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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
DOE OSTI2022

Task 5 Field Scale Stress Modeling Scripts and Data

Project data and Matlab scripts for Task 5 of the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO2 Storage”. Project includes the static earth models, grids and other data used for simulation and calibration of the two case studies addressed in the project, referred to herein as the “FutureGen2” model (Illinois) and the “Perch” model (Otsego county, Michigan). Large Dataset, please contact EDXSupport@netl.doe.gov

FutureGen2↗
DOE OSTI2022

Effective Field Theory Analysis of CDMSlite Run 2 Data

CDMSlite Run 2 was a search for weakly interacting massive particles (WIMPs) with a cryogenic 600 g Ge detector operated in a high-voltage mode to optimize sensitivity to WIMPs of relatively low mass from 2 - 20 GeV/$c^2$. In this article, we present an effective field theory (EFT) analysis of the CDMSlite Run 2 data using an extended energy range and a comprehensive treatment of the expected background. A binned likelihood Bayesian analysis was performed on the recoil energy data, taking into account the parameters of the EFT interactions and optimizing the data selection with respect to the dominant background components. Energy regions within 5$\sigma$ of known activation peaks were removed from the analysis. The Bayesian evidences resulting from the different operator hypotheses show that the CDMSlite Run 2 data are consistent with the background-only models and do not allow for a signal interpretation assuming any additional EFT interaction. Consequently, upper limits on the WIMP mass and coupling-coefficient amplitudes and phases are presented for each EFT operator. These limits improve previous CDMSlite Run 2 bounds for WIMP masses above 5 GeV/$c^2$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2021

A comprehensive analysis of transient pressure and rate data from CO 2 storage projects in a depleted pinnacle reef oil field complex, Michigan, USA

Pressure and rate data are commonly recorded as part of a basic monitoring program in CCS projects. This paper discusses the application of multiple analytical techniques to interpret pressure and rate transient data from CO 2 injection and storage operations. The techniques of interest, i.e., injection-falloff analysis, injectivity/productivity index analysis and pressure pulse arrival time analysis, are commonly used in the oil and gas industry to assess reservoir properties, but not well known in the CCS literature (especially the last two). Injection-falloff analysis involves log-log pressure derivative plotting for the falloff data and history-matching of the entire injection-falloff sequence to determine permeability. In the injectivity/productivity index analysis, rate-normalized pressure buildup is plotted against material balance time or ratio of cumulative injection to injection rate to determine the injectivity index (ratio of injection rate to stabilized pressure buildup) which can be related to the permeability-thickness product. The arrival time analysis identifies the arrival of a pressure disturbance (~0.1 psi change from ambient) to determine the hydraulic diffusivity from which permeability can be estimated. The applicability of these techniques is demonstrated via illustrative examples from multiple wells in different pinnacle carbonate reefs undergoing CO 2 -EOR in Northern Michigan. The paper ends with a discussion of the relative merits of each interpretive technique, as well as recommendations that could be useful for other field projects.

42 ENGINEERING↗
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↗
DOE OSTI2020

Model-less Source Location for Forced Oscillation based on Synchrophasor and Moving Fast Fourier Transformation

Forced oscillations in power systems occur when the grid is driven by an external and periodic force. To quickly detect and locate the source of the forced oscillation is critical in terms of ensuring the reliability of an interconnected power grid. This paper explores the electromechanical wave propagation theory and the Fast Fourier Transformation to analyze the forced oscillations. It proposes a model-less, adaptive, fast, and accurate source location algorithm. The proposed algorithm is extensively evaluated through simulation data from a 70k-bus U.S. Eastern Interconnection test system and field-collected synchrophasor data from the distribution-level wide-area monitoring system, FNET/GridEye. The evaluation results demonstrate the correctness and effectiveness of the proposed model-less forced oscillation source location algorithm.

