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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Proteome-scale Structure Prediction Data - Pseudodesulfovibrio mercurii

The number of proteins predicted for Pseudodesulfovibrio mercurii is 3,446, each of which have five predicted structures from an AlphaFold run, as well as structural alignment results using the TMscore-based structural alignment method within the APoc program. Specifically, AlphaFold outputs the atoms and coordinates of the protein model in human-readable PDB files and quantitative prediction metrics in Python PICKLE files. The 5 models have been ranked based on the predicted TM-score (pTMS), a quantitative confidence metric output by AlphaFold that reports on protein model quality. The top ranked model has undergone an energy minimization calculation to relax and remove any potential clashes in the atomic coordinates. Structural alignment results are stored in two files for each protein; the top ranked model (as discussed above) is used for all alignment analyses. Both are compressed gzip files that, once unpacked, are human readable. The first file is the TMalign score results and contains the quantitative metrics for the top alignments between the predicted structure and experimental structures from the PDB70, a curated non-redundant database of about 80,000 experimental structures developed by the Soding lab. Each data point in this file is directly associated with one experimental structure; PDB ID and brief meta-data about the protein taken from the PDB70 file are reported alongside the quantitative metrics. The second results file contains the raw results associated with each alignment reported in the score results file. Specifically, the translation and rotation arrays for each alignment are provided so that the structural alignment can be recreated. Additionally, residue-level scores are reported to quantify the closeness of the aligned residues between the predicted and experimental models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change ( Δ r G ′ o ) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings ( https://github.com/maranasgroup/dGPredictor ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.

Kusmec, Aaron (ORCID:000000032295385X)↗
DOE OSTI2021

Nutritional markers and proteome in patients undergoing treatment for pulmonary tuberculosis differ by geographic region

Contemporary phase 2 TB disease treatment clinical trials have found that microbiologic treatment responses differ between African versus non-African regions, the reasons for which remain unclear. Understanding host and disease phenotypes that may vary by region is important for optimizing curative treatments. We characterized clinical features and the serum proteome of phase 2 TB clinical trial participants undergoing treatment for smear positive, culture-confirmed TB, comparing host serum protein expression in clinical trial participants enrolled in African and Non-African regions. Serum samples were collected from 289 participants enrolled in the Centers for Disease Control and Prevention TBTC Study 29 (NCT00694629) at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). After a peptide level proteome analysis utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS) and subsequent statistical analysis, a total of 183 core proteins demonstrated significant differences at both baseline and at week 8 timepoints between participants enrolled from African and non-African regions. The majority of the differentially expressed proteins were upregulated in participants from the African region, and included acute phase proteins, mediators of inflammation, as well as coagulation and complement pathways. Downregulated proteins in the African population were primarily linked to nutritional status and lipid metabolism pathways. We have identified differentially expressed nutrition and lipid pathway proteins by geographic region in TB patients undergoing treatment for pulmonary tuberculosis, which appear to be associated with differential treatment responses. Future TB clinical trials should collect expanded measures of nutritional status and further evaluate the relationship between nutrition and microbiologic treatment response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

INGENIOUS Thermal Conductivity Measurement Source Categorization

Thermal conductivity (TC) data taken for different wells at a specified drill depth. This is an abridged version of the complete SMU heat flow database, downloaded from the SMU node of the NGDS at the beginning of INGENIOUS (approximately April 2021), and filtered to the INGENIOUS study area. This National Geothermal Data System (NGDS) project aggregates geothermal data collected and curated by the SMU Geothermal Laboratory and its partner organizations. All columns in this database are the same as the SMU database, except for 2 additions associated with this project. Repeated columns are for data correlation purposes. Column descriptions and data types are the same as previous iterations of the SMU database. The new values that are the addition are two new columns developed as part of the INGENIOUS project: INGENIOUS TC Value | INGENIOUS notes INGENIOUS notes are individual notes that were written for specific data points during the analysis process. There are not always notes associated with each input value. INGENIOUS TC Value includes 4 values: 1. Assumed Measured These are values that are assumed to be measured thermal conductivity values, either within a specific well or within the same study region. Many of these have either a published reference, a reported standard deviation, or a unique thermal conductivity value. 2. Data release - assumed measured These are values in the SMU database that are from proprietary data that were added to the SMU database and are labeled as data release for their reference. These values were searched for in person at the SMU Geothermal Laboratory as well as virtual examination of data available on the NGDS. For many of these, there are reported thermal conductivity values associated with the heat flow data in the database, but no specific table or reference to measurements in the original data release files. 3. Known measured These are values that have a reported measurement, either as an original file in the SMU data files on the NGDS or a reported table in a publication. In the rare circumstances, Maria Richards or David Blackwell confirmed measurement. Confirmation of measurement would be written in the INGENIOUS notes column. 4. Unmeasured Unmeasured values are those that are known to be unmeasured, either estimated from another report or no information given. In the SMU database, there are wells that have a heat flow but no thermal conductivity. These are categorized as unmeasured. There are also heat flow values that are stated to have estimated or generalized average thermal conductivity values for the region and rock type. Because these are known to be unmeasured, they are categorized as such. 5. Blank Blank values are either A quality or X quality. These quality values are stated in the INGENIOUS notes. These values were not going to change associated with the heat flow analysis, so these were not examined.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

