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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 OSTI2021

Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains shapefiles, geotiffs, and symbology for the revised-from-Play-Fairway potential structures/structural settings used in the Nevada Geothermal Machine Learning project. Layers include potential structural setting ellipses, centroids, and distance-to-centroid raster. A submission linking the full GitHub repository for our machine learning Jupyter Notebooks will appear in the related datasets section of this page once available.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

A geo-visual analysis for exploring the socioeconomic benefits of the heating electrification using geothermal energy

In parallel to population growth and climate change, the rapid pace of urbanization worldwide has led to an enormous increase in energy demand and costs in urban areas. The subsequent energy burden has become an increasing concern for many households in the U.S. Previous studies have revealed that geothermal resources can effectively lower the electricity demand and carbon emissions in large cities. In this paper, we focus on the socioeconomic impacts of geothermal energy on urban systems by presenting an interactive visual analytics dashboard. The dashboard allows urban planners to spatially examine geothermal energy's practical benefits on energy affordability, urban livability, and resilience across the U.S. We compiled a list of socioeconomic metrics by integrating the simulation results from multiple geothermal and building models with multi-domain urban datasets (socioeconomic, demographic, and electricity utility). These metrics are created to characterize the benefits of the heating electrification of buildings using Geothermal Heat Pumps (GHP) for lowering the energy burden of middle-and low-income households nationwide. The visual dashboard employs a combination of multivariate, glyph-based, and geospatial visualization to reveal the variability and patterns in our metrics. We present a pilot study to demonstrate the GHPs' potential as a renewable and affordable solution for increasing the economic and energy grid resilience in U.S cities.

Xu, Haowen↗
DOE OSTI2020

Janus: A Python Package for Agent-Based Modeling of Land Use and Land Cover Change

Janus is an open source Python package for agent-based modeling (ABM) of land use and land cover change (LULCC). Many ABMs of LULCC have been created across platforms, some of which are not ideal for large scale, high resolution scenarios. This model provides a simple object-oriented framework for creating ABMs specific to LULCC. The organizational philosophy of the modeling framework is to create software objects (agents) that are associated with specific and contextual attributes which are isolated from where those agents exist in the spatial setting of the model, yet provide clear linkages between the agent, their environment, and other agents in the simulation. In this way, the framework allows for assembly of LULCC ABMs with low (programmatic) overhead, making the models extensible and providing clear mechanisms for integrating them with process-oriented biophysical models. Provided with Janus is a suite of geospatial data preprocessing tools that can use arbitrary land cover products as an input. Crop choice decisions are based on potential crop prices, these can be created synthetically, or drawn from integrated human-Earth systems models such as the GCAM. Janus is publicly accessible through GitHub and provides an example dataset for testing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

U.S. Hydropower Relicensing Map Series V1.1

This thematic map series uses Oak Ridge National Laboratory’s 2016 Existing Hydropower Assets Plant Dataset to map currently operational FERC-licensed hydropower plants that are anticipated to be up for relicensing in the near-term.

13 HYDRO ENERGY↗
DOE OSTI2022

Advanced Hydropower and PSH Capacity Expansion Modeling (Final Report on HydroWIRES D1 Improvements to Capacity Expansion Modeling)

Hydropower and pumped-storage hydropower (PSH) have played a key role in providing flexible, low-carbon electricity to the U.S. electricity system for over a century. As variable generation (VG) deployment increases the demand for flexible, dispatchable generation, it is important to use all available methods for understanding how hydropower and PSH interfaces with VG in a future low-carbon grid. Capacity expansion models (CEMs) of electricity systems are often used to study future electricity scenarios, but these tools often have difficulty representing site- and technology-details of hydropower and PSH due to limited spatial, temporal, or process resolution. This report demonstrates a set of model advancements to improve hydropower and PSH representations in CEMs and other models that consider hydropower and PSH's role in electricity systems. Model advancements include new data integration to define closed-loop PSH resource availability and cost, along with site-level parameterizations of existing PSH capacity and energy storage specifications. Shifting energy across seasons for both hydropower and PSH is explored to demonstrate the potential value of long-duration storage. New representations of hydropower upgrades offer opportunities to increase dispatchability, add pumps, or independently add capacity or energy depending on what is most valuable. New model structures and data are demonstrated individually and in combination to observe their impact on model results and provide initial insights into what is most important for analysts, electricity system planners, and hydropower decision-makers to consider when assessing future roles of hydropower. Improving flexibility of existing hydropower assets has the potential to reduce CO 2 emissions through complementing VG technologies and/or improve electricity system economics by reducing the need to deploy higher-cost systems. Large-scale energy storage is also shown to provide opportunities for balancing seasonal differences in VG, particularly solar photovoltaics (PV). Initial analysis with a new closed-loop PSH resource and cost dataset also demonstrate the potential for new PSH deployment to offer new grid flexibility opportunities. Future analysis and modeling of hydropower's role in the U.S. and other electricity systems could include methodological improvements based on those discussed here along with sensitivity analysis to better represent current and future potential hydropower and PSH flexibility. Improvements to geospatial and site-level data can further improve the understanding of how hydropower and PSH can be upgraded and operated in response to future electricity system needs. This work lays the groundwork to use advanced planning models to explore the range of roles hydropower and PSH can play in the grid of the future.

