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DOE OSTI2020

WHONDRS Summer 2019 Sampling Campaign: Global River Corridor Sediment FTICR-MS, Dissolved Organic Carbon, Aerobic Respiration, Elemental Composition, Grain Size, Total Nitrogen and Organic Carbon Content, Bacterial Abundance, and Stable Isotopes (v10)

The WHONDRS Summer 2019 Sampling (S19S) Campaign collected samples in 97 globally distributed river corridor systems between July and September 2019. Surficial streambed sediments were collected at three locations within each site (upstream, midstream, and downstream). Surface water was collected at the downstream site. This dataset includes a portion of the data types produced from the sediment samples and does not include any results from the surface water. Surface water data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1603775. The S19S campaign was designed with the science community to ask questions associated with links among core/transient metabolomes, microbial metabolism, biogeochemical function, and physical properties of watershed and river corridor systems. This data package was originally published in December 2020. It was updated in April 2021 (v2; modified files), May 2022 (v3; new and modified files), November 2022 (v4; new and modified files), January 2023 (v5 and v6; new and modified files), February 2023 (v7; new and modified files), August 2023 (v8; new and modified files), July 2025 (v9; new and modified files), and June 2026 (v10; new and modified files). Version 10 includes major restructuring of the files and their formats. See the change history in the readme for details. 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 contains (1) readme, (2) data dictionary, (3) file level metadata, (4) sampling protocol, (5) a data checks report, (6) field metadata, (7) international generic sample number (IGSN) mapping file, (8) joint genome institute (JGI) mapping file, (9) a folder of site photos, and (10) a folder of sample data. The sample data folder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) and FTICR methods; (2) respiration rates and normalized respiration rates calculated from laboratory sediment incubations, (3) X-ray fluorescence (XRF) elemental composition, (4) grain size distribution from the < 2 millimeter fraction of sediment, (5) total organic carbon and total nitrogen content, (6) bacterial abundance flow cytometry data, (7) sediment stable isotopes (C and N), (8) laboratory methods, (9) water and sediment mass and volume used in incubations, (10) a folder with code, inputs, and plots for the respiration rates, (11) a folder containing XRF data, (12) a folder containing unaligned, peak-picked high resolution characterization of dissolved organic matter from sediment via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory), (12) and a folder containing processed FTICR-MS data. All files are .msa, .txt, .spx, .xml, .csv, .pdf, .txt, .jpg, or .jpeg.

13C↗

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 OSTI2023

HarDWR - Cumulative Water Rights Curves

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. This product is the dataset used as input to the WBM model (Grogan et al., in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (submission pending). This database contains 1,744 individual .csv files, two for each Water Management Area (WMA; see here) in the 11-state region. File naming convention: WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either S for surface water rights, or G for groundwater rights. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the ### unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al., in review)

Economics↗

DOE OSTI2024

HarDWR - Cumulative Water Rights Curves

Economics↗

NASA NTRS1986

The LATDYN user's manual

The LATDYN User's Manual presents the capabilities and instructions for the LATDYN (Large Angle Transient DYNamics) computer program. The LATDYN program is a tool for analyzing the controlled or uncontrolled dynamic transient behavior of interconnected deformable multi-body systems which can undergo large angular motions of each body relative other bodies. The program accommodates large structural deformation as well as large rigid body rotations and is applicable, but not limited to, the following areas: (1) development of large flexible space structures; (2) slewing of large space structure components; (3) mechanisms with rigid or elastic components; and (4) robotic manipulations of beam members. Presently the program is limited to two dimensional problems, but in many cases, three dimensional problems can be exactly or approximately reduced to two dimensions. The program uses convected finite elements to affect the large angular motions involved in the analysis. General geometry is permitted. Detailed user input and output specifications are provided and discussed with example runstreams. To date, LATDYN has been configured for CDC/NOS and DEC VAX/VMS machines. All coding is in ANSII-77 FORTRAN. Detailed instructions regarding interfaces with particular computer operating systems and file structures are provided.

Housner, J. M.↗

NASA NTRS2007

Handling Input and Output for COAMPS

Two suites of software have been developed to handle the input and output of the Coupled Ocean Atmosphere Prediction System (COAMPS), which is a regional atmospheric model developed by the Navy for simulating and predicting weather. Typically, the initial and boundary conditions for COAMPS are provided by a flat-file representation of the Navy s global model. Additional algorithms are needed for running the COAMPS software using global models. One of the present suites satisfies this need for running COAMPS using the Global Forecast System (GFS) model of the National Oceanic and Atmospheric Administration. The first step in running COAMPS downloading of GFS data from an Internet file-transfer-protocol (FTP) server computer of the National Centers for Environmental Prediction (NCEP) is performed by one of the programs (SSC-00273) in this suite. The GFS data, which are in gridded binary (GRIB) format, are then changed to a COAMPS-compatible format by another program in the suite (SSC-00278). Once a forecast is complete, still another program in the suite (SSC-00274) sends the output data to a different server computer. The second suite of software (SSC- 00275) addresses the need to ingest up-to-date land-use-and-land-cover (LULC) data into COAMPS for use in specifying typical climatological values of such surface parameters as albedo, aerodynamic roughness, and ground wetness. This suite includes (1) a program to process LULC data derived from observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Terra and Aqua satellites, (2) programs to derive new climatological parameters for the 17-land-use-category MODIS data; and (3) a modified version of a FORTRAN subroutine to be used by COAMPS. The MODIS data files are processed to reformat them into a compressed American Standard Code for Information Interchange (ASCII) format used by COAMPS for efficient processing.