GBCGE Subsurface Database Explorer and APIs

This submission defines a DOI for the Great Basin Center for Geothermal Energy's (GBCGE) Subsurface Database Explorer web application and underlying data services, and acknowledges the INGENIOUS project as a major source of funding for data compilation and quality assurance. The GBCGE Subsurface Database Explorer is an interactive web mapping application that provides public access to the GBCGE Subsurface Database, and its collection of datasets pertinent to geothermal exploration, oil and gas exploration, critical mineral exploration, and other subsurface characterization for the Great Basin Region, western US. This is a living database, and will be continuously updated with new data and datasets as funding and motivations allow. The underlying database views that populate the web application are on an automated refresh schedule. Data sources and acknowledgements: We thank our partners with the Nevada Division of Minerals (NDOM), the Southern Methodist University (SMU), and Great Basin State Geological Surveys for their active efforts in data curation, schema design, and quality assurance. We also thank contributors among the USGS, Oregon Institute of Technology, State Divisions of Water Resources, State Divisions of Oil, Gas, and Minerals, and State Geological Surveys for open data availability and direct contributions made under the National Geothermal Data System (NGDS).

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Roadmap on thermodynamics and thermal metamaterials

Thermal metamaterials represent a transformative paradigm in modern physics, synergizing thermodynamic principles with metamaterial engineering to master heat flow at will. As next-generation technologies demand multi-scale thermal control, this field urgently requires systematic frameworks to unify its multidisciplinary advances. Curated through a global collaboration involving over 50 specialists across 25 subdisciplines, this review primarily summarizes two decades of advancements, ranging from theoretical breakthroughs to functional implementations. The review reveals groundbreaking innovations in heat manipulation through the exploration of both classical and non-classical transport regimes, topological thermal control mechanisms, and quantum-informed phonon engineering strategies. By bridging physical insights like non-Hermitian thermal dynamics and valleytronic phonon transport with cutting-edge applications, we demonstrate paradigm-shifting capabilities: environment-adaptive thermal cloaks, AI-optimized metamaterials, and nonlinear thermal circuits enabling heat-based computation. Experimental milestones include 3D thermal null media with reconfigurable invisibility and thermal designs breaking classical conductivity limits. Here, this collaborative effort establishes an indispensable roadmap for physicists, highlighting pathways to quantum thermal management, entropy-controlled energy systems, and topological devices. As thermal metamaterials transition from laboratory marvels to technological cornerstones, this work provides the foundational lexicon and design principles for the coming era of intelligent thermal matter.

heat conduction control↗
DOE OSTI2020

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. Although it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. Furthermore, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Underwater Target Detection Software Demonstration on the RivGen Turbine

This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95). - Contents - Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format. - Requirements - Python3.8+ is required to install and run the train and validation script. The README.md provides instruction for installing the requirements from the requirements.py file. - Instructions - The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Hyporheic zone, river, and groundwater metagenome resolved genomes and rpS3 genes in East River Watershed, Colorado USA Summer 2020, 2021

Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from water filter collected across 8 locations along the East River Watershed, CO, and 1 nearby groundwater well. The purpose was to look for connectivity and similarities across the network and to see the impact of the groundwater. As a part of Lawrence Berkeley National Laboratory (LBNL) Watershed Science Focus Area (SFA), we assessed community composition and strain similarities between the sites and we also compared it to previous metagenomic studies within the watershed looking at floodplain (Matheus Carnevali et al. 2021) and hillslope (Lavy et al. 2019) microbiomes. Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from filters across 8 locations during August 2020 and July 2021. This resulted in 32 samples. The groundwater sample was sequenced at UC Berkley's QB3. The other 31 samples were sequenced at University of Maryland. Metagenomes were assembled using four autobinners and the best bins were selected using dasTool. The genomes were dereplicated at 95% with dRep and the subset of winning genomes were manually curated based on visual inspection of taxonomic profile, GC content, coverage, and a set of 51 bacterial single copy genes (BSCG), and 38 archaeal signal copy genes (ASCG). The dataset includes a zip file of 311 genomes (HZ_River_SW_MAGS_Dereplicated_95.zip). The dataset additionally includes a zipped file of ribosomal protein small subunit 3 (rpS3) proteins from the hyporheic zone and river data (rpS3_Proteins_HZ_River.zip), a metadata file used to register associated samples with IGSNs (International Generic Sample Numbers) (samples.csv), a location metadata file (locations.csv). 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.

