Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “FILES”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation

This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by 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. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: 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. * Todorov and Worsham are co–first authors.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗
DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗
DOE OSTI2021

Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012

Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities. This data package includes ten csv files (five data files and their corresponding data dictionaries) and one file-level metadata excel file. Data files contains information about which plots were exposed to treatments related to drought and nitrogen, information about litter bags reciprocal transplants manipulation for water input and nitrogen, detail information about water addition, precipitation records, and litter variables collected. Data dictionary files include detail explanation for each column in the data files. The file-level metadata file describes each file mentioned above. All the analyses were done using the R software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗
DOE OSTI2012

Generalized Nuclear Data: A New Structure (with Supporting Infrastructure) for Handling Nuclear Data

The Evaluated Nuclear Data File (ENDF) format was designed in the 1960s to accommodate neutron reaction data to support nuclear engineering applications in power, national security and criticality safety. Over the years, the scope of the format has been extended to handle many other kinds of data including charged particle, decay, atomic, photo-nuclear and thermal neutron scattering. Although ENDF has wide acceptance and support for many data types, its limited support for correlated particle emission, limited numeric precision, and general lack of extensibility mean that the nuclear data community cannot take advantage of many emerging opportunities. More generally, the ENDF format provides an unfriendly environment that makes it difficult for new data evaluators and users to create and access nuclear data. The Cross Section Evaluation Working Group (CSEWG) has begun the design of a new Generalized Nuclear Data (or 'GND') structure, meant to replace older formats with a hierarchy that mirrors the underlying physics, and is aligned with modern coding and database practices. In support of this new structure, Lawrence Livermore National Laboratory (LLNL) has updated its nuclear data/reactions management package Fudge to handle GND structured nuclear data. Fudge provides tools for converting both the latest ENDF format (ENDF-6) and the LLNL Evaluated Nuclear Data Library (ENDL) format to and from GND, as well as for visualizing, modifying and processing (i.e., converting evaluated nuclear data into a form more suitable to transport codes) GND structured nuclear data. GND defines the structure needed for storing nuclear data evaluations and the type of data that needs to be stored. But unlike ENDF and ENDL, GND does not define how the data are to be stored in a file. Currently, Fudge writes the structured GND data to a file using the eXtensible Markup Language (XML), as it is ASCII based and can be viewed with any text editor. XML is a meta-language, meaning that it has a primitive set of definitions for representing hierarchical data/text in a file. Other meta-languages, like HDF5 which stores the data in binary form, can also be used to store GND in a file. In this paper, we will present an overview of the new GND data structures along with associated tools in Fudge.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Design of an experimental breeder reactor run 138B reactor physics benchmark evaluation management application

Reactor Monte Carlo simulations present numerous challenges including input file generation and processing output files for relevant results. Creating an input file for a heterogeneous core is further complicated when using depleted fuel. Performing perturbations to geometry and material characteristics exacerbates the challenge due to human errors if the input files are manually generated. The difficulty of generating input files is often encountered during the process of performing a reactor physics benchmark evaluation. These challenges were encountered in this study when performing a reactor physics benchmark evaluation for the Experimental Breeder Reactor II (EBR-II), a sodium cooled fast reactor. The EBR-II Monte Carlo input file uses more than 1,000 unique material specifications and ~8,000 regions. To address the input file generation challenge, a computer application was designed to automate the input file generation process. The application utilizes user input from a graphical user interface in conjunction with spreadsheets containing material and geometric data to generate Monte Carlo input files. The application, called MICKA, was used to generate input files to support the reactor physics benchmark evaluation for EBR-II. This paper describes the reactor configuration and provides information about the application design and capabilities for addressing challenges often encountered in reactor physics benchmark evaluations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Data from: "Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest"