Wang, Weikang↗
DOE OSTI2025

Data and Scripts associated with a manuscript on ecosystem responses to wildfires in the Columbia River Basin

This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Field-Array Benchmark of Commercial Bifacial PV Technologies with Publicly Available Data

We present results for a 75-kW field array deployed with rows of 5 different commercially available bifacial technologies. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed in the 10-row field. High accuracy string-level DC monitoring and module-level measurements have been recorded for six months. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. A custom-module to measure shading loss from the torque-tube was installed. One month data shows up to 6% irradiance non-uniformity. A fixed-tilt test-site with 3 years of bifacial data is also presented, with a bifacial gain of 3%.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Nitrogen derived from Combined Algal Processing supports algae cultivation for biofuels

Algae biomass, a biofuel feedstock with potential to reduce global CO 2 emissions, is currently too expensive Nitrogen, a significant input and life cycle cost, requires research on recycling and additional sources to reduce costs. Integral to nitrogen recycling is the knowledge of actual nitrogen requirements in outdoor ponds, thus data from the Unified Field Studies of the Algae Testbed Public-Private Partnership was analyzed. The data showed that Nannochloropsis oceanica used 14–24 mg/L NH 4 + per harvest cycle for robust growth. Nitrogen recycled directly from the algae conversion process will likely be the primary source of nitrogen for algae cultivation. Harvested algae can be fractionated via Combined Algal Processing (CAP), where algae are pretreated, fermented, and the fuel feedstocks (e. g. ethanol, lipids) are removed leaving a nitrogen rich slurry. Chlorella vulgaris grew robustly in nitrogen-free media supplemented with CAP residues through 4 harvest cycles with no inhibition. The amount of nitrogen as free ammonium available for recycling directly without further processing was insufficient (~4% of total needed), thus the nitrogen fixing bacteria, Azotobacter vinelandii, was investigated as a means for increasing ammonium concentration in CAP residues. This organism was shown to be proficient at growth on sugars liberated from algae biomass during pretreatment and sustained algae growth using A. vinelandii supernatants was also demonstrated. Using Azotobacter to increase ammonium concentrations on-site using an inexpensive biological process to generate the necessary ammonium for outdoor algae cultivation improves sustainability of algae biofuels by avoiding the energy intensive steps of typical ammonium production and transport. Furthermore, these results show that byproducts of algae conversion begin to close nitrogen demand and, with further supplementation by diazotrophic bacteria or protein hydrolysis, may completely replace current energyintensive and costly industrial nitrogen sources offering a path to increased economic feasibility and improved life cycle analysis of algae-based biofuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Block Island Noise Modeling Data

Noise propagation near Block Island was simulated to assess environmental impacts of impact pile driving during wind turbine construction. Computational models complement field-recorded acoustic data, providing insights into sound attenuation, spectral variability, and propagation dynamics. The dataset includes: 1. Propagation Models: Simulated underwater sound fields documenting sound pressure and directional variability across frequency bands and distances. 2. Spectral Analysis (LTSA): Long-term averages and processed outputs calculating acoustic intensity over time. 3. Visualization Files: Graphs, 2D/3D simulation results, and reference calculations used in sound modeling.

17 WIND ENERGY↗
DOE OSTI2023

WPEC SG50: Developing an Automatically Readable, Comprehensive and Curated Experimental Nuclear Reaction Database

The Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) Working Party on International Nuclear Data Evaluation Co-operation (WPEC) subgroup (SG) 50 was formed in 2020 to develop an automatically readable, comprehensive and curated experimental nuclear reaction database. This database is called MEDUSAL (Machine-readable Experimental Data User Application & Library), and will draw from EXFOR. The EXFOR database preserves experimental nuclear reaction data true to its original documentation and information from the authors of the data. MEDUSAL will deviate from EXFOR by storing additional information from users of the data for their fields of work (evaluation, model development, validation, etc.). This includes expert judgment on the data sets, identification of data points as outliers, renormalization of the data to the newest monitor reactions, and estimations of missing uncertainty sources. The format for MEDUSAL is being developed to enable easy automatic parsing of large amounts of data. Here, we will summarize the use cases, high-level requirements and first steps towards developing the database MEDUSAL and the API to access it.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