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Fig. 2 in Potential pest bark and ambrosia beetles from Cuba not present in the continental United States
Fig. 2. Dorsal and lateral view of female Euwallacea posticus. Scale bar: 1 mm.
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
FIGURE 8 in Amateur paleontological societies and fossil clubs, interactions with professional paleontologists, and social paleontology in the United States
FIGURE 8. Map of amateur paleontological organizations, Facebook likes, and Twitter followers.
FIGURE 2 in Pteropoda (Mollusca, Gastropoda, Thecosomata) from the Paleocene-Eocene Thermal Maximum (United States Atlantic Coastal Plain)
FIGURE 2. Specimen storage in the Naturalis (Leiden, NL) fossil holoplanktic mollusk collection.
Does Location Influence Code Quality? Mining Stack Overflow Snippets Across the United States – Replication Package
<p>Developers routinely integrate Stack Overflow code snippets into their codebases. However, the quality of snippets embedded in users’ answers remain elusive, and existing evaluations of code quality tend to be language or context-specific. Moreover, literature have found that contribution patterns vary depending on geographical locales, creating an unexplained rift between code quality, user location, and latent contextual regional factors. </p> <p>The proposed study evaluates the quality of SQL, JavaScript, Python, Ruby, and Java snippets across reliability, readability, performance, and security dimensions, benchmarking findings across states in the USA and investigating how different diversity indicators correlate against code quality violations. The study culminates in a series of inductive content analyses that qualitatively supplement prior quality dimensions.</p> <p>This replication package is provided for those interested in further examining our research methodology.</p>
Table S1: Records of bramble shark sightings on 16 December, 2023, offshore of Fujairah, UAE, in the Gulf of Oman. Time is provided in Zulu time (GMT), not in local time. Vessels where sightings occurred are labeled as remotely operated vessels (ROV: Chimera) or human-driven submersibles (Sub: Neptune and Nadir).; Video S1: Video footage of a bramble shark (Echinorhinus brucus) encounter with a submersible at 780 m depth offshore from Fujairah, United Arab Emirates.
<p><span>Table S1: Records of bramble shark sightings on 16 December, 2023, offshore of Fujairah, UAE, in the Gulf of Oman. Time is provided in Zulu time (GMT), not in local time. Vessels where sightings occurred are labeled as remotely operated vessels (ROV: Chimera) or human-driven submersibles (Sub: Neptune and Nadir).</span></p> <p><span>Video S1: Video footage of a bramble shark (<em>Echinorhinus brucus</em>) encounter with a submersible at 780 m depth offshore from Fujairah, United Arab Emirates.</span></p>
Data on digitization, unit labour costs and capital stock based productivity indicators
<p>Dataset was collected from Eurostat involving 9 indicators included in 2 panel data regression models</p>
Slope Units delineation for Marche region in Italy
<p>To use this dataset, please cite the article; <em>Ahmed, M., Titti, G., Trevisani, S., Borgatti, L., Francioni, M.: Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction. Submitted to Geoscientific Model Development. 2024. <br></em></p> <p> </p> <p>Using three metrics to assess the segmentation of the terrain into Slope Units (SUs), we choose the partition which has been calculated with the following parameters in r.slopunits software (Alvioli et. al., 2016): flow accumulation threshold of 1,000,000 square meters, clean size of 20,000 square meters with the cleaning method (flag -m), minimum area of 300,000 square meters with circular variance of 0.1. The metrics incorporated the landslide extension, landslide number and the aspect homogenity of each segmentation. This has been done using TINITALY resampled to 30m for the Marche region of Italy.</p> <p> </p> <p> </p>
Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"
<p>Data and codes for the manuscript entitled:</p> <p>"<strong>The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"</strong></p> <p> </p> <p><strong>Please see Readme.txt for details</strong></p>
Bankfull and Mean-flow Channel Geometry Dataset across the CONtiguous United States (CONUS)
<p>This dataset includes estimated river channel geometry attributes, specifically width and depth, under bankfull and mean-flow conditions across the CONtiguous United States (CONUS). The method utilized for providing these estimations are based on eXtreme Gradient Boosting Regression (XGBR). This dataset can be linked to the National Hydrograohy Dataset Plus (NHDPlusV2.1) through Common identifier of the NHD feature, known as COMID.</p>
Essential Urban Land Cover Category across the Conterminous United States
<p>This dataset, titled "Essential Urban Land Cover Category across the Conterminous United States", contains high-resolution urban land cover maps for urban areas across the Conterminous United States produced in the manuscript "Large-scale High-resolution Essential Urban Land Cover Category (EULCC) Mapping Using a Semantic-Augmented and Noise-Tolerant Approach". The land cover maps are organized by city, with each city’s data stored as a separate <span>TIFF </span>file. These city-level files are grouped by state, and the dataset provided here is the compressed files of the state-level folders.</p> <p>The raster data values range from 1 to 9 in the land cover maps, representing different essential urban land cover categories as follows:</p> <table> <tbody> <tr> <td>Values</td> <td>Classes</td> </tr> <tr> <td>1</td> <td>Building</td> </tr> <tr> <td>2</td> <td>Tree</td> </tr> <tr> <td>3</td> <td>Grass/Shrub</td> </tr> <tr> <td>4</td> <td>Parking lot</td> </tr> <tr> <td>5</td> <td>Road</td> </tr> <tr> <td>6</td> <td>Water</td> </tr> <tr> <td>7</td> <td>Barren</td> </tr> <tr> <td>8</td> <td>Agriculture</td> </tr> <tr> <td>9</td> <td>Others</td> </tr> </tbody> </table> <p> </p>
Complex Patterns and Determinants of Regional Multiple Chronic Conditions Across the United States
Open the record for dataset details and reuse information.