This data package contains data used to support conclusions drawn in “Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest”, by Kueppers et al. 2017. Data were collected in field sites within the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, on the eastern slope of the Colorado Rocky Mountains, USA. Files containing geospatial data are also included, to provide additional locational context.There are four document formats associated with this archive: three comma-separated values (.csv) files, three Microsoft Excel (.xlsx) files, one .pdf data user’s guide, four keyhole markup language (.kml) files, and a compressed folder containing seven ESRI shapefiles (.shp). The .csv files can be opened using any simple text-editor software, R, or Microsoft Excel. The .xlsx files can only be opened using Microsoft Excel. The .kml file can be opened by Google Earth and Google Maps, and the shapefiles can be opened with any GIS application compatible with the file type, such as ESRI’s ArcGIS, and QGIS.We provide two versions of the seedling data file: “PIEN_PIFLseedlings20150522_20150525rev12222020.csv/.xlsx” (hereafter PIEN_PIFLseedlings2015) and “PIEN_PIFLseedlings20160408rev12222020.csv/.xlsx” (hereafter PIEN_PIFLseedlings2016). PIEN_PIFLseedlings2015 contains the data we used in the paper. PIEN_PIFLseedlings2016 contains an updated version of these data that includes sampling from later years. The main differences between the two files lie in the columns titled “k[YEAR],” which describe the number of seedlings that were killed in a particular year. In PIEN_PIFLseedlings2016, there also is an additional year of data for k2015, and k2014 also has additional data input for the 2014 cohort. Additionally, in years 2010-2014, there are minor differences in the number of seedlings killed -- in as few as 0 plots (in 2011) to as many as 5 plots (in 2014) -- due to errors in data input that were rectified in later years.------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Upslope range shifts by subalpine tree species are a widely anticipated effect of climate change. Climate niche models predict subalpine forests to expand upslope, given more suitable growing conditions for adult trees. However, these models do not take into account climates required for successful seedling recruitment and establishment, an essential element for expansion. Further, localized upper treeline populations are hypothesized to contain favorable traits for colonizing the alpine. To test these expectations and to expand our knowledge of seedling recruitment under climate change, we designed a common garden, climate-warming experiment spread across an elevation gradient at Niwot Ridge in the Colorado Rocky Mountains. We focus on two widespread Western North American species, Engelmann spruce (Picea engelmannii Parry ex. Engelm) and limber pine (Pinus flexilis James), which occur at treeline. While the former is considered a late-seral species more tolerant of shade, limber pine is a shade-intolerant pioneer species able to establish on infertile sites.Every autumn, seeds of the two species were collected from high- (3370 m–3570 m) and low-provenance (2910–3240 m) sources close to the experimental sites and sown in our plots. A subset of plots were heated and another subset watered over the summer months to offset the effects of warming. Across five years, we found that seeds originating from low elevation recruited more strongly for both species, although this provenance difference diminished by the fourth year for Engelmann spruce, likely due to small sample sizes. Despite the recruitment of low-provenance seed, warming treatments decreased recruitment at all elevations. Combining this with the likeliness and availability of lower-quality, high provenance seed moving upslope at the treeline, tree migration into the alpine may be slowed. Overall, our findings suggest that the hardier limber pine is likely to become a more significant species in subalpine forest communities in the future, while the more sensitive Engelmann spruce may experience range contraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Deep PDF parsing to extract features for detecting embedded malware.

The number of PDF files with embedded malicious code has risen significantly in the past few years. This is due to the portability of the file format, the ways Adobe Reader recovers from corrupt PDF files, the addition of many multimedia and scripting extensions to the file format, and many format properties the malware author may use to disguise the presence of malware. Current research focuses on executable, MS Office, and HTML formats. In this paper, several features and properties of PDF Files are identified. Features are extracted using an instrumented open source PDF viewer. The feature descriptions of benign and malicious PDFs can be used to construct a machine learning model for detecting possible malware in future PDF files. The detection rate of PDF malware by current antivirus software is very low. A PDF file is easy to edit and manipulate because it is a text format, providing a low barrier to malware authors. Analyzing PDF files for malware is nonetheless difficult because of (a) the complexity of the formatting language, (b) the parsing idiosyncrasies in Adobe Reader, and (c) undocumented correction techniques employed in Adobe Reader. In May 2011, Esparza demonstrated that PDF malware could be hidden from 42 of 43 antivirus packages by combining multiple obfuscation techniques [4]. One reason current antivirus software fails is the ease of varying byte sequences in PDF malware, thereby rendering conventional signature-based virus detection useless. The compression and encryption functions produce sequences of bytes that are each functions of multiple input bytes. As a result, padding the malware payload with some whitespace before compression/encryption can change many of the bytes in the final payload. In this study we analyzed a corpus of 2591 benign and 87 malicious PDF files. While this corpus is admittedly small, it allowed us to test a system for collecting indicators of embedded PDF malware. We will call these indicators features throughout the rest of this report. The features are extracted using an instrumented PDF viewer, and are the inputs to a prediction model that scores the likelihood of a PDF file containing malware. The prediction model is constructed from a sample of labeled data by a machine learning algorithm (specifically, decision tree ensemble learning). Preliminary experiments show that the model is able to detect half of the PDF malware in the corpus with zero false alarms. We conclude the report with suggestions for extending this work to detect a greater variety of PDF malware.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Processing Meteorological Data for the CAP-88 PC Model at Los Alamos National Laboratory

