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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 937 records · Page 52
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DOE OSTI2025

Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes

The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational method development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Role of underlying surface, rainstorm and antecedent wetness condition on flood responses in small and medium sized watersheds in the Yangtze River Delta region, China

Floods are becoming more frequent and are hard to control due to the shortage of water conservancy projects in small and medium sized basins, especially in developing regions. Understanding the hydrologic responses and estimated flood characteristics to storm events can help to predict flood disasters and form better effective mitigation and adaptation strategies. Here, using observations of several representative watersheds and Artificial Neural Networks (ANN) and Principal Component Regression (PCR) models, we conducted data-driven analyses to examine the flood responding characteristics. We quantified the relative contributions of the influencing factors to the variation of each flood characteristic. Statistical analysis of the observations shows that the drainage area plays a key role in determining the distribution of lag time and peak discharge. Rainstorm variability has direct influence on floods, and typhoon-induced rainstorms with high total rainfall and rainfall intensity generate higher lag time, flood peak, unit discharge and runoff depth, but lower runoff coefficient. The ANN and PCR models accurately predicted the variations of flood features using the driving factors including physical geographical characteristics, rainstorm features, and antecedent condition. Physical geographic characteristics are key influential factors of lag time, flood peak and runoff coefficient, while the rainstorm features control the magnitude of unit discharge and runoff depth. These results indicate that floods are mainly affected by rainstorm features and physical geographic characteristics in the Yangtze River delta, and it might become more damaging with the increasing rainfall extremes and sprawling impervious surfaces in a changing environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Metrics for evaluating tropical cyclones in climate data

This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Furthermore, upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions

Understanding protein-protein interactions (PPIs) between viruses and host organisms is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPIs by utilizing models trained on better-studied virus-human and human-human PPIs. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93–99 %) and AUPRC scores (0.8–0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance using standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, resulting in a drop in accuracy below 50 %. We propose a viral protein-specific evaluation framework that categorizes viral proteins into majority and minority classes based on their representation in the dataset, enabling comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Beyond integration: modeling every pixel to obtain better structure factors from stills

Most crystallographic data processing methods use pixel integration. In serial femtosecond crystallography (SFX), the intricate interaction between the reciprocal lattice point and the Ewald sphere is integrated out by averaging symmetrically equivalent observations recorded across a large number (10 4 -10 6 ) of exposures. Although sufficient for generating biological insights, this approach converges slowly, and using it to accurately measure anomalous differences has proved difficult. This report presents a novel approach for increasing the accuracy of structure factors obtained from SFX data. A physical model describing all observed pixels is defined to a degree of complexity such that it can decouple the various contributions to the pixel intensities. Model dependencies include lattice orientation, unit-cell dimensions, mosaic structure, incident photon spectra and structure factor amplitudes. Maximum likelihood estimation is used to optimize all model parameters. The application of prior knowledge that structure factor amplitudes are positive quantities is included in the form of a reparameterization. The method is tested using a synthesized SFX dataset of ytterbium(III) lysozyme, where each X-ray laser pulse energy is centered at 9034 eV. This energy is 100 eV above the Yb 3+ L -III absorption edge, so the anomalous difference signal is stable at 10 electrons despite the inherent energy jitter of each femtosecond X-ray laser pulse. This work demonstrates that this approach allows the determination of anomalous structure factors with very high accuracy while requiring an order-of-magnitude fewer shots than conventional integration-based methods would require to achieve similar results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Long-term leaf C:N ratio change under elevated CO 2 and nitrogen deposition in China: Evidence from observations and process-based modeling

Climate change, elevating atmosphere CO 2 (eCO 2 ) and increased nitrogen deposition (iNDEP) are altering the biogeochemical interactions between plants, microbes and soils, which further modify plant leaf carbon-nitrogen (C:N) stoichiometry and their carbon assimilation capability. Many field experiments have observed large sensitivity of leaf C:N ratio to eCO 2 and iNDEP. However, the large-scale pattern of this sensitivity is still unclear, because eCO 2 and iNDEP drive leaf C:N ratio toward opposite directions, which are further compounded by the complex processes of nitrogen acquisition and plant-and-microbial nitrogen competition. Here, we attempt to map the leaf C:N ratio spatial variation in the past 5 decades in China with a combination of data-driven model and process-based modeling. These two approaches showed consistent results. Over different regions, we found that leaf C:N ratio had significant but uneven changes between 2 time periods (1960-1989 and 1990-2015): a 5% ± 8% increase for temperate grasslands in northern China, a 3% ± 6% increase for boreal grasslands in western China, and by contrast, a 7% ± 6% decrease for temperate forests in southern China, and a 3% ± 5% decrease for boreal forests in northeastern China. Additionally, the structural equation models indicated that the leaf C:N change was sensitive to ΔNDEP, ΔCO 2 and ΔMAT rather than ΔMAP and ecosystem types. In this work, process-based modeling suggested that iNDEP was the main source of soil mineral nitrogen change, dominating leaf C:N ratio change in most areas in China, while eCO 2 led to leaf C:N ratio increase in low iNDEP area. This study also indicates that the long-term leaf C:N ratio acclimation was dominated by climate constraint, especially temperature, but was constrained by soil N availability over decade scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope

From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).