Minimal dataset for study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020
<p>These datasets are the minimal datasets for the study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020.</p> <p>Study DOI: https://doi.org/10.21203/rs.3.rs-4752336/v1</p>
Data from: The spatial structure of phylogenetic and functional diversity in the United States and Canada: an example using the sedge family (Cyperaceae)
Systematically quantifying diversity across landscapes is necessary to understand how clade history and ecological heterogeneity contribute to the origin, distribution, and maintenance of biodiversity. Here, we chart the spatial structure of diversity among all species in the sedge family (Cyperaceae) throughout the USA and Canada. We first identify areas of remarkable species richness, phylogenetic diversity, and functional trait diversity, and highlight regions of conservation priority. We then test predictions about the spatial structure of this diversity based on the historical biogeography of the family. Incorporating a phylogeny, over 400,000 herbarium records, and a database of functional traits mined from online floras, we find that species richness and functional trait diversity peak in the Northeastern USA, while phylogenetic diversity peaks along the Gulf of Mexico. Floristic turnover among assemblages increases significantly with distance, but phylogenetic turnover is twice as rapid along latitudinal gradients as along longitudinal gradients. These patterns reflect the expected distribution of Cyperaceae, which originated in the tropics but radiated in temperate regions. We identify assemblages with an abundance of rare, range-restricted lineages, and assemblages composed of species generally lacking from diverse regions. We argue that both of these metrics are useful for developing targeted conservation strategies. We use the data generated here to establish future research priorities, including the testing of a series of hypotheses regarding the distribution of chromosome numbers, photosynthetic pathways, and resource partitioning in sedges.
Data from: Centennial-scale reductions in nitrogen availability in temperate forests of the United States
Forests cover 30% of the terrestrial Earth surface and are a major component of the global carbon (C) cycle. Humans have doubled the amount of global reactive nitrogen (N), increasing deposition of N onto forests worldwide. However, other global changes—especially climate change and elevated atmospheric carbon dioxide concentrations—are increasing demand for N, the element limiting primary productivity in temperate forests, which could be reducing N availability. To determine the long-term, integrated effects of global changes on forest N cycling, we measured stable N isotopes in wood, a proxy for N supply relative to demand, on large spatial and temporal scales across the continental U.S.A. Here, we show that forest N availability has generally declined across much of the U.S. since at least 1850 C.E. with cool, wet forests demonstrating the greatest declines. Across sites, recent trajectories of N availability were independent of recent atmospheric N deposition rates, implying a minor role for modern N deposition on the trajectory of N status of North American forests. Our results demonstrate that current trends of global changes are likely to be consistent with forest oligotrophication into the foreseeable future, further constraining forest C fixation and potentially storage.
Figure 1 in New adult host records for three Buprestidae (Coleoptera) rarely encountered in the United States and significant extension of the known geographic range of Agrilus pilosicollis Fisher
Figure 1. Agrilus langei Obenberger ♂. a) Dorsal view. b) Ventral view. c) Lat-
Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States
<p>This dataset contains information of 432 study catchments used in the WRR paper "Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States", including shapefile, hydrological signatures, catchment descriptors, and catchment classification (the primary class of each catchment).</p>
Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data</p>
AI results complementing the 2020 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - The United Kingdom
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2020, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund</li> </ul> <p>Disclaimer: Data collected by the UK at national level prior to 31/01/2020 has been used in EFSA data reports as is related to a period in which this country was still a European Union Member State.</p>
Weak spatial-genetic structure in a native invasive, the southern pine beetle (Dendroctonus frontalis), across the eastern United States
<p>The southern pine beetle<i>, Dendroctonus frontalis</i>, is a native pest of pine trees that has recently expanded its range into the northeastern United States. Understanding its colonization, dispersal, and connectivity will be critical for mitigating negative economic and ecological impacts in the newly invaded areas. Characterization of spatial-genetic structure can contribute to this; however, previous studies have reached different conclusions about regional population genetic structure, with one study reporting a weak east-west pattern, and the most recent reporting absence of structure. Here we systematically assessed several explanations for the absence of spatial-genetic structure. To do this, we developed nine new microsatellite markers and combined them with an existing 24-locus data matrix for the same individuals. We then reanalyzed this full dataset alongside datasets in which certain loci were omitted with the goal of creating more favorable signal to noise ratios. We also partitioned the data based on the sex of <i>D. frontalis</i> individuals, and then employed a broad suite of genotypic clustering and isolation-by-distance (IBD) analyses. We found that neither inadequate information content in the molecular marker set, nor unfavorable signal-to-noise ratio, nor insensitivity of the analytical approaches could explain the absence of structure. Regardless of dataset composition, there was little evidence for clusters (i.e., distinct geo-genetic groups) or clines (i.e., gradients of increasing allele frequency differences over larger geographic distances), with one exception: significant IBD was repeatedly detected using an individual-based measure of relatedness whenever datasets included males (but not for female-only datasets). This is strongly indicative of broad-scale female-biased dispersal, which has not previously been reported for <i>D. frontalis</i>, in part owing to logistical limitations of direct approaches (e.g., capture-mark-recapture). Weak spatial-genetic structure suggests long-distance connectivity and that gene flow is high, but additional research is needed to understand range expansion dynamics in this species using alternate approaches.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.