13 HYDRO ENERGY↗
DOE OSTI2022

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The performance of solvent-based direct air capture across geospatial and temporal climate regimes

Liquid-solvent direct air capture (DAC) is a prominent approach for carbon dioxide removal but knowing where to site these systems is challenging because it requires considering a multitude of interrelated geospatial factors. Two of the most pressing factors are: (1) how should DAC be powered to provide the greatest net removal of CO 2 and (2) how does weather impact its performance?. To investigate these questions, this study develops a process-level model of a liquid-solvent DAC system and couples it to a 20-year dataset of temperature and humidity conditions at a ~9km resolution across the contiguous US.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Site and endmember spectra of terrestrial vegetation and soils for the Colorado Headwaters Ecological Spectroscopy Study, June-July 2025

This dataset provides site and endmember spectra collected during the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign. The site spectra were collected to help validate airborne hyperspectral data acquired by the National Ecological Observatory Network's aerial observation platform (NEON AOP). Endmember spectra were collected to augment existing spectral libraries with additional samples of bare surfaces and non-photosynthetic vegetation. All measurements were acquired with an Analytical Spectral Devices (ASD) FieldSpec4 Hi-Res NG (Next Generation) spectroradiometer, which records radiance at 1nm (nanometer) intervals from the ultraviolet to the short-wave infrared (350-2500 nm). The dataset includes spectra measured at meadow sites where the CHESS team also collected vegetation samples for trait analyses. The site spectra were collected with the ASD FieldSpec4 palm grip attachment using an 8° field-of-view foreoptic. Site spectra are integrated measurements of the entire surface within the foreoptic’s field of view. For site-level spectra, the sun is the illumination source. A Spectralon panel mounted on a tripod was used for instrument optimization and white reference measurements for all site spectra. Site spectra were acquired within two hours of solar noon and within 48 hours of a NEON AOP overflight. Site spectra are labeled by date, sampling area, and site number according to the naming conventions of the CHESS campaign’s data management plan. The dataset also contains endmember spectra in the following categories: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare (soil/rock), and flowers. Endmember measurements were acquired using either the contact probe or the leaf clip attachments of the ASD FieldSpec4. In these configurations, the bulb inside the spectrometer provides the light source for the measurements. The spectrometer was optimized and white reference measurements were recorded using the circular white pucks attached to the contact probe and leaf clip. Because they do not rely on solar illumination, contact probe and leaf clip measurements were collected during a broader time frame than the palm grip site spectra. Some endmembers were measured at CHESS meadow sites, while others were collected within the larger sampling area or in nearby locations (e.g. Gothic Townsite) with similar characteristics. Radiance, reflectance, and metadata files are split into three subfolders according to measurement type: proximal/palm grip (prx), contact probe (cp), and leaf clip (lc). Radiance spectra are provided in ASD file format (.asd file extension). All ASD files can be opened using the provided scripts. Metadata is provided in two formats: CSV file format (no geolocation) and GEOJSON file format (includes geolocation for each spectra). The dataset includes a set of pre-processed reflectance spectra as CSV files (yyyymmdd_rfl.csv). The python scripts and jupyter notebook used to calculate reflectance spectra from the ASD radiance data is included here and was previously published at: https://doi.org/10.3334/ORNLDAAC/2446. There is also a folder of JPEG photographs corresponding to selected spectra. We include a protocol document with detailed steps for ASD FieldSpec4 assembly and operations. This data additionally contains a file level metadata (flmd.csv) and data dictionary (dd.csv) file. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2023

