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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

Big Data Analysis of Massive PMU Datasets: A Data Platform Perspective

The discovery of `event signatures' and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.

Kumar, Vijay S.↗
DOE OSTI2023

Leveraging High-Fidelity Datasets for Machine Learning-based Anomaly Detection in Smart Grids

Data-driven intrusion detection systems are increasingly becoming essential for protecting critical cyber-physical infrastructure, such as the power grid, against the growing number of sophisticated cyber-attacks. The development of such tools is reliant on the availability of high-fidelity cyber-physical datasets that cover a diverse variety of potential cyber events. In this work, a high-fidelity smart grid platform is utilized to develop an extensive dataset, which is used to train and test a machine learning-based intrusion detection system. The evaluation of the developed IDS shows robust performance even when tested with statistically diverse test data not used in training.

Hyder, Burhan↗
DOE OSTI2024

Impact Study of Thunderstorms on the US Power Grid Using Publicly Available Datasets

This work analyzes the impact of thunderstorms on the US power grid based on publicly available data. Since thunderstorms can bring lightning, heavy precipitation, and wind storms, analyzing their impact on the power system provides a combined correlation of lightning strikes, floods, and wind storms on power outages. This paper leverages publicly available thunderstorm datasets from the National Weather Service (NWS) and power outage datasets from Oak Ridge National Laboratory’s Environment for Analysis of Geo-Located Energy Information (EAGLE-I) to study the correlation between thunderstorms and power outages. This work is analyzing the patterns of thunderstorms from 2013-2022, which shows that the thunderstorms are not slowing down and will seem to continue their impact on human life in the future. This work also analyzes the monthly and yearly pattern of the impact of thunderstorms on power systems at the national, state, and county level.

Bhusal, Narayan↗
DOE OSTI2026

Curation and Dissemination of Complex Multi-Modal Datasets for Radiation Detection, Localization, and Tracking

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.

Data curation↗
DOE OSTI2024

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Search for the lepton flavour violating decay $\mu ^+ \rightarrow \mathrm {e}^+ \gamma $ with the full dataset of the MEG experiment: MEG Collaboration

Our final results of the search for the lepton flavour violating decay μ+→e+γ based on the full dataset collected by the MEG experiment at the Paul Scherrer Institut in the period 2009–2013 and totalling 7.5×1014 stopped muons on target are presented. Furthermore, there was not a significant excess of events observed in the dataset with respect to the expected background and a new upper limit on the branching ratio of this decay of B(μ+→e+γ)<4.2×10-13 (90 % confidence level) is established, which represents the most stringent limit on the existence of this decay to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Towards Class-Balancing Human Comfort Datasets with GANs

Human comfort datasets are widely used in smart buildings. From thermal comfort prediction to personalized indoor environments, labelled subjective responses from participants in an experiment are required to feed different machine learning models. However, many of these datasets are small in samples per participants, number of participants, or suffer from a class-imbalance of its subjective responses. In this paper we explore the use of Generative Adversarial Networks to generate synthetic samples to be used in combination with real ones for data-driven applications in the built environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Distribution System Dataset Generator for AI Applications [SWR-24-75]

This software is a simple, light-weight python package to generate pytorch compatible machine learning graph dataset representing electric power distribution system. User is able to use these graph datasets to test their graph generation artificial intelligence (AI) models, link prediction AI models, graph classification AI models and so much more. This package uses grid-data-models (https://github.com/NREL-Distribution-Suites/grid-data-models) as input data format for power distribution system. NREL-Ditto (https://github.com/NREL-Distribution-Suites/ditto) tool can be leveraged to transform popular distribution system file formats such as opendss, cyme and synergi to grid-data-models.

Duwadi, Kapil↗
DOE OSTI2025

Open-Datasets for WEC Simulation

SAND2025-11468O The Open-Datasets for WEC Simulation is a tool that uses datasets and simulation configuration files to conduct OpenFOAM wave energy converter (WEC) simulations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Chartrand, Chris [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2022

Dataset of Simulated Intracardiac Transmembrane Voltage Recordings and ECG Signals

The dataset consists of pairs of computationally simulated intracardiac transmembrane voltage recordings and ECG signals. In total, 16140 organ-level simulations were conducted to create this dataset, using a range of cardiac geometries and physiological parameters. Simulations were performed at LLNL's Lassen supercomputer, concurrently utilizing 4 GPUs and 40 CPU cores. Each simulation produced pairs of 500ms-by-10 ECG signals and 500ms-by-75 transmembrane voltage signals. For convenience, the signals are concatenated and saved as matrices. Each of these matrices is then stored as a numpy array. See the documentation for further details.

