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At least 865 records · Page 48

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. 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.

2018 NEON and 2025 CHESS Campaigns↗

ML for microbiomes

The software provides machine learning analysis and visualization to detect patterns in microbiome data, including topic modeling, probabilistic graphical modeling, conventional machine learning methods, and deep learning. The software is written in python and R, it uses some python and R libraries as well as big open-source libraries like sklearn, networkX, pytorch (python), pgmpy (python), and bnlearn (R). It also has a script to use for MALLET and DTM (open-source packages for topic modeling, written in Java).

Kim, Anastasiia↗

Scripts and data associated with a manuscript linking soil and sediment elemental composition with dissolved organic matter chemistry across CONUS

This data package provides scripts and geochemical data for a manuscript titled “Linkages between mineral element composition of soils and sediments with hyporheic zone dissolved organic matter chemistry across the contiguous United States” (preprint: doi: 10.22541/essoar.169447343.31694990/v1). This data is associated with the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS, https://whondrs.pnnl.gov) and is an extension of the Summer 2019 Sampling campaign which crowdsourced samples from rivers and sediment across the continental United States. Data from this study can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719. The main objective of this manuscript was to couple sediment water extractable dissolved organic matter chemistry, defined by ultra-high resolution mass spectrometry, with localized sediment elemental composition and watershed scale soil elemental characteristics. This data package contains one main folder with four subfolders. The main data folder contains (1) readme; (2) data dictionary (dd); (3) file-level metadata (flmd); (4) an R markdown to reproduce manuscript figures and analyses; (5) a pdf of instructions to reproduce NGS interpolations with ArcGIS software; and (6) a python script to reproduce NGS extrapolations with python. The four subfolders contain files required to reproduce NGS extrapolations include (1) ‘CONUS_boundaries’ containing boundary layers (.shp) for the Continental United States; (2) ‘ngs_project’ containing files (.shp) with point level NGS soil elemental data (Grossman et al., 2004); (3) ‘raster_outputs’ containing the interpolated raster output files for various soil elements; and (4) ‘NGS_Chemistry_Final’ contain final extracted soil elemental data.

54 ENVIRONMENTAL SCIENCES↗

Surface Water Quality Data from Beaver-Impacted Streams; Trail Creek and East River, Colorado 2025

This data package contains surface water chemistry measurements collected in 2025 to evaluate how beaver damming and low-tech process-based stream restoration influence water quality and metal mobility in mountainous headwater systems of the Upper Colorado River Basin. Sampling was conducted at Trail Creek (Taylor Park watershed, Colorado), a tributary undergoing restoration through installation of low-tech process-based structures (i.e., beaver dam analogs), and at off-channel beaver ponds within the East River floodplain (East River watershed, Colorado). Samples were collected along longitudinal transects spanning upstream control reaches, beaver-influenced ponded reaches, and downstream segments. Additional samples were collected from near-surface pore waters within a beaver dam seepage face. The dataset includes concentrations of major and trace elements measured by inductively coupled plasma–mass spectrometry (ICP-MS) and inductively coupled plasma–optical emission spectrometry (ICP-OES), major anions measured by ion chromatography (IC), and dissolved organic carbon (DOC; reported as non-purgeable organic carbon, NPOC). Samples were size-fractionated at 0.45 micrometers (µm), 0.22 µm, and 0.02 µm to distinguish particulate (>0.45 µm), colloidal (0.22–0.02 µm), and dissolved (<0.02 µm) fractions. The data package consists of comma-separated value (.csv) files containing tabulated chemical concentration data, sample metadata (site identifiers, geographic coordinates, sampling dates, fraction type), and quality control flags. All files are provided in open, non-proprietary formats that can be accessed using standard data analysis software such as Microsoft Excel, R, Python, MATLAB, or other programs capable of reading .csv files. Units, detection limits, and analytical methods are documented in accompanying metadata files. The dataset is designed to support analyses of (1) how beaver impoundment and restoration structures alter elemental partitioning and transport, (2) the role of iron and organic carbon in mediating trace metal mobility, and (3) reach-scale changes in water quality across restoration gradients. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Anions↗

PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles

PetroFit is an open-source Python package based on Astropy and Photutils that can calculate Petrosian profiles and fit galaxy images. It offers end-to-end tools for making accurate photometric measurements, estimating morphological properties, and fitting 2D models to galaxy images. Petrosian metric radii can be used for model parameter estimation and aperture photometry to provide accurate total fluxes. Correction tools are provided for improving Petrosian radii estimates affected by galaxy morphology. PetroFit also provides tools for sampling Astropy-based models (including custom profiles and multicomponent models) onto image grids and enables point-spread function convolution to account for the effects of seeing. These capabilities provide a robust means of modeling and fitting galaxy light profiles. We have made the PetroFit package publicly available on GitHub (PetroFit/petrofit) and PyPi (pip install petrofit).