Spatial Study 2022: Water Column, Sediment, and Total Ecosystem Respiration Rates across the Yakima River Basin, Washington, USA (v2)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin and is associated with the manuscript “Sediment-associated processes account for most of the spatial variation in ecosystem respiration in the Yakima River basin” submitted to Nature Communications Earth & Environment (Garayburu-Caruso et al., in review). The dataset provides ecosystem metabolism estimates generated from streamMetabolizer (Appling et al.; 2018) using data collected during the same five-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Additionally, it includes the scripts used for the analysis and producing the figures in the manuscript. The contents include streamMetabolizer inputs and outputs and additional relevant data needed to generate the main manuscript results. The data included are: total ecosystem respiration, water respiration, calculated sediment-associated respiration, gross primary production outputs from the river corridor model for the Yakima River Basin, median grain size (d50), depth, dissolved oxygen, water temperature, pressure, and annual oxygen consumption. The associated GitHub repository can be found at https://github.com/river-corridors-sfa/SSS_metabolism. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” Raw time series sensor data, total suspended solids, and depth data from SSS were published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1969566. A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Field metadata for the samples in this da This dataset is comprised of one main data folder with four subfolders. The main data folder contains of (1) file-level metadata; (2) data dictionary; (3) total/water column/sediment respiration; (4) gross primary production (GPP); (5) median grain size (d50); and (6) annual oxygen consumption. The “Figures” subfolder contains the figures used in the paper and all intermediate files (including geospatial files). The “Published_Data” contains a readme directing the user to download the public data to reproduce analyses and figures. The “Scripts” folder contains all scripts used in the analyses that were not part of running StreamMetabolizer. Lastly, the “Stream_Metabolizer” folder contains all files associated with running StreamMetabolizer including (1) model input files, (2) model output files, (3) processing scripts, (4) histogram plots of the outputs, and (5) an R project. All files are .csv, .pdf, .R, .Rmd, .Rproj, .html, .png, .txt, .qgz, .cpg, .dbf, .prj, .shp, .shp.ea.iso.xml, .shp.iso.xml, .shx, .sbn. ta package can be found at either link. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of 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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

HarDWR - Raw Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Switched source data from collecting records from each state independently to using the WestDAAT dataset v1.0 - Initial public release Description In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, as well as geospatial data defining the areas in which water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. In addition, while all states make their water rights publicly available, each provides their records in unique formats, meaning that file types, field availability, and terms vary from state to state. This leads to additional challenges to managing resources which cross state lines, or conducting consistent multi-state water analyses. For the first version of HarDWR, we collected the water rights databases from 11 Western States of the United States. In order to preform regional analyses with the collected data, the raw records had to be harmonized into one single format. The Water Data Exchange (WaDE) is a program dedicated to the sharing of water-related data for the Western U.S. in a singular consistent format. Created by the Western States Water Council (WSWC) to facilitate the collection and dissemination of water data among WSWC's member states and the public, WaDE provides an important service for those interested in water resource planning and management in their focus region. Of the services which WaDE provides, the one of the most interesting is the WestDAAT dataset, which is a collection of water rights data provided by the 18 WSWC member states that have been standardized into a single format, much like we had done on a more limited scale with HarDWR v1. For this version of HarDWR we decided to use WestDAAT, specifically a snapshot created in Feburary 2024, as our water rights source data. A full explanation of the benefits gained from this switch can be found in the description of the updated Harmonized Water Rights Records v2.0, but in short it has allowed us to focus more of our efforts on answering research questions and gaining a more realistic understanding of how water rights are allocated. For more information on how the data for WestDAAT was collected, please see the WaDE data summary. Terms of Use While WaDE works directly with the state agencies to collect and standardize the water rights records, the ultimate authority for the water rights data remains the individual states. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. As several of the states update their water rights databases daily, the information provided here is not the latest possible, and should not be used for legal purposes. WestDAAT itself has irregular updates. Additional questions about the data the source states provided should be directed to the respective state agencies (see methods.csv and organization.csv files described below). In addition, although data was presented here was not collected directly from the states, several states requested specifically worked disclaimers when sharing their data. These disclaimers are included here as an acknowledgement from where the water rights data is primarily sourced. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." File Descriptions The unmodified February, 2024 WestDAAT snapshot is composed of nine files. Below is a brief description of each file, as well as how they were utilized for HarDWR. WaDEDataDictionaryTerms.xlsx: As the file's name implies, this is a data dictionary for all of the below named files. This file describes the column names for each of the following files, with the exception of citation.txt which does not have any columns. The descriptions for each file are divided by tab,with the same name as their associated file, within this document. allocationamount.csv: The "main" file of the group, it contains the water right records for each state. Of particular note, each water right is broken down into one or more water allocations. Allocations may be withdrawn from one or more locations, or even multiple allocations associated with a particular location. This is a more subtle and realistic representation of how water is used than what was available in the first version of HarDWR. For the records from some states, this can mean that multiple allocations listed under a single right will appear as rows within this file. citation.txt: A combination of contact information for WaDE personnel, disclaimer about how the data should be used, and guidelines for citing WestDAAT. methods.csv: A file describing the source and method by which WaDE collected water rights data from each state. organization.csv: A file listing the water rights authoritative agencies for each state. sites.csv: This file provides the geographic, and other descriptors, of the physical location of allocations, called 'sites'. To reiterate, it is possible for one allocation to be associated with multiple sites, as well as one site to be associated with multiple allocations. The two descriptors which we were most interested in where the site's coordinates, as well as whether the site was classified as a Point of Diversion (POD) or a Place of Use (POU). As a general rule, PODs are geographic points, while POUs are areas typically represented as property boundaries or irregularly shaped polygons. sites_pouGeometry.csv: For those allocations with a POU site, this file contains the defining points for the associated polygons. variables.csv: A file describing the units in which an allocation's water amount is reported within WestDAAT. This information is essentially a repeat of the 'AllocationFlow_CFS' and 'AllocationVolume_AF' columns within allocationamount.csv, at least for our purposes. watersources: This file describes the source of water from which each site extracts from. For our purposes, this table was used to determine whether the water came from Surface Water, Groundwater, or Unspecified Water.