The Environmental Protection and Compliance-Compliance Programs (EPC-CP) group at Los Alamos National Laboratory (LANL) uses the Clean Air Act Assessment Package 1988 (CAP-88, Littleton 2020) PC model (Version 4.1) to estimate radiological doses for a set of areal sectors surrounding a release location, in order to satisfy the Environmental Protection Agency (EPA) National Emission Standards for Hazardous Air Pollutants (NESHAP) dose calculation requirement in 40 CFR 61 Subpart H. Among several types of data that must be prepared for CAP-88 input is a text file of meteorological data (“WIND” file), consisting of the joint frequency of wind direction, wind speed, and atmospheric stability categories. EPC-CP produces customized WIND files by running a CAP-88 utility program on a user generated text file of wind data in a different format, known as a STability ARray (STAR) file (Turner, 1964). At LANL, EPC-CP meteorologists prepare customized STAR files with data over desired time periods at selected meteorological towers. A custom program written in Precision Visuals -Workstation Analysis and Visualization Environment (PV-WAVE), a commercial Fortran-like language, is used to read LANL meteorological data and write a STAR file; the executable filename is “Star.out”. However, the outdated PV-WAVE utility program is being phased out by EPC-CP, due to the inefficient process to run it and an inability to modify the code. To preserve the ability to create customized meteorological data for CAP-88 in a way that will be easy to use and maintain, a new replacement utility program, written in the Python programming language, has been developed. The new, improved program reads a data file from any LANL meteorological tower, and at each desired observation time, determines the wind direction, wind speed, and stability categories defined in the CAP-88 documentation. The frequencies of all combinations of the three sets of categories are calculated and written to a file in the STAR format, which can later be converted to a WIND file for input into CAP-88.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

HydroFish: freshwater fish co-occurrence with hydropower plants and non-powered dams in conterminous United States sub-basins

The HydroFish dataset lists all existing hydropower plants (EHAs) and non-powered dams (NPDs; ≥ 0.001 MW potential nominal capacity), delineates the hydrologic sub-basins in which they are situated, and then lists all freshwater fish species reported to occur in those sub-basins. This dataset was compiled using the HydroBio dataset (https://hydrosource.ornl.gov/data/datasets/hydrobio/) and contains 24 total variables that describe hydrologic sub-basins, each unique EHA (plant ID and name, geographic coordinates, permit type, capacity, etc.) and NPD (ID value, known names, geographic coordinates, and estimated potential nominal capacity), and freshwater fish species in the sub-basin (common and scientific name, origin, and migratory and threat status). The HydroBio dataset was built using Oak Ridge National Laboratory’s Existing Hydropower Assets Dataset (2024 version) and Non-powered Dam Technical Potential Dataset (2024 version), and NatureServe’s fish species distribution dataset (2023 version). The dataset also contains summary variables that report the unique number of EHAs, NPDs, and freshwater fish species per sub-basin. The HydroFish dataset contains two unique data files: 1) a .csv metadata file describing the dataset variables, and 2) a .csv data file containing the actual dataset. Note that there may be many rows per unique existing hydropower plant or non-powered dam given that distinct species are listed per existing plant or NPD per sub-basin. The dataset is downloadable as a zip file containing the metadata and dataset files.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2018

Global Paleoclimatic Data for 6000 Yr B.P.

To determine regional and global climatic variations during the past 6000 years, pollen, lake level, and marine plankton data from 797 stations were compiled to form a global data set. Radiocarbon dating and dated tephras were used to determine the ages of the specimens. The data available for the pollen data are site number, site name, latitude, longitude, elevation, and percentages of various taxa. For lake-level data, the data are site number, site name, latitude, longitude, and lake-level status. And for marine plankton, the data are site number, site name, latitude, longitude, water depth, date, dating control code, depth of sample, interpolated age of sample, estimated winter and summer sea-surface temperatures, and percentages of various taxa. The data are in 55 files: 5 files for each of 9 geographic regions and 10 supplemental files. The files for each region include (1) a FORMAT file describing the format and contents of the data for that region, (2) an INDEX file containing descriptive information about each site and its data, (3) a DATA file containing the data and available climatic estimates, (4) a PUBINDEX file indexing the bibliographic references associated with each site, and (5) a REFERENCE file containing the bibliographic references. The files range in size from 2 to 66 kB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets

Publication For a thorough description of the methods, see the peer-reviewed paper: Jones, A.D., Rastogi, D., Vahmani, P. et al. Continental United States climate projections based on thermodynamic modification of historical weather. Sci Data 10, 664 (2023). https://doi.org/10.1038/s41597-023-02485-5 Overview The IM3 / HyperFACETS climate simulations provide 40-year historical (1980-2019) as well as four 80-year future simulations (2020-2099) over the U.S. The future simulations are split into near (2020-2059) and far future (2060-2099) segments. The future scenarios span a range of plausible changes in future climate (both Global Circulation Model (GCM) and Representative Concentration Pathways/Shared Socioeconomic Pathway (RCP/SSP) dimensions). The simulations provide climate variables with high spatiotemporal resolution (25 hourly variables and 207 3-hourly variables at 12 km2). The datasets are generated using dynamical downscaling with the WRF (Weather Research and Forecasting) model (version 4.2.1) and therefore preserve physical consistency across variables. WRF is a state-of-the-art, fully compressible, non-hydrostatic, mesoscale numerical weather prediction model. WRF is coupled with an urban canopy model (UCM), which resolves urban surfaces. The future scenarios were developed using a thermodynamic global warming approach where past events are replayed under a range of future warming conditions. These scenarios therefore provide a perspective on potential increases in extreme event intensity, geographic scope, and duration, with previously non-extreme conditions potentially crossing new thresholds to be considered extreme by today's standards. This approach is not intended to estimate future changes in extreme event frequency that might result from changes in large-scale atmospheric dynamics. This dataset has NOT been bias corrected. A bias corrected version of selected variables is under development and will be released here when available. Scenarios Files Data for each scenario is provided in weekly NetCDF files. 25 variables are available at hourly resolution, and 207 variables are available at three-hourly resolution. Spatial resolution is 12km and spans the conterminous United States (CONUS), including some areas of Canada and Mexico, resulting in a grid of 424 by 299 cells. The spatial projection is a Lambert Conformal Conic with the following proj-string: "+proj=lcc +lat_0=40.0000076293945 +lon_0=-97 +lat_1=30 +lat_2=45 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs". The available scenarios and simulation periods are listed below: historical | 1980 - 2019 rcp45cooler | 2020 - 2059 rcp45cooler | 2060 - 2099 rcp45hotter | 2020 - 2059 rcp45hotter | 2060 - 2099 rcp85cooler | 2020 - 2059 rcp85cooler | 2060 - 2099 rcp85hotter | 2020 - 2059 rcp85hotter | 2060 - 2099 * The first year (1979, 2019, and 2059) of data within each scenario represents a model warmup period and should not be used. These are located in the `spinup_files` directory. Historical year 2020 is considered an extra year of data beyond the simulation period and can be found in the `additional_files` directory. For information on specific variables and a more in-depth discussion of methodology, please refer to the data landing page at https://tgw-data.msdlive.org. Delta Warming Files The global and CONUS warming deltas for each scenario are provided in degrees Celsius annually and monthly. Restart Files Yearly restart files are provided for each scenario which can be used to restart the WRF model at a particular point in time. Spinup Files The first year of data within each simulation period represents a model warmup period and should not be used. The files are provided here for the sake of reproducibility. Additional Files Additional years of data are provided as an extension of the historic simulation.

Jones, Andrew D.↗
DOE OSTI2025

Projected Urban Morphology of the Los Angeles Area by the Year 2100

This dataset provides projections of urban building morphologies for the Los Angeles urban area at 30-meter spatial resolution. It contains 192 raster files that detail two primary building attributes: building footprint fractions (ranging from 0 to 1) and average building heights (ranging from 0 to 75 meters). The projections account for a wide range of future pathways, covering two Shared Socioeconomic Pathway (SSP) scenarios (SSP3 and SSP5), two population scenarios, two developed land intensification scenarios, and four distinct levels of intensification. The dataset was created using dual Generative Adversarial Networks (GANs) trained on 2015 land cover and building properties from the National Land Cover Database (NLCD) and Model America datasets. Supporting information on the dataset has been described in the LAUrbanAreaMorphologyProjections2100_README.txt file.

Pandey, Bhartendu↗
DOE OSTI2026

Urban Parameters Los Angeles County 100m version 2

132 Urban parameters based on building physical dimensions and location were generated for the city of Los Angeles at 100m resolution using the NATURF model. To use the binary file with WRF, the binary file and the index file must be placed in their own directory in WRF_GEOG and accessed in the same way NUDAPT44 would be accessed. The kmz file can be visualized on Google Earth.