79 ASTRONOMY AND ASTROPHYSICS↗

BPP R package

The BPP package contains R code to perform Bayesian projection pursuit regression under various regression settings. Bayesian projection pursuit regression learns the functional relationship between inputs and outputs by considering a sum of functions of one dimensional projections of the inputs. This is useful as a general machine learning software.

Francom, Devin↗

Harmonized geospatial data to support infrastructure siting feasibility planning for energy system transitions

Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 264 suitability layers for use with 56 power plant technologies in a harmonized format that can be easily ingested by geospatially-enabled modeling software allowing for customization to robustly address science objectives when evaluating varying future conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Polaris Studio

SF-24-040 Polaris-studio is the Python entry point/front end for using the Polaris transportation simulator. The package contains the code used to create new models from open and user-provided data, an extensive range of convenience tools for data preparation and result analysis and the ability of designing, launching and tracking studies combining multiple individual Polaris simulations from a Python terminal. The Polaris Studio software contains several modules for accomplishing the above tasks, including the PolarisLib - the Polaris object model, Polaris Manager - code for setting up and running Polaris studies, QPOLARIS - an add-on for the QGis software to allow model files to be created and edited, PolarisVis - a juptyer-based postprocessing library, and Polaris Tools - a useful collection of supplemental tools for interacting with Polaris files.

Auld, Joshua↗

Geant4 based positron beam source (GPos) v1.0

GPos is a software that was created to determine the properties of positron beams resulting from the interaction of the LBNL BELLA center PetaWatt laser-driven plasma-capillary accelerated electron beam and the atoms of a thin solid target. GPos is written in C++, easily compiled with cmake and the spack package manager, which allows for multi-thread and MPI parallel computing. Its functions expand on the Geant4 toolkit library and allow for propagation of the modelled particles through vacuum drift distances with a focusing element (thin lens approximation). Users can change beam-foil-drift-lens parameters - to adapt GPos to other particle sources and infrastructures - in a simple input file. The code particle data output format, openPMD, which is compatible, for example, with the input of the ECP WarpX project code used to explore the physics of particle acceleration in plasmas. Using GPos in conjunction to WarpX allowed us to test various configurations for designing a high-quality and high-energy positron source at BELLA -required for us to address positron acceleration challenges in the development of future linear colliders. GPos can also be advantageous when tackling the physics of muon sources for future muon colliders as well as for the investigation of positron sources in lower energy regimes for applications like annihilation spectroscopy and astrophysical gamma-ray-bursts.

Pinto de Almeida Amorim, Ligia↗

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗

bifacial_radiance: a python package for modeling bifacial solar photovoltaic systems

bifacial_radiance is a national-laboratory-developed, community-supported, open-source toolkit that provides a set of functions and classes for simulating the performance of bifacial photovoltaic (PV) systems. (Bifacial PV modules collect light on the front as well as the rear side.) bifacial_radiance automates calculations of PV system layout and performance to use along with the popular ray-tracing software tool RADIANCE (Ward, 1994). Specific algorithms include design and layout of PV modules, reflective ground surfaces, shading obstructions, and irradiance calculations throughout the system, among others. bifacial_radiance is an important component of a growing ecosystem of open-source tools for solar energy (William F Holmgren et al., 2018).

97 MATHEMATICS AND COMPUTING↗

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗

Introducing the Video In Situ Snowfall Sensor (VISSS)

The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

54 ENVIRONMENTAL SCIENCES↗

A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines

Abstract. The article presents a symbolic framework (also called computer algebra program) that is used to obtain, in symbolic mathematical form, the linear and nonlinear equations of motion of a mid-fidelity multibody system including rigid and flexible bodies. Our approach is based on Kane's method and a nonlinear shape function representation for flexible bodies. The shape function approach does not represent the state of the art for flexible multibody dynamics but is an effective trade-off to obtain mid-fidelity models with few degrees of freedom, taking advantage of the separation of space and time. The method yields compact symbolic equations of motion with implicit account of the constraints. The general and automatic framework facilitates the creation and manipulation of models with various levels of complexity by adding or removing degrees of freedom. The symbolic treatment allows for analytical gradients and linearized equations of motion. The linear and nonlinear equations can be exported to Python code or dedicated software. There are multiple applications, such as time domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins. In this article, we describe the method we used to systematically generate the equations of motion of multibody systems and present the implementation of the framework using the Python package SymPy. We apply the framework to generate illustrative land-based and offshore wind turbine models. We compare our results with OpenFAST simulations and discuss the advantages and limitations of the method. The Python implementation is provided as an open-source project.