Lisk, Matthew↗
DOE OSTI2024

A five-year milestone: reflections on advances and limitations in GeoAI research

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Global Corn Heat Stress: Mean and SD of Degree Days Above 29°C based on NEX-GDDP-CMIP6 Climate Projections

Description This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). The current dataset includes: Long-Term Average Degree Days Above 29°C- Historical Long-Term Average Degree Days Above 29°C- SSP245 Long-Term Standard Deviation of Degree Days Above 29°C- Historical Long-Term Standard Deviation of Degree Days Above 29°C- SSP245 The mean and SD are calculated over 1985-2014 for the historical period and over 2035-2064 for future projections. A full description of methods, including growing season, daily temperature distribution, and statistical coefficients, can be found in Haqiqi (2024). The source climate data are obtained from https://ds.nccs.nasa.gov/thredds2/catalog/catalog.html and are described in Thrasher et al (2022). The codes used to create this dataset are available at https://github.com/ihaqiqi/dd29c_nex_cmip6. Acknowledgments This work was supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. The data processing, computation, and storage were completed on Purdue Anvil supercomputer and cyberinfrastructure supported by the National Science Foundation HDR award # 2118329: "NSF Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE)". References Haqiqi. I. (2024). Trade can buffer climate-induced risks and volatilities in crop supply. Environmental Research: Food Systems. https://doi.org/10.1088/2976-601X/ad7d12 Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific Data, 9(1), 262. https://doi.org/10.1038/s41597-022-01393-4

Climate Change↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
DOE OSTI2025

Applying a Multisector Scenario Framework to Evaluate Past and Future Public Surface Water Supply Infrastructure Strategies in Texas

Datasets supporting the index model and scenario analysis used in evaluating surface water supply strategies across different water system types in Texas. These data underpin the scenario development and application of five key indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets are organized by system type—stream reaches (flowlines), waterbodies, and reservoirs—and include both raw and standardized index values. The integrated datasets also provide scenario classifications (original and adjusted) based on infrastructure and planning priorities, enabling comparison across Shared Socioeconomic Pathways (SSPs). Additional strategy-level data are included to support evaluation of state-level new reservoir projects in relation to cost and availability tradeoffs. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions Folder: Index_model_database.zip Subfolder: Stream_reach.zip Fl_wf.csv, Fl_wq.csv, Fl_er.csv, Fl_wf_wtcUV.csv, Fl_wf_wtcnoUV.csv, Fl_allfac_wic1.csv, Fl_allfac_wic2.csvDatasets for computing WAI, WQI, ERI, WTC, and WIC for surface water systems classified as stream reaches (flowlines). Subfolder: Waterbody.zip Wb_wf.csv, Wb_wq.csv, Wb_er.csv, Wb_wf_wtcUV.csv, Wb_wf_wtcnoUV.csv, Wb_allfac_wic1.csv, Wb_allfac_wic2.csvEquivalent index model datasets for waterbodies, reflecting hydrologic and infrastructure attributes specific to impounded natural systems. Subfolder: Reservoir.zip Rs_wf.csv, Rs_wq.csv, Rs_er.csv, Rs_wf_wtcUV.csv, Rs_wf_wtcnoUV.csv, Rs_allfac_wic1.csv, Rs_allfac_wic2.csvIndex model datasets specific to regulated reservoir systems, incorporating both resource indicators and cost parameters. Folder: Integrated data.zip combined_merged_data.csv, combined_merged_data_scenario.csvDatasets integrating index model indicators (both raw and scaled) with scenario classifications, including adjustments reflecting SSP-aligned transitions and planning shifts. Folder: Additional data.zip wai_supplystrat_wic_merged.csvCurated dataset capturing proposed major reservoir-based municipal water supply strategies in Texas. Integrates site-level planning data with estimated capital infrastructure costs and water availability scores for comparative assessment.

