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1,255 results for “High-resolution”
Genotyping of European Toxoplasma gondii strains by a new high-resolution next-generation sequencing-based method
<p>The data set comprises 164 FASTQ files generated with an Ion AmpliSeq-based genotyping method for <em>Toxoplasma gondii </em>and<em> </em>a BED file used for the design of the Ion AmpliSeq primer panel. The FASTA file named as "AmpliSeq-ME49-Reference" was used as a reference for mapping and data analysis of the FASTQ files. The GZ file named as "Tgondii_IonAmpliSeq_Results_SNPs_VCF" is a VCF file, which contains all SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference. The VCF file was converted into a FASTA file named as "Tgondii_IonAmpliSeq_Results_SNPs", which also contains the SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference.</p> <p>The work is published in the European Journal of Clinical Microbiology & Infectious Diseases with the title "Genotyping of European <em>Toxoplasma gondii</em> strains by a new high‑resolution next‑generation sequencing‑based method"; https://doi.org/10.1007/s10096-023-04721-7</p>
High-resolution T2M and RH simulated using WRF-ARW over Cyprus for 2015
<p>Meteorological data generated over Cyprus for the year 2015 using the open-source, community-based, state-of-the-art Weather Research and Forecasting (WRF-ARW) Model. The model was configured according to the operational numerical weather forecasts of the Cyprus Department of Meteorology (DoM). The meteorological fields, generated with a nested configuration setup, are at an ultra-fine spatiotemporal resolution (<em>i.e.,</em> 2 km horizontal grid spacing and 1-hour temporal frequency).</p>
High-resolution spatiotemporal modelling of sand fly abundance in Cyprus in 2015
<p>The expected population size of <em>P. papatasi</em> in Cyprus in 2015 was simulated using the stochastic climate-driven population dynamics model of the species presented in Erguler <em>et al.</em> (2019). The model was simulated with air temperature and relative humidity obtained from WRF-ARW. Two sets of parameters, one for Steni and one for Geri - each with 1000 alternative configurations - were used to simulate the average number of adult females per day per trap (a proxy to expected population size).</p>
High-resolution maps of rubber and rubber-related deforestation for Southeast Asia
<p>This dataset contains maps of rubber plantations in 2021, and maps of rubber-related deforestation between 1993-2016 for Southeast Asia. The rubber maps have a 10 m pixel size, and the deforestation maps have a 30 m pixel size. The dataset and the methods for generating it are described in Wang et al. 2023. High-resolution maps show that rubber causes substantial deforestation. <em>Nature</em>. <strong>Please note that an update of this dataset will follow in September 2025.</strong> </p>
SBC LTER: Ocean: High-resolution Landsat 8 chlorophyll imagery of the Santa Barbara Channel
This is a timeseries of Landsat 8 images of the Santa Barbara Channel, processed for both chlorophyll and particulate backscattering. These data are at a particularly high spatial resolution (30 m), making them useful for analysis of submesoscale biophysical and biogeochemical interactions in the SBC, and for comparison with high-resolution modeling results. Data are contained in netcdf files, and there are 88 images in total, spanning the deployment of Landsat 8 (2013 - present). MATLAB scripts are available to load the netcdf files, flag the bad pixels, interpolate over the flagged pixels, and plot images, in the matlab folder. In addition, Landsat8_chl_imagery.zip contains jpegs of all of the images. A recommended workflow to users would be looking through all the jpegs to decide what images you want, and then downloading those specific netcdf files and using the matlab scripts and accompanying functions to process them. Our hope is that concurrent study of submesoscale variability in phytoplankton biomass via both submesoscale-resolving model studies and high-resolution satellite imagery may reveal further insights into the importance of submesoscale biophysical variability to regional and global biogeochemical processes.