Fitzpatrick, Patrick↗

DOE OSTI2020

Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

MSTAR2019 Code Description and User's Manual

This report describes an ideal cascade model of uranium enrichment that includes side feed and side product streams and flexible input options. It is based on the original MSTAR model developed by Ed Von Halle and implemented in a Visual Basic code. The current version allows the user to specify integer numbers of stages or the stage numbers of external flows instead of assays in those flows. The computational engine is written in FORTRAN-90 and is invoked by a user-friendly GUI written in C++. This version of MSTAR has been demonstrated to operate on Linux, Mac, and Windows platforms, and has undergone significant testing and quality analysis. A number of examples are presented to illustrate the operation of the code and guide the user. The code is extremely fast, and results are returned immediately. The input and output have been specially configured for analysis by the environmental sampling team of the International Atomic Energy Agency (IAEA). A number of additional output features are included to assist the user in visualizing computational results and downloading data to files for use in other analysis software.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1980

Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE). Volume 1: User's guide

An interactive computer program for automatically generating traffic models for the Space Transportation System (STS) is presented. Information concerning run stream construction, input data, and output data is provided. The flow of the interactive data stream is described. Error messages are specified, along with suggestions for remedial action. In addition, formats and parameter definitions for the payload data set (payload model), feasible combination file, and traffic model are documented.

Dupnick, E.↗

NASA NTRS2003

Java Library for Input and Output of Image Data and Metadata

A Java-language library supports input and output (I/O) of image data and metadata (label data) in the format of the Video Image Communication and Retrieval (VICAR) image-processing software and in several similar formats, including a subset of the Planetary Data System (PDS) image file format. The library does the following: It provides low-level, direct access layer, enabling an application subprogram to read and write specific image files, lines, or pixels, and manipulate metadata directly. Two coding/decoding subprograms ("codecs" for short) based on the Java Advanced Imaging (JAI) software provide access to VICAR and PDS images in a file-format-independent manner. The VICAR and PDS codecs enable any program that conforms to the specification of the JAI codec to use VICAR or PDS images automatically, without specific knowledge of the VICAR or PDS format. The library also includes Image I/O plugin subprograms for VICAR and PDS formats. Application programs that conform to the Image I/O specification of Java version 1.4 can utilize any image format for which such a plug-in subprogram exists, without specific knowledge of the format itself. Like the aforementioned codecs, the VICAR and PDS Image I/O plug-in subprograms support reading and writing of metadata.

Deen, Robert↗

NASA NTRS2013

Modeling to Improve the Risk Reduction Process for Command File Errors

The Jet Propulsion Laboratory has learned that even innocuous errors in the spacecraft command process can have significantly detrimental effects on a space mission. Consequently, such Command File Errors (CFE), regardless of their effect on the spacecraft, are treated as significant events for which a root cause is identified and corrected. A CFE during space mission operations is often the symptom of imbalance or inadequacy within the system that encompasses the hardware and software used for command generation as well as the human experts and processes involved in this endeavor. As we move into an era of increased collaboration with other NASA centers and commercial partners, these systems become more and more complex. Consequently, the ability to thoroughly model and analyze CFEs formally in order to reduce the risk they pose is increasingly important. In this paper, we summarize the results of applying modeling techniques previously developed to the DAWN flight project. The original models were built with the input of subject matter experts from several flight projects. We have now customized these models to address specific questions for the DAWN flight project and formulating use cases to address their unique mission needs. The goal of this effort is to enhance the project's ability to meet commanding reliability requirements for operations and to assist them in managing their Command File Errors.

spacecraft↗

DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"

This dataset contains BEPAM model code and input data to the replicate the results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land." The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output_0213-2020_Complete_daycent-agversion-[rental payment level]%_[biomass price].gms. (BEPAM-CRP model-Sep2020.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

biomass price↗

DOE OSTI2020

(U) LNK3DNT Capability in SENSMG Tested with Transport Correction

The SENSMG multigroup neutron sensitivity code can read materials and geometry from redoin and lnk3dnt files written by the PARTISN multigroup discrete ordinates code or the MCNP6 Monte Carlo code. Reference 5 described an initial version of this capability. Reference 6 introduced two important extensions: 1) Reaction-rate ratios are now allowed as responses with redoin/lnk3dnt inputs; and 2) Sensitivities of reaction-rate ratios to mass densities and interface locations are now calculated (for all problems, not just redoin/lnk3dnt problems). In this report, we document the testing of the redoin/lnk3dnt capability when used with transport correction. PARTISN’s transport corrections are described in Ref. 3, and Refs. 7 and 8 discuss the calculation of cross-section sensitivities when transport corrections are applied. References 7 and 8 verify SENSMG for problems with transport correction using central differences. This report verifies the capability with redoin/lnk3dnt inputs by comparison with regular SENSMG inputs. Unlike Refs. 5 and 6, in this report we use a spherical test problem because the sensitivity of the response with respect to the highest-order scattering is zero in cylinders due to the use of flux moments in the inner products.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

