scale structure surveys; specifically, the galaxy correlation functions. We show that weak lensing fields can be used to achieve competitive and complementary constraints. This is shown via theUlagamsuite of N-body simulations, a subset of which evolves primordial fields with four types of PNGs. We create full-sky lensing maps and estimate the Fisher information from three summary statistics measured on the maps: the moments, the cumulative distribution function, and the 3-point correlation function. We find that the year 10 sample from the Rubin Observatory Legacy Survey of Space and Time (LSST) can constrain PNGs toσ(f NL eq ) ≈ 110,σ(f NL or, lss ) ≈ 120,σ(f NL loc ) ≈ 40. For the former two, this is better than or comparable to expected galaxy clustering-based constraints from the Dark Energy Spectroscopic Instrument (DESI). The PNG information in lensing fields is on non-linear scales and at low redshifts (z≲ 1.25), with a clear origin in the evolution history of massive halos. The constraining power degrades by ∼60% under scale cuts of ≳ 20 Mpc, showing there is still significant information on scales mostly insensitive to small-scale systematic effects (e.g., baryons). We publicly release theUlagamsuite to enable more survey-focused analyses.

Astronomy & Astrophysics↗
DOE OSTI2022

BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Characterizing Corrosion Mechanisms Of Structural Alloys In Actinide-based Molten Chloride Salt

This is a dataset consisting of 33 GB of .png image files, each featuring a two-dimensional distribution of different element species captured an individual slice of the specimen, and .ang files that capture microstructural information captured from each respective slice using a technique called electron backscatter diffraction. The .ang files can be processed to visualize alloy grains captured in a respective slice.

Copeland, Trishelle M.↗
DOE OSTI2026

Supplemental material for paper "Radiosonde-to-Space (R2S) atmospheric specifications: Bridging observations and models for infrasound propagation"

This document describes the supplemental materials for the paper: TITLE: "Radiosonde-to-Space (R2S) atmospheric specifications: Bridging observations and models for infrasound propagation" DOI: xxxxxxxxxxx JOURNAL: TBD Written by Loring Schaible, September 17, 2025 This folder contains eight subfolders, each for a specific date and UTC time (in format YYYY-MM-DD_HH). All eight of these dates are exemplified and described in the manuscript. Within each folder is six documents. As an example, consider the folder "2022-12-31_00". The six files are comprised of: - Two text files (extension .txt) that describe the atmospheric specifications of Albuquerque, NM. One is the G2S model, the other is the R2S model. "G2S_2022-12-31_00.txt" "R2S_2022-12-31_00.txt" - Two text files (extension .dat) that contain the ground arrivals predicted by infraGA using the parameters described in the manuscript. One each for the two atmospheric specification models above, G2S and R2S. "G2S_2022-12-31_00.arrivals.dat" "R2S_2022-12-31_00.arrivals.dat" - Two image files (extension .png) that illustrate the predicted arrivals contained in the two files above. One image shows all arrivals as either black (G2S) or red (R2S). The other image shows the same but with arrivals common to both models in gray. "2022-12-31_00_Arrivals.png" "2022-12-31_00_DifArrivals.png"

Schaible, Loring Pratt [Sandia National Laboratori↗
DOE OSTI2023

PRESSURE-FILM OPEN SOURCE SCANNING AND MAPPING

SF-22-099 Pressure-film Open Source Scanning and Mapping (POSSM) is software that analyses images of pressure-film (scans or photographs). POSSM collects data from that analysis in order to produce a number of visual aides, including pressure maps, histograms, and 3d surface plots. POSSM is designed to be able to analyze an entire folder full of images, exporting numeric data as .csv files and visual graphics as .png files.

Grider, Patrick↗
DOE OSTI2025

SF-25-047 IMAGEMARKER

Image Marker is a tool for marking, categorizing, and annotating TIFF, FITS, PNG, and JPEG files. The software is intended to facilitate crowd sourcing human classification of features in scientific images without having to use web tools.

BLEEM, LINDSEYE [Argonne National Laboratory (ANL)↗
DOE OSTI2021

2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset

The dataset is comprised of both real and synthetic images from a vehicle's forward-facing camera. Each camera image is accompanied by a corresponding pixel-level semantic segmentation image (all files are .png files). In total, the dataset contains 5600 images in the training/validation set and 1400 images in the testing set. The training dataset contains mostly synthetic RGB images collected with a wide range of weather and lighting conditions using the CARLA simulator [1]. In addition, the training data also includes a small pre-selected subset of data from the Cityscapes training dataset – which is comprised of RGB-segmentation image pairs from driving scenarios in various European cities [2]. The testing data is split into three sets. The first set contains synthetic CARLA images with weather/lighting conditions that were not present in the training set. The second set is a subset of the Cityscapes testing dataset. Finally, the third set is an unknown testing set which will not be revealed to the participants until after the submission deadline. [1] Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, October). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR. [2] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., ... and Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Dataset for Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2020

Shale drilling field notes, lithologic descriptions, and core photographs of wells PLM5, PLM8, GUM1, and GLS1 at the East River Watershed, Colorado