A multiple model high-resolution head-related impulse response database for aided and unaided ears (SOFA format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in SOFA format, with the data identical to the HDF5 files in <a href="https://zenodo.org/record/1226873">doi:10.5281/zenodo.1226873</a>.</p>
Variability of OB stars from TESS southern Sectors 1-13 and high-resolution IACOB and OWN spectroscopy
<p>Typical MESA and GYRE inlists associated with <a href="https://arxiv.org/abs/2005.09658">Burssens et al. 2020</a>. MESA v. 12155, GYRE version v. 5.2.</p> <p><em>Context:</em> Lack of high-precision long-term continuous photometric data for large samples of stars has prevented the large-scale exploration of pulsational variability in the OB star regime. As a result, the candidates for in-depth asteroseismic modelling remained limited to a few tens of dwarfs. The TESS nominal space mission has surveyed the southern sky, including parts of the galactic plane, yielding continuous data of at least 27 d for hundreds of OB stars.<br> <em>Aims:</em> We aim to couple TESS data in the southern sky with ground-based spectroscopy to study the variability in two dimensions, mass and evolution. We focus mainly on the presence of coherent pulsation modes that may or may not be present in the predicted theoretical instability domains and unravel all frequency behaviour in the amplitude spectra of the TESS data.<br> <em>Methods: </em>We compose a sample of 98 OB-type stars observed by TESS in Sectors 1-13 and with available multi-epoch, high-resolution spectroscopy gathered by the IACOB and OWN surveys. We present the short-cadence 2-min light curves of dozens of OB-type stars, that have one or more spectra in the IACOB or OWN database. Based on these light curves and their Lomb-Scargle periodograms we perform variability classification and frequency analysis. We place the stars in the spectroscopic Hertzsprung-Russell diagram to interpret the variability in an evolutionary context.<br> <em>Results:</em> We deduce diverse origins of the mmag-level variability found in all of the 98 OB stars in the TESS data. We find among the sample several new variable stars, including three hybrid pulsators, three eclipsing binaries, high frequency modes in a Be star, and potential heat-driven pulsations in two Oe stars. <br> <em>Conclusions:</em> We identify stars for which future asteroseismic modelling is possible, provided mode identification is achieved. By comparing the position of the variables to theoretical instability strips we discuss the current shortcomings in non-adiabatic pulsation theory, and the distribution of pulsators in the upper Hertzsprung-Russell diagram.</p> <p> </p> <p> </p>
Raw data for "Multi-slice ptychography enables high-resolution in situ measurements in extended chemical reactors"
<p>Raw data used in "Multi-slice ptychography enables high-resolution in situ measurements in extended chemical reactors" by M. Kahnt, L. Grote, D. Brückner, M. Seyrich, F. Wittwer, D. Koziej and C.G. Schroer.</p>
Reproduction packages for the paper "Spectral and Imaging properties of Sgr A∗ from High-Resolution 3 DGRMHD Simulations with Radiative Cooling"
<p>This is a basic reproduction package for the paper"Spectral and Imaging properties of Sgr A∗ from High-Resolution 3D GRMHD Simulations with Radiative Cooling" by Yoon et al. (2020). It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
EstSoil-EH: A high-resolution eco-hydrological modelling parameters dataset for Estonia (dataset)
<p>For the EstSoil-EH dataset, we synthesized more than 20 extended eco-hydrological variables for Estonia as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5m Lidar DEM, Estonian Topographic Database and EU-HydroSoilGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75% of mapped units smaller than 4.0 ha, based on Soviet era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only of the actual texture class, but also of classifiers for rock content, peat soils, distinct compositional layers and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil profiles (e.g., layers, depths), texture (clay, silt, sand components), coarse fragments and rock content. In addition, we aggregated and predicted physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content, available water capacity).<br> The developed methodology and dataset will be an important resource for the Baltic region, but possibly also all other regions where detailed field-based soil mapping data is available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia.</p> <p> </p> <p>We created an extended eco-hydrological dataset for Estonia, the EstSoil-EH, containing derived numerical values for the following data in all of the mapped soil units in the 1:10 000 soil map: soil profiles (e.g., layers, depths), texture (clay, silt, and sand components), rockiness, and physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content). Ultimately, our objective was to develop a reproducible method for deriving numerical values to support modelling and prediction of eco-hydrological processes in Estonia using the popular Soil and Water Assessment Tool.</p> <p>For more information on the development of this dataset look for "EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia", Alexander Kmoch, Arno Kanal†, Alar Astover, Ain Kull, Holger Virro, Aveliina Helm, Meelis Pärtel, Ivika Ostonen and Evelyn Uuemaa, 2021, Earth Syst. Sci. Data, 13, 83–97, <a href="https://doi.org/10.5194/essd-13-83-2021">https://doi.org/10.5194/essd-13-83-2021</a> </p>
Reply to comment on "High-resolution, multi-layer modelling of Singapore's urban climate incorporating local climate zones"
<p>This data is for the publication submitted to the Journal of Geophysical Research Atmospheres</p>
High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)