mosartwmpy sample input data; 1980 - 1985

Sample input data spanning the years 1980-1985 for running the `mosartwmpy` water routing and management model: https://github.com/IMMM-SFA/mosartwmpy. More information is available in the README.md. Changelog: v0.0.8: Update with new CAP_MIN reservoir parameter, corresponds with mosartwmpy v1.0.0 v0.0.7: new reservoir files with corrected locations based on an exhaustive review by Dan Broman for the 9505 project; corresponding long term mean demand and flow files updated based on VIC4 simulations using Daymet forcing. v0.0.6: updates reservoir parameters to indicate which reservoirs should follow generic operating rules and which should follow ISTARF data-driven operating rules. v0.0.5: adds supporting files for running the Farmer Agent Based Model of adaptive water demand.

Bracken, Cameron [Pacific Northwest National Labor↗

DOE OSTI2024

Fusion Neutron Generator

The proposed code, named FROG (Fusion neutron Generator) is built upon the open-source particle transport Monte Carlo toolkit Geant4. Geant4 provides C++ classes that can be leveraged to build application-specific codes dealing with the transport of particles through matter. Geant4-based codes are applied in high-energy particle physics experiments, medical applications, shielding, and space applications for example. The FROG code allows the user to define the geometry of a neutron converter device shaped as a hollow cylinder, where a neutron breeding material such as lithium deuteride (LiD) is cladded by two concentric cylinders. Such neutron converter is then placed inside a regular nuclear fission reactor, where thermal neutrons will react with the neutron breeder material (typically, Lithium 6), and through a series of reactions, will generate high-energy neutrons – neutrons whose kinetic energy are around 14 MeV. The hollowed central portion can hold a specimen that will be bombarded by high-energy neutrons created inside the neutron breeding material. Figuratively speaking, this type of device transforms neutrons from thermal (~0.625 eV) to fusion (~14 MeV) energies and is sometimes termed “fusion-to-thermal neutron converters” in the literature. The code consists of C++ source file compiled and linked to generate an executable. The user can select the dimensions of the converter (radius, length, and thickness of the breeder material), the breeder material type, the cladding material, and the specimen material that will be activated or irradiated. As input, the neutron flux for a specific location inside a reactor, for instance, positions in ATR, is required. As output, the code predicts the number of high-energy neutrons produced, the total neutron flux and fluence as well as its detailed spectrum. The physics involved in such device is very complex, as it requires modeling neutron transport, light-ion (tritons) transport, as well as fusion reactions. The Geant4 toolkit provides the required physical models.

Martin, NicholasP. [Idaho National Laboratory (INL↗

DOE OSTI2022

Model Code and Data for "Assessing the Efficiency Implications of Renewable Fuel Policy Design in the United States"

This dataset contains model code (including input data) to replicate the outcomes for "Assessing the Efficiency Implications of Renewable Fuel Policy Design in the United States". The model consists of: (1) The replication codes and data for the model. To run the model, using GAMS to run the "Models.gms" file.

cellulosic waiver credit↗

DOE OSTI2023

IM3 GO WEST Parameter Search Dataset

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It is developed to address weather and water dynamics, and associated vulnerabilities in this bulk power system. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. Therefore, GO WEST can help researchers to strike a balance between model fidelity (i.e. accuracy) and computational complexity (i.e. runtime). This dataset includes model inputs and outputs from 600 model versions for each 2019, 2020, and 2021. The folder naming convention is as follows: Exp{Number of Nodes}_{Mathematical Formulation}_{Transmission Line Limit Scaling Factor in MW}_{Hurdle Rate Scaling Factor in %}_{Year}. Linear programming is designated with "simple" label whereas mixed-integer linear programming is designated with "coal" label. For example, "Exp100_simple_1000_50_2019" folder contains inputs and outputs from 2019 model version with 100 nodes, linear programming, +1000 MW transmission line limit scaling factor, and +50% hurdle rate scaling factor. GO WEST GitHub repository hosts all raw datasets, processing scripts, and model scripts. Please refer to the README file for a detailed description of the included files.

Economics↗

NASA NTRS1973

Small Interactive Image Processing System (SMIPS) system description

The Small Interactive Image Processing System (SMIPS) operates under control of the IBM-OS/MVT operating system and uses an IBM-2250 model 1 display unit as interactive graphic device. The input language in the form of character strings or attentions from keys and light pen is interpreted and causes processing of built-in image processing functions as well as execution of a variable number of application programs kept on a private disk file. A description of design considerations is given and characteristics, structure and logic flow of SMIPS are summarized. Data management and graphic programming techniques used for the interactive manipulation and display of digital pictures are also discussed.

Moik, J. G.↗