DNA↗
DOE OSTI2022

Artificial intelligence in computational materials science

In this themed collection we aim to broadly review some of the critical, recent progress in the application of AI/ML to various aspects of computational materials science and materials science more broadly. In this collection spread across two issues, we have assembled a collection of articles from leaders in the broad domain of applying AI/ML, which we collectively refer to as ML, in computational materials science. Together these articles curate the critical, recent progress in the application of ML to various aspects of materials science. Furthermore, these include ML approaches for understanding and driving electron microscopy, designing energy materials and the discovery of principles and materials relevant to the design of materials for the future, studying crystal nucleation and growth, the use of ML to describe force fields governing material and molecular behavior, and other topics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exploring model complexity in machine learned potentials for simulated properties

Abstract Machine learning (ML) enables the development of interatomic potentials with the accuracy of first principles methods while retaining the speed and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks map descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor Li $$_{10}$$ 10 Ge(PS $$_{6}$$ 6 ) $$_{2}$$ 2 (LGPS). We further investigate how these errors affect simulated properties and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Graphical abstract

Rohskopf, A. (ORCID:0000000227128296)↗
DOE OSTI2020

Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from 1000 pressure signals recorded at local ranges (<20 km) with scaled distance up to 8000 m/kg 1/3 . Additionally, the measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

58 GEOSCIENCES↗
DOE OSTI2020

Semisupervised Learning for Seismic Monitoring Applications

The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. Finally, in geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.

58 GEOSCIENCES↗
DOE OSTI2023

Chemical Blast Standard (1 kg)

Chemical explosions create blast waves with large overpressure disturbances. It is important to develop a standard blast model based on data to accurately predict acoustic blast-wave amplitudes near detonations and invert for explosion energy from distant observations of blast-wave signals. However, open data from large, controlled chemical explosions with reliable ground truth can be challenging to find. The lack of access to such data could limit the number of contributions to related research and potentially stifle the rate of discoveries or validation of existing models. Here, to address these data scarcity problem, we have curated and compiled a standardized set of 817 blast-wave waveforms from 19 distinct high-explosive events. The blast-wave waveforms are standardized to a 1 kg trinitrotoluene explosion using scaling laws and corrections for location effects. A brief overview of the dataset is presented along with explosion feature models as well as recommendations for extracting explosion features. The resulting dataset is distributed to an open repository in both Seismic Analysis Code and pandas DataFrame formats containing the waveforms, the scaled distances, and the sample rates.

58 GEOSCIENCES↗
DOE OSTI2023

Energy Community Atlas

The Energy Community Atlas provides efficient access to authoritative, curated, and relevant data that is vital to supporting energy planning, development, and economic growth across the U.S. In this effort, researchers at the National Energy Technology Laboratory (NETL) are utilizing advanced data visualization and transformation capabilities to develop an integrated, data atlas and resource focused on supporting energy community transitions to new manufacturing opportunities. Specifically, this project is working to find, acquire, integrate, and virtually host in a user-friendly, public and private solution from available resources, relevant to understanding and characterizing fossil energy communities themselves and inform energy planning, development, and economic growth opportunities, including opportunities for co-development to support manufacturing, critical materials, and more. This Atlas when complete is to offer a one-stop-shop for stakeholders to derive new insights to accelerate energy investments and strategic decision support needs. These are following datasets that are available as part of this ongoing project • Energy Community Atlas Map Package - This is ArcPro Map package and it contains all of the symbolized layers along with ArcPro map and geodatabase • Energy Community Atlas ArcGIS REST service - https://www.arcgis.com/apps/mapviewer/index.html?panel=gallery&suggestField=true&layers=537ced69bd88440380a62c2ec8aca30c • README Energy Community Atlas - Read me word document that has details about feature classes in Map package, ArcPro map and ArcGIS Rest Service

Bipartisan Infrastructure Law↗