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Excel files containing the data points in the figures or the data to obtain those points. Included are data for Fig1 (nuclear intensity fit) and Fig 5b (magnetic intensity fit). Included are 2D maps and 1D cuts from the 3D data obtained on WAND² for 5K and 10K.
Understanding soil texture and organic matter content supports our understanding of hydrology and ecology of Arctic sites. Soil organic matter content and composition of sand, silt, and clay were measured from soils collected at the Teller 27 field site on the Seward Peninsula and at the Barrow Environmental Observatory (BEO) near Utqiaġvik, Alaska, on August 2nd and 6th 2024, respectively. Soil samples were collected from the active layer to varied depths. Precise location data were collected at each observation point using Avenza Maps on a mobile device. Sand, silt, clay, and organic matter percentages were measured at Desert Research Institute Soil Characterization and Quaternary Pedology Laboratory in Reno, NV. This dataset contains a *.csv file of soil properties, a *.kml file of measurement locations, a *.pdf user guide, a *.csv data dictionary, and a *.csv file level metadata.
The Macho Project has undertaken a 5 year effort to search for dark matter in the halo of the Galaxy by scanning the Magellanic Clouds for micro-lensing events. Each evening's raw image data will be reduced in real-time into the observed stars' photometric measurements. The actual search for micro-lensing events will be a post-processing operation. The theoretical prediction of the rate of such events necessitates the collection of a large number of repeated exposures. The project designed camera subsystem delivers 64 Mbytes per exposure with exposures typically occurring every 500 seconds. An ideal evening's observing will provide 6 Gbytes of raw image data and 40 Mbytes of reduced photometric measurements. Recognizing the difficulty of digging out from a snowballing cascade of raw data, the project requires the real-time reduction of each evening's data. The software team's implementation strategy centered on this non-negotiable mandate. Accepting the reality that 2 full time people needed to implement the core real-time control and data management system within 6 months, off-the-shelf vendor components were explored to provide quick solutions to the classic needs for file management, data management, and process control. Where vendor solutions were lacking, state-of-the-art models were used for hand tailored subsystems. In particular, petri nets manage process control, memory mapped bulletin boards provide interprocess communication between the multi-tasked processes, and C++ class libraries provide memory mapped, disk resident databases. The differences between the implementation strategy and the final implementation reality are presented. The necessity of validating vendor product claims are explored. Both the successful and hindsight decisions enabling the collection and processing of the nightly data barrage are reviewed.
The Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC) is responsible for the distribution of the Level 1 data, and the higher levels of all Ocean and Atmosphere products (Land products are distributed through the Land Processes (LP) DAAC DAAC, and the Snow and Ice products are distributed though the National Snow and Ice Data Center (NSIDC) DAAC). Ocean products include sea surface temperature (SST), concentrations of chlorophyll, pigment and coccolithophores, fluorescence, absorptions, and primary productivity. Atmosphere products include aerosols, atmospheric water vapor, clouds and cloud masks, and atmospheric profiles from 20 layers. While most MODIS data products are archived in the Hierarchical Data Format-Earth Observing System (HDF-EOS 2.7) format, the ocean binned products and primary productivity products (Level 4) are in the native HDF4 format. MODIS Level 1 and 2 data are of the Swath type and are packaged in files representing five minutes of Files for Level 3 and 4 are global products at daily, weekly, monthly or yearly resolutions. Apart from the ocean binned and Level 4 products, these are in Grid type, and the maps are in the Cylindrical Equidistant projection with rectangular grid. Terra viewing (scenes of approximately 2000 by 2330 km). MODIS data have several levels of maturity. Most products are released with a provisional level of maturity and only announced as validated after rigorous testing by the MODIS Science Teams. MODIS/Terra Level 1, and all MODIS/Terra 11 micron SST products are announced as validated. At the time of this publication, the MODIS Data Support Team (MDST) is working with the Ocean Science Team toward announcing the validated status of the remainder of MODIS/Terra Ocean products. MODIS/Aqua Level 1 and cloud mask products are released with provisional maturity.
The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.
Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.
This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.
Digital land cover files derived from computer processing of LANDSAT and soil productivity data were linked and used by linear programming model to determine production of forested areas under different management strategies. Results of model include maps and data graphics for four-county region in Western Massachusetts.