Landajuela, Mikel↗
DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Surface Radiation Balance Dataset from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

Abstract A field campaign at Siple Dome in West Antarctica during the austral summer 2019/20 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-powered equipment, includes surface energy balance measurements, meteorology, and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model’s snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6- μ m atmospheric window reveal cloud phase transitions associated with cloud life cycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations. significance statement Antarctica is a critical region for understanding climate change and sea level rise, as the great ice sheets and the ice shelves are subject to increasing risk as global climate warms. Climate models have difficulties over Antarctica, particularly with simulation of cloud properties that regulate snow surface melting or refreezing. Atmospheric and climate-related field work has significant challenges in the Antarctic, due to the small number of research stations that can support state-of-the-art equipment. Here we present new data from a suite of transportable and solar-powered instruments that can be deployed to remote Antarctic sites, including regions where ice shelves are most at risk, and we demonstrate how key components of climate model simulations can be evaluated against these data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Simulating metagenomic stable isotope probing datasets with MetaSIPSim

DNA-stable isotope probing (DNA-SIP) links microorganisms to their in-situ function in diverse environmental samples. Combining DNA-SIP and metagenomics (metagenomic-SIP) allows us to link genomes from complex communities to their specific functions and improves the assembly and binning of these targeted genomes. However, empirical development of metagenomic-SIP methods is hindered by the complexity and cost of these studies. We developed a toolkit, ‘MetaSIPSim,’ to simulate sequencing read libraries for metagenomic-SIP experiments. MetaSIPSim is intended to generate datasets for method development and testing. To this end, we used MetaSIPSim generated data to demonstrate the advantages of metagenomic-SIP over a conventional shotgun metagenomic sequencing experiment. Through simulation we show that metagenomic-SIP improves the assembly and binning of isotopically labeled genomes relative to a conventional metagenomic approach. Improvements were dependent on experimental parameters and on sequencing depth. Community level G + C content impacted the assembly of labeled genomes and subsequent binning, where high community G + C generally reduced the benefits of metagenomic-SIP. Furthermore, when a high proportion of the community is isotopically labeled, the benefits of metagenomic-SIP decline. Finally, the choice of gradient fractions to sequence greatly influences method performance. Metagenomic-SIP is a valuable method for recovering isotopically labeled genomes from complex communities. We show that metagenomic-SIP performance depends on optimization of experimental parameters. MetaSIPSim allows for simulation of metagenomic-SIP datasets which facilitates the optimization and development of metagenomic-SIP experiments and analytical approaches for dealing with these data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

Abstract Background Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. Methods We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Results Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. Conclusion The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Dataset: Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery

This dataset supports the implementation described in the manuscript "Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery." It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper. For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.

generative and adversarial learning↗
DOE OSTI2021

SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 3 (Raw Temperature Replica Exchange Molecular Dynamics Trajectories)

Raw temperature replica-exchange molecular dyanmics trajectories of 23 different SARS CoV-2 Systems including S Protein ACE2-receptor binding-domain, MPro, PLPro, NSP3 ADRP (X-domain/macrodomain/phosphatase), NSP15 (endoribonuclease), NSP9, NSP10, NSP16, and N-protein N-terminus. Systems were prepared with charmm-gui and simulated using the GROMACS simulation software suite. Non-demuxed, i.e. discontinuous/constant temperature window, trajectories are provided in compressed GROMACS xtc and full trr formats. This data supplements the data releases DOI: 10.13139/OLCF/160650 'SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)' and DOI: 10.13139/OLCF/1657844 'SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 2 (Extracted Protein Coordinate Trajectories)'

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Summit Darshan Archival Dataset

Summit Darshan Archival Dataset contains 2021 Summit Darshan log data for 25 applications and is grouped into science domains. The dataset is processed, and all the propriety fields are anonymized. The resultant data is converted into a tabular structure and saved in parquet file format. In this notebook, we demonstrate how to access the data. Data Organization: The data is organized into two directories: Darshan total (`darshan_total`): List all the high levels generated by the `darshan-parser --total` command on `.darshan` files. There is one parquet file for each application. Note: `uid` and `exe` field are masked Darshan detail (`darshan_detail`): This data contains detailed job level log information extracted by command `darshan-parser` on the raw `.darshan` files. The data is sorted by directory hierarchy in the order of `year/month/day (2021/12/07)`. For instance, to get the data for a `job_id` 3819766 of application `App11`, which was executed on `2021-12-07`can be accessed as follows. Note:`uid` and `filename` fields are masked

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Putting error bars on density functional theory dataset

This dataset contains submission files and raw output files from high-throughput DFT simulations to analyze the systemic errors in lattice constant, bulk moduli and formation energy predictions for a range of binary and ternary oxides using four exchange correlation functionals (LDA, PBE, PBEsol and vdW-DF-C09). This data was then used as the basis for employing materials informatics methods to predict the expected errors in the lattice constants of the studied compounds. Predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide “error bars” for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

36 MATERIALS SCIENCE↗