Sweet-Breu, Levi [Baylor University]↗
DOE OSTI2025

Data for: Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources

Data package for manuscript "Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources" by Emily G. Wright, Ivey Wang, Yihang Fang, Elaine D. Flynn, and Jeffrey G. Catalano. This dataset contains adsorption results from experiments designed to investigate the effect of chloride on Pd(II) adsorption to goethite and Pt(II) adsorption to hematite and goethite, including lab experiments, X-ray absorption fine structure spectroscopy, and models of retention within a laterite. See the associated manuscript for full methods information. The file "Wright2025_PtAds_data.csv" contains the target starting Pt concentration (uM), final aqueous Pt and associated error (in uM), calculated adsorbed Pt and associated error (in umol/m2), target and measured aqueous chloride (mM), target aqueous nitrate (mM), final pH, and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, S20, and S22 in the associated manuscript. The file "Wright2025_PdAds_data.csv" contains the target starting Pd concentration (uM), final aqueous Pd and associated error (in uM), calculated adsorbed Pd and associated error (in umol/m2), target and measured aqueous chloride (mM), and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, and S20 in the associated manuscript. The file "Wright2025_MineralBatches_data.csv" contains the mineral identity and BET specific surface area (m2/g) for every mineral batch synthesized and used in experiments. The annealing time used is listed for hydrothermally annealed goethite. These data appear in Table S2 in the associated manuscript. The file "Wright2025_XRD_data.csv" contains the XRD patterns for every mineral batch synthesized as the counts as a function of two theta (in degrees). See "Wright2025_MineralBatches_data.csv" for more details on specific mineral batches. These data appear in Figure S2 in the associated manuscript. The file "Wright 2025_ZetaPotential_data.csv" contains the measured zeta potentials for samples of goethite (batch G2) at pH 4 the presence of varying amounts of sodium chloride. These data appear in Table S3 in the associated manuscript. The file "Wright2025_XAFSSamples_data.csv" contains the specific mineral batch, measured final aqueous Pd or Pt (uM), measured final aqueous chloride (mM), and estimated adsorbed Pd or Pt (umol/m2) of all XAFS samples. These data appear in Tables S4, S7, S8, and S10 in the associated manuscript. The files "Wright2025_PdXAFS_data.csv" and "Wright2025_PtXAFS_data.csv" contain the normalized spectra of Pd and Pt, respectively, adsorbed to minerals at varying chloride concentrations. See "Wright2025_XAFSSamples_data.csv" for a guide to sample names. Note that "05" in a sample name is equivalent to "0.5". These data appear in Figures 2, S6, S7, S8, S12, S13, and S14 in the associated manuscript. The file "Wright2025_LateriteProfileProfileModelParameters_data.csv" include the ratio of hematite to hematite and goethite in two synthetic, modeled profiles, as well as the modeled surface areas of goethite and hematite as a function of relative depth within the modeled weathering zone. These data were used, in conjunction with equations presented in the paper, to calculate the theoretical concentrations of Pd and Pt (and the resulting Pt/Pd ratio) within the profiles. These data appear in Figure 3 in the associated manuscript. The file "Wright2025_Imagery_data.zip" is a zipped folder containing the TEM and STEM images appear in Figures S18 and S19. Individual files are labeled as either STEM (Fig. S18) or TEM (Fig. S19) with a letter representing the part of the multipart figure.

58 GEOSCIENCES↗
NASA NTRS1984

NIMBUS 7 Earth Radiation Budget (ERB) Matrix User's Guide. Volume 2: Tape Specifications

The ERB MATRIX tape is generated by an IBM 3081 computer program and is a 9 track, 1600 BPI tape. The gross format of the tape given on Page 1, shows an initial standard header file followed by data files. The standard header file contains two standard header records. A trailing documentation file (TDF) is the last file on the tape. Pages 9 through 17 describe, in detail, the standard header file and the TDF. The data files contain data for 37 different ERB parameters. Each file has data based on either a daily, 6 day cyclic, or monthly time interval. There are three types of physical records in the data files; namely, the world grid physical record, the documentation mercator/polar map projection physical record, and the monthly calibration physical record. The manner in which the data for the 37 ERB parameters are stored in the physical records comprising the data files, is given in the gross format section.

Ray, S. N.↗