UIMAGE is a data analysis package written in IDL for the Cosmic Background Explorer (COBE) project. COBE has extraordinarily stringent accuracy requirements: 1 percent mid-infrared absolute photometry, 0.01 percent submillimeter absolute spectrometry, and 0.0001 percent submillimeter relative photometry. Thus, many of the transformations and image enhancements common to analysis of large data sets must be done with special care. UIMAGE is unusual in this sense in that it performs as many of its operations as possible on the data in its native format and projection, which in the case of COBE is the quadrilateralized sphereical cube ('skycube'). That is, after reprojecting the data, e.g., onto an Aitoff map, the user who performs an operation such as taking a crosscut or extracting data from a pixel is transparently acting upon the skycube data from which the projection was made, thereby preserving the accuracy of the result. Current plans call for formatting external data bases such as CO maps into the skycube format with a high-accuracy transformation, thereby allowing Guest Investigators to use UIMAGE for direct comparison of the COBE maps with those at other wavelengths from other instruments. It is completely menu-driven so that its use requires no knowledge of IDL. Its functionality includes I/O from the COBE archives, FITS files, and IDL save sets as well as standard analysis operations such as smoothing, reprojection, zooming, statistics of areas, spectral analysis, etc. One of UIMAGE's more advanced and attractive features is its terminal independence. Most of the operations (e.g., menu-item selection or pixel selection) that are driven by the mouse on an X-windows terminal are also available using arrow keys and keyboard entry (e.g., pixel coordinates) on VT200 and Tektronix-class terminals. Even limited grey scales of images are available this way. Obviously, image processing is very limited on this type of terminal, but it is nonetheless surprising how much analysis can be done on that medium. Such flexibility has the virtue of expanding the user community to those who must work remotely on non-image terminals, e.g., via modem.
SynTrack is a dynamic, workflow-driven data management system that tracks the DNA build process: Management of the hierarchical relationships of the DNA fragments; Monitoring of process tasks for the assembly of multiple DNA fragments into final constructs; Creations of vendor order forms with selectable building blocks. Organizing plate layouts barcodes for vendor/pcr/fusion/chewback/bioassay/glycerol/master plate maps (default/condensed); Creating or updating Pre-Assembly/Assembly process workflows with selected building blocks; Generating Echo pooling instructions based on plate maps; Tracking of building block orders, received and final assembled for delivering; Bulk updating of colony or PCR amplification information, fusion PCR and chewback results; Updating with QA/QC outcome with .csv & .xlsx template files; Re-work assembly workflow enabled before and after sequencing validation; and Tracking of plate/well data changes and status updates and reporting of master plate status with QC outcomes.
Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.
This submission contains information used to compute the risk factors for the GPFA-AB project. The risk factors are natural reservoir quality, thermal resource quality, potential for induced seismicity, and utilization. The methods used to combine the risk factors included taking the product, sum, and minimum of the four risk factors. The files are divided into images, rasters, shapefiles, and supporting information. The image files show what the raster and shapefiles should look like. The raster files contain the input risk factors, calculation of the scaled risk factors, and calculation of the combined risk factors. The shapefiles include definition of the fairways, definition of the US Census Places, the center of the raster cells, and locations of industries. Supporting information contains details of the calculations or processing used in generating the files. An image of the raster will have the same name except *.png as the file ending instead of *.tif. Images with 'fairways' or 'industries' added to the name are composed of a raster with the relevant shapefile added. The file About_GPFA-AB_Phase1RiskAnalysisTask5DataUpload.pdf contains information the citation, special use considerations, authorship, etc. ***See 'GPFA-AB.zip' at bottom for compressed and organized version of the files associated with this submission*** **More details (including location) on each file are given in the spreadsheet 'list_of_contents.csv' in the folder 'SupportingInfo'** Code used to calculate values is available: https://github.com/calvinwhealton/geothermal_pfa under the folder 'combining_metrics' - *See link below*
Confident identification of unknown chemicals in high resolution mass spectrometry (HRMS) screening studies requires cohesive workflows and complementary data, tools, and software. Chemistry databases, screening libraries, and chemical metadata have become fixtures in identification workflows. To increase confidence in compound identifications, the use of structural fragmentation data collected via tandem mass spectrometry (MS/MS or MS2) is vital. However, the availability of empirically collected MS/MS data for identification of unknowns is limited. Researchers have therefore turned to in silico generation of MS/MS data for use in HRMS-based screening studies. This paper describes the generation en masse of predicted MS/MS spectra for the entirety of the US EPA’s DSSTox database using competitive fragmentation modelling and a freely available open source tool, CFM-ID. The generated dataset comprises predicted MS/MS spectra for ~700,000 structures, and mappings between predicted spectra, structures, associated substances, and chemical metadata. Together, these resources facilitate improved compound identifications in HRMS screening studies. These data are accessible via an SQL database, a comma-separated export file (.csv), and EPA’s CompTox Chemicals Dashboard.