Bilheux, Hassina↗
DOE OSTI2022

Harmonizing model organism data in the Alliance of Genome Resources

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein–protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States

Utilization of marginal land for growing dedicated bioenergy crops for second-generation biofuels is appealing to avoid conflicts with food production. This study develops a novel framework to quantify marginal land for the Contiguous United States (CONUS) based on a history of satellite-observed land use change (LUC) over the 2008–2015 period. Frequent LUC between crop and noncrop is assumed to be an indicator of economically marginal land; this land is also likely to have a lower opportunity cost of conversion from food crop to bioenergy crop production. We first present an approach to identify cropland in transition using the time series of Cropland Data Layer (CDL) land cover product and determine the amount of land that can be considered marginal with a high degree of confidence vs with uncertainty across the CONUS. We find that the biophysical characteristics of this land and its productivity and environmental vulnerability vary across the land and lie in between that of permanent cropland and permanent natural vegetation/bare areas; this land also has relatively low intrinsic value and agricultural profit but a high financial burden and economic risk. We find that the total area of marginal land with confidence vs with uncertainty is 10.2 and 58.4 million hectares, respectively, and mainly located along the 100th meridian. Only a portion of this marginal land (1.4–2.2 million hectares with confidence and 14.8–19.4 million hectares with uncertainty) is in the rainfed region and not in crop production and, thus, suitable for producing energy crops without diverting land from food crops in 2016. These estimates are much smaller than the estimates obtained by previous studies, which consider all biophysically low-quality land to be marginal without considering economical marginality. The estimate of marginal land for bioenergy crops obtained in this study is an indicator of the availability of economically marginal land that is suitable for bioenergy crop production; whether this land is actually converted to bioenergy crops will depend on the market conditions. Here, we note the inability to conduct field-level validation of cropland in transition and leave it to future advances in technology to ground-truth land use change and its relationship to economically marginal land.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multidecadal Biological Monitoring and Abatement Program assessing human impacts on aquatic ecosystems within the Oak Ridge Reservation in eastern Tennessee, USA

Human activities can be powerful drivers of ecosystem change within catchments. While most long-term catchment studies have been conducted at pristine sites, such studies are less common from sites more impacted by human activity. The Oak Ridge National Laboratory's Biological Monitoring and Abatement Program (BMAP) was developed in the mid-1980s to (1) assess compliance with environmental regulations, (2) identify causes of adverse ecological impacts, (3) provide data for human and ecological risk assessments, and (4) evaluate the effectiveness of remedial actions taken to mitigate the impacts of contaminants in soils, groundwater, and surface water by documenting ecological recovery on the Oak Ridge Reservation (ORR), a federally owned 33,476-acre site in eastern Tennessee, USA, managed by the U.S. Department of Energy. The ORR is composed of multiple watersheds containing many small to mid-size streams. BMAP uses an integrated approach for determining stream health; its databases include long-term seasonal records of contaminant concentrations in water and biota, data from aquatic toxicity testing, and surveys of macroinvertebrate and fish assemblages from impacted and reference streams. These long-term data provide valuable records of degradation and recovery in catchment ecosystems. Our objective in this work is to describe our study system and data series in order to increase awareness of the availability of these long-term data to the catchment science community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗
DOE OSTI2021

Long-term measurements in a mixed-grass prairie reveal a change in soil organic carbon recalcitrance and its environmental sensitivity under warming

Soil respiration, the major pathway for ecosystem carbon (C) loss, has the potential to enter a positive feedback loop with the atmospheric CO2 due to climate warming. For reliable projections of climate-carbon feedbacks, accurate quantification of soil respiration and identification of mechanisms that control its variability are essential. Process-based models simulate soil respiration as functions of belowground C input, organic matter quality, and sensitivity to environmental conditions. However, evaluation and calibration of process-based models against the long-term in situ measurements are rare. Here, we evaluate the performance of the Terrestrial ECOsystem (TECO) model in simulating total and heterotrophic soil respiration measured during a 16-year warming experiment in a mixed-grass prairie; calibrate model parameters against these and other measurements collected during the experiment; and explore whether the mechanisms of C dynamics have changed over the years. Calibrating model parameters against observations of individual years substantially improved model performance in comparison to pre-calibration simulations, explaining 79–86% of variability in observed soil respiration. Interannual variation of the calibrated model parameters indicated increasing recalcitrance of soil C and changing environmental sensitivity of microbes. Overall, we found that (1) soil organic C became more recalcitrant in intact soil compared to root-free soil; (2) warming offset the effects of increasing C recalcitrance in intact soil and changed microbial sensitivity to moisture conditions. Furthermore, these findings indicate that soil respiration may decrease in the future due to C quality, but this decrease may be offset by warming-induced changes in C cycling mechanisms and their responses to moisture conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential ‘fit for the purpose’ use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. In conclusion, the analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City

Abstract Shoreline cities are influenced by both urban‐scale processes and land‐water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake‐to‐land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity‐related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

54 ENVIRONMENTAL SCIENCES↗