geospatial↗
DOE OSTI2025

Geospatial analysis of freight accessibility and job attraction: The role of interstate ramps, airports, ports, and rail

The number of jobs within an industry is significantly influenced by geographical location, with transportation infrastructure playing a key role. While previous research has largely focused on how access to jobs affects employment, less attention has been given to how transportation infrastructure impacts business operations and job attraction. Here, this study addresses this gap by examining how the ease of transporting products to key transportation facilities affects job numbers in freight-intensive industries. Using job data from the Longitudinal Employment Household Dynamics dataset at the Census Tract level, we applied a non-parametric model to assess the impact of proximity to interstate ramps, rail intermodals, ports, and airports. Our analysis revealed that closer transportation infrastructure generally has a greater impact on employment. Specifically, interstate ramps are crucial for attracting jobs, particularly in rural areas, while airport proximity is essential for industries dealing with high-value, time-sensitive goods, as seen notably in Massachusetts. The importance of transportation facilities varies considerably across states and industries. The findings and method in this study can be used by transportation agencies for freight planning.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Analyses to Inform Equitable Investment Prioritization

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. Megan Day and Liz Ross, along with their co-authors and supported by NREL's Sustainable Communities Catalyzer, identified areas where disadvantaged community indicators intersect with high potential for renewable energy deployment. Through a geospatial intersection of energy burden, environmental hazard, and sociodemographic data with the technical generation potential and levelized cost of energy for multiple renewable energy technologies, we identified trends across disadvantaged community indicators and renewable energy deployment potential. Combining metrics across several tools, including the State and Local Planning for Energy (SLOPE) platform, the Low-Income Energy Affordability (LEAD) tool, and the Environmental Justice Screening and Mapping (EJSCREEN) tool, we compiled a dataset that can be used to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantage communities.

catalyzer↗
DOE OSTI2026

CHESS 2025: Field-collected vegetation attributes and site photos

This dataset represents field observations of vegetation samples collected as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Samples were collected in the field using tablet computers and digital forms, with target data differing by sample type (individual trees, individual shrubs, or 1-meter square plots of meadow and subshrub vegetation). Field samples were collected within 72 hours of airborne data collection using the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. Remote sensing data for the project is available on ESS-DIVE (DOI and citation to be added upon publication). Field data collected included canopy height and per-species horizontal proportional cover for meadow plots, species identity and height information for shrubs, as well as species identity, height, diameter at breast height, and health assessment information for trees. Photos of the focal site and surrounding landscape were taken for all sampling sites and are included in this archive. Green leaves or needles were collected for plant trait and foliar chemistry analysis. This data is archived separately (DOI and citation to be added upon publication). High-precision geospatial data for each sample (crown perimeter polygons for trees and shrubs, plot boundaries for meadow plots) is available here (Henderson et al., 2026). Field and remote sensing protocols largely followed those of a previous field and airborne imaging campaign performed in 2018 (described in Chadwick et al. 2020). Field data from the 2018 campaign can be found here (Chadwick et al., 2020 doi:10.15485/1618130). Because different field measurements were taken for meadow, shrub, and tree sites, data from these three sample types are archived as separate tables (chess_meadow_site_cleaned.csv, chess_shrub_site_cleaned.csv, chess_tree_site_cleaned.csv). Meadow proportional cover data is stored in a separate table (chess_meadow_cover_cleaned.csv). Taxonomy was treated identically between sample types, and the dataset shares a common set of voucher specimens (chess_voucher_IDs_cleaned.csv), as well as a single species list (chess_species_list_cleaned.csv). All taxonomic determinations were performed to the species level, and adhere to the Global Biodiversity Information Facility (GBIF) backbone taxonomy as of January 10th, 2026 (GBIF Secretariat 2023). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