I present tabulations of the diffuse observations made by the GALEX spacecraft in two UV bands (FUV: 1539 Å and NUV: 2316 Å) from the (almost) final data release of the GALEX spacecraft (GR6/GR7). This data release includes all the FUV observations and the majority of the NUV observations. I discuss overall trends in the data but the primary purpose of this paper is to make the data available to the public. The data files described in this paper are hosted by the Mikulski Archive for Space Telescopes at the Space Telescope Science Insitute from whence they may be downloaded. For ease of use, I have also created maps of the diffuse radiation in both bands over the entire observed sky at 6' resolution.
The ESS-DIVE sample identifiers and metadata reporting format primarily follows the System for Earth Sample Registration (SESAR) Global Sample Number (IGSN) guide and template, with modifications to address Environmental Systems Science (ESS) sample needs and practicalities (IGSN-ESS). IGSNs are associated with standardized metadata to characterize a variety of different sample types (e.g. object type, material) and describe sample collection details (e.g. latitude, longitude, environmental context, date, collection method). Globally unique sample identifiers, particularly IGSNs, facilitate sample discovery, tracking, and reuse; they are especially useful when sample data is shared with collaborators, sent to different laboratories or user facilities for analyses, or distributed in different data files, datasets, and/or publications. To develop recommendations for multidisciplinary ecosystem and environmental sciences, we first conducted research on related sample standards and templates. We provide a comparison of existing sample reporting conventions, which includes mapping metadata elements across existing standards and Environment Ontology (ENVO) terms for sample object types and environmental materials. We worked with eight U.S. Department of Energy (DOE) funded projects, including those from Terrestrial Ecosystem Science and Subsurface Biogeochemical Research Scientific Focus Areas. Project scientists tested the process of registering samples for IGSNs and associated metadata in workflows for multidisciplinary ecosystem sciences. We provide modified IGSN metadata guidelines to account for needs of a variety of related biological and environmental samples. While generally following the IGSN core descriptive metadata schema, we provide recommendations for extending sample type terms, and connecting to related templates geared towards biodiversity (Darwin Core) and genomic (Minimum Information about any Sequence, MIxS) samples and specimens. ESS-DIVE recommends registering samples for IGSNs through SESAR, and we include instructions for registration using the IGSN-ESS guidelines. Our resulting sample reporting guidelines, template (IGSN-ESS), and identifier approach can be used by any researcher with sample data for ecosystem sciences.
The D Machine is described as a small user microprogrammable computer designed to be a versatile building block for such diverse functions as: disk file controllers, I/O controllers, and emulators. TRANSLANG is an ALGOL-like language, which allows D Machine users to write microprograms in an English-like format as opposed to creating binary bit pattern maps. The TRANSLANG translator parses TRANSLANG programs into D Machine microinstruction bit patterns which can be executed on the D Machine simulator. In addition to simulation and translation, the two programs also offer several debugging tools, such as: a full set of diagnostic error messages, register dumps, simulated memory dumps, traces on instructions and groups of instructions, and breakpoints.
PIXTOOLS is software for Silicon Graphics IRIS consisting of thirteen programs plus library for operating on bit-map images. Enables user to create, edit, and save high-resolution images in forms in which displayed on video screens, resize them, and capture them. Eleven programs print information and read and write files. Two offer graphical interfaces. Menus enable manipulation of images and background color and saving of an image screen to file. Written in C.
A computerized method which utilizes the engine performance data and estimates the installed performance of aircraft gas turbine engines is presented. This installation includes: engine weight and dimensions, inlet and nozzle internal performance and drag, inlet and nacelle weight, and nacelle drag. The use of two data base files to represent the engine and the inlet/nozzle/aftbody performance characteristics is discussed. The existing library of performance characteristics for inlets and nozzle/aftbodies and an example of the 1000 series of engine data tables is presented.