Branlard, Emmanuel (ORCID:0000000277506128)↗

SAM Plug-in Development (Phase I Final Report)

The DOE Office of Nuclear Energy (NE) has created an extensive set of advanced modeling and simulation tools for nuclear engineering analysis. The advanced capabilities of these newer analysis codes require more in-depth training, skills, and knowledge in order to effectively utilize them for the design, analysis, and licensing of advanced nuclear systems and experiments. A high learning curve for inexperienced users may deter organizations from incorporating these tools into their internal processes. This project involved development of a plug-in to the Symbolic Nuclear Analysis Package (SNAP) for the System Analysis Module (SAM) tool. SAM is an advanced system analysis tool for reactor transient analyses being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SNAP provides a highly flexible framework for creating, modifying and documenting input for engineering analysis codes such as SAM as well as extensive functionality for submitting, monitoring, and interacting with the codes through an intuitive graphical user interface (GUI). The common user interface provided by SNAP minimizes the learning curve for engineers starting with a new analysis code and provides an intuitive framework for transitioning between different analysis codes. SNAP provides a powerful but intuitive interface to facilitate access to advanced modeling and simulation tools for inexperienced users. Unlike many “form based” GUI’s, SNAP maps each engineering code’s component input to an internal database which manages all component input parameters along with component interconnections. This level of abstraction permits SNAP to support several advanced capabilities such as renodalization, model validation and consistency checks, embedded documentation, model notebook generation, data ownership and reviewer tracking, and variable assignment for inputs to name a few. SNAP includes a built-in Python interpreter and is interfaced to several commercial and open source packages including CPython, MATLAB/OCTAVE, Microsoft Office, Open Office, and SANDIA’s DAKOTA package which provides Uncertainty Quantification analysis through the SNAP plug-ins. Phase I of this project involved development a fully functional basic SAM plug-in to SNAP. This plug-in provides the ability to import existing models, graphically construct, edit and submit models using SNAP’s extensive functionality.

99 GENERAL AND MISCELLANEOUS↗

Exascale models of stellar explosions: Quintessential multi-physics simulation

The ExaStar project aims to deliver an efficient, versatile, and portable software ecosystem for multi-physics astrophysics simulations run on exascale machines. The code suite is a component-based multi-physics toolkit, built on the capabilities of current simulation codes (in particular Flash-X and Castro), and based on the massively parallel adaptive mesh refinement framework AMReX. It includes modules for hydrodynamics, advanced radiation transport, thermonuclear kinetics, and nuclear microphysics. The code will reach exascale efficiency by building upon current multi- and many-core packages integrated into an orchestration system that uses a combination of configuration tools, code translators, and a domain-specific asynchronous runtime to manage performance across a range of platform architectures. The target science includes multi-physics simulations of astrophysical explosions (such as supernovae and neutron star mergers) to understand the cosmic origin of the elements and the fundamental physics of matter and neutrinos under extreme conditions.

79 ASTRONOMY AND ASTROPHYSICS↗

time-resolved spectroscopy fit (trspecfit) v0.01

Analyze 2D time- and energy-resolved data, such as from a pump-probe spectroscopy experiment. User can select and input different peak shapes/ functions and background types to first fit a ground state/ unperturbed spectrum. This would be similar to how standard spectroscopy data is fit. Subsequently, to describe the time domain, users can choose functions that describe the temporal dynamics of one or more spectral features, such as a peak amplitude, peak position, etc. These time dynamics functions can be added or convoluted (e.g. describing an instrument response function) with each other. Functionality to integrate implicit variables leading to distributions of certain parameters/ functions is in development. Alternatively, 2D data can be analyzed one time step at a time to get an idea of the time dynamics of the system before deploying the global 2D fit described above. Typically people write custom software for this purpose. During my PhD I've seen five internal LBL and external researchers write one-off code in different languages to analyze time- and energy-resolved spectra. While this was specifically was for a laser pump - X-ray probe spectroscopy experiment, I'm trying to write a general package for the time-resolved spectroscopy community.

Mahl, Johannes [Lawrence Berkeley National Laborat↗

Gross and Net Soil Methane Flux and Ancillary Data, Edgewater, MD, USA, summer 2022

This data package contains measurements used to quantify methane cycling and environmental conditions in coastal forest soils during the 2022 growing season. It includes time‑series data of soil methane flux, soil respiration, soil temperature, and volumetric water content collected from soil monoliths transplanted along an inundation and salinity gradient. The package also provides one‑time measurements from a stable‑isotope pool‑dilution incubation, including gravimetric water content, methane headspace concentrations, and ¹³CH₄ enrichment over time. Data files are provided in comma‑separated values (CSV) format, with accompanying metadata and readme documentation in PDF and plain‑text formats. All files can be opened with standard software such as R, Python, or spreadsheet programs capable of handling CSV files. The metadata file describes variable definitions, units, processing steps, and the structure of each data table to support reuse and integration with other datasets.

13-C↗