The purpose of this dataset is to maintain a record of the shale drilling field notes, lithologic descriptions, and core photographs taken at the PLM5 (Pumphouse Lower Montane Borehole #5), PLM8 (Pumphouse Lower Montane Borehole #8), GUM1 (Gothic Upper Montane Borehole #1), and GLS1 (GLS1 = Gothic Lower Subalpine Borehole #1) wells at the East River Watershed, Colorado during October 2018 through November 2019. The wells were created as part of data collection for the Lawrence Berkeley National Laboratory Watershed Function Scientific Focus Area (WF SFA). This dataset consists of (1) detailed field notes and metadata (well locations, elevations, completion depths, and pressure transducer deployments) associated with drilling and coring activities accompanying the 2018 and 2019 installations of wells PLM5, PLM8, GUM1, and GLS1 and (2) high resolution photographs (*.png format) of all shale core recovered during the installations. Core photographs are labeled with location- and depth-specific names (e.g. PLM5_134-144ft), with the prefix (e.g. PLM5) indicating the well location and the suffix (e.g. 134-144ft) indicating the depth below ground surface of the recovered core. With the exception of core collected from well PLM5 from depths 0-59 feet, which were photographed by Tetsu Tokunaga in Berkeley, CA, all core was photographed by Kenneth H. Williams at the US Geological Survey core repository in Denver, Colorado, where all core samples are permanently archived.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Stream discharge and temperature data collected within the East and Taylor Watershed, Colorado for the Lawrence Berkeley National Laboratory Watershed Function Science Focus Area (water years 2019 to 2025)

This dataset contains stream discharge and temperature data for water years 2019 to 2025 from the East and Taylor Watersheds in Colorado, United States. This data was collected to understand hydrological processes occurring in the East River and Taylor River Watersheds, Colorado, which is part of the Lawrence Berkeley National Laboratory Watershed Function Scientific Focus Area. Data includes instantaneous observed discharge using salt dilution and acoustic doppler velocimeter techniques, raw pressure transducer downloaded data, sub-hourly temperature as well as corrected water level and associated stream discharge and mean daily values. Notes on water level corrections, rating curve development and metadata provided. A rating curve is the translation of depth to streamflow. The rating curve can be used as a quantitative measure of the “quality of the data.” Data within this dataset is formatted using ESS-DIVE’s Hydrological Monitoring Reporting Format. This data package contains (1) a zip file (Stream_Discharge_Data_WY19-WY25.zip) containing stream discharge and temperature data organized by location; (2) an InstallationMethods file (InstallationMethods.csv) describing metadata about the installation; (3) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; (4) a data dictionary (dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (5) a locations metadata file (locations.csv); (6) and a sensor metadata file (sensors.csv). All data files are in non-proprietary formats (csv, png, or pdf formats). Please contact Rosemary Carroll, Curtis Beutler, or Austin Shirley for any support in accessing the files. Update on 2023-05-12: Additional data from WYs 2021 and 2022 were added. Additionally, the dataset was converted using ESS-DIVE’s Hydrological Monitoring Reporting Format. Data files were reformatted to match reporting format guidance, new metadata files were added, and files were converted from excel to CSV. Update on 2025-05-16: Additional data from WYs 2022 (for locations not previously included), 2023, and 2024 were added. An additional descriptive PDF (WFSFA_Streamflow_Hydrograph_Disclaimer.pdf) was added. Metadata files were updated to reflect the addition of new data and locations. Update on 2026-05-18: Additional data from WY 2025 were added, including a new location Upper Trail Creek (TR-TCG2). Metadata files were updated to reflect the addition of new data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Organic carbon content and turnover time in relation to pedogenic iron and aluminum in four temperate forest soils sampled in 2007

Soil organic carbon (SOC) can be stabilized via association with iron (Fe) and aluminum (Al) minerals. Fe and Al can be strong predictors of SOC storage and turnover in soils with relatively high extractable metals content and moderately acidic to circumneutral pH. Here we test whether pedogenic Fe and Al influence SOC content and turnover in soils with low Fe and Al content and acidic pH. In soils from four sites spanning three soil orders, we quantified the amount of Fe and Al in operationally-defined poorly crystalline and organically-complexed phases using selective chemical dissolution applied to the soil fraction containing mineral-associated carbon. We evaluated the correlations of Fe and Al concentrations, mean annual precipitation (MAP), mean annual temperature (MAT), and pH with SOC content and 14C-based turnover times. We found that poorly crystalline Fe and Al content predicted SOC turnover times (p < 0.0001) consistent with findings of previous studies, while organically-complexed Fe and Al content was a better predictor of SOC concentration (p < 0.0001). Greater site-level MAP (p < 0.0001) and colder site-level MAT (p < 0.0001) were correlated with longer SOC turnover times but were not correlated with SOC content. Our results suggest that poorly crystalline Fe and Al effectively slow the turnover of SOC in these acidic soils, even when their combined content in the soil is less than 2% by mass. However, in the strongly acidic Spodosol, organo-metal complexes tended to be less stable resulting in a more actively cycling mineral-associated SOC pool. We characterized mineralogy and carbon content in soils from four forested sites in the eastern deciduous forest zone that span three soil orders. Using selective chemical dissolution applied to the soil fraction containing mineral-associated carbon, we quantified the amount of Fe and Al in operationally-defined poorly crystalline and organically-complexed phases, and correlated these with previously published SOC content and 14C-based turnover times (McFarlane et al. 2013). This dataset comprises site characterization undertaken in 2007 (.png) and subsequent soil analyses (.csv containing C, Fe, Al, 14C-derived TurnoverTime) concluded in 2010.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