<p>The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high-resolution radar-based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias-correct and downscale a state-of-the-art atmospheric reanalysis (<a href="https://doi.org/10.24381/cds.adbb2d47">ERA5</a>) rainfall dataset, using the radar-based <a href="https://doi.org/10.5065/D6PG1QDD">Stage IV</a> precipitation product as fine resolution reference, to develop an hourly CONUS-wide precipitation product over a 4-km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two-component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using <a href="https://doi.org/10.25921/p7j8-2170">NOAA's raingauge</a> measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow-prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial-resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling.</p>
High-resolution neutron spectroscopy of La1.855Sr0.145CuO4
<p>Data from a high-resolution neutron spectroscopy investigation of the low-energy incommensurate spin excitations in the high-temperature superconductor La1.855Sr0.145CuO4. The study was conducted using the IN5 time-of-flight neutron spectrometer at the Institut Laue-Langevin. The sample consisted of two crystals with a combined mass of 3.5 g and a superconducting transition temperature of 36 K. These crystals were prepared from a single rod grown via the traveling-solvent floating-zone method and aligned within a deviation of less than a degree. The lattice parameters are a = b = 3.81 Å and c = 13.2 Å, in the notation of the high-temperature tetragonal unit cell. Measurements were performed on the sample in a standard cryostat. Magnetic excitation spectra were acquired with a fixed sample orientation (b-axis perpendicular to the horizontal scattering plane) over 12 or 24 hours at various temperatures. Two distinct experimental configurations were utilized, employing incident wavelengths λ_i = 3.3 Å and 5 Å. The shorter wavelength was used to validate the magnitude of the superconducting spin gap (4 meV) at a base temperature of T = 2 K. The λ = 5 Å setup was employed to gather spectra at temperatures of T = 40, 30, 25, and 20 K, with an elastic full-width at half maximum resolution of ΔE = 80 μeV.</p>
High-resolution analysis of power plant land requirements for GODEEEP
<p>This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below. </p> <p>For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential"</p> <p>Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a></p> <h2>Scenario Descriptions</h2> <ul> <li><strong>business-as-usual</strong>: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>high renewables</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> <p><strong>Description</strong></p> <p>These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas.</p> <p><strong>Scenario Files:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Difference Analysis Files:</strong></p> <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;"><strong>Column</strong></td> <td style="width: 82.845413%; height: 19px;"><strong>Description</strong></td> <td style="width: 4.029241%; height: 19px;"><strong>Units</strong></td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> <p>Description:</p> <p>This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3. US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> <p><strong>Description:</strong></p> <p>This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table> <p> </p> <h2>Funding statement</h2> <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> <p> </p> <h2>Changelog</h2> <p>v1.1</p> <p> The following updates were made following manuscript revision:</p> <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
StageIV-IRC – A High-resolution Dataset of Extreme Orographic Quantitative Precipitation Estimates (QPE) Constrained to Water Budget Closure for Historical Floods in the Appalachian Mountains
<h2>Quantitative Flood Estimation (QFE) in complex terrain remains a grand challenge in operational hydrology due to the lack of accurate high-resolution Quantitative Precipitation Estimates (QPE) at spatial and temporal resolutions needed to capture the variability of orographic precipitation, and where radar-based QPE are available there are significant biases due to the geometry and constraints of radar operations. Here, we present a high-resolution (i.e. 250m, 5minute-hourly) QPE dataset for the most extreme (flood-producing) events from 2008 to 2024 for 26 gauged basins (in total 215 events) in the Appalachian mountains constrained to meet basin-scale water budget closure through inverse rainfall-runoff modeling to correct the Next Generation Weather Radar (NEXRAD) Stage IV analysis (4km resolution, hourly) using a fully-distributed uncalibrated hydrological model that leverages recent advances in hydrologic modeling in mountainous regions (e.g. improved river routing and initial soil moisture estimation) (Liao and Barros, 2024a and 2024b). The corrected Stage IV analysis is referred to as StageIV-IRC (Inverse Rainfall Correction). Previously, a subset of this dataset informed the construction of a generalized QPE error model (Liao and Barros, 2023), supporting the development of water budget closure constrained QPE and providing physics insights into orographic QPE uncertainties for various radar-based products at high resolution in complex terrain. The unique advantage of the StageIV-IRC QPE is that it achieves water budget closure at the storm-flood event scale within observational uncertainty of streamflow observations, that is the golden standard in hydrological modeling. The QPE dataset is publicly available at: <a href="https://doi.org/10.5281/zenodo.14028867">https://doi.org/10.5281/zenodo.14028867</a></h2> <p><strong> </strong></p>
Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins
<p class="MsoNormal"><span>Age is necessary information for the study of life history of wild animals. A general method to estimate the age of odontocetes is counting dental growth layer groups (GLGs). However, this method is highly invasive as it requires the capture and handling of individuals to collect their teeth.</span><span> Recently, the development of DNA-based age </span><span>estimation methods has been actively studied as an alternative to such invasive methods, of which many have used biopsy samples. However, if DNA-based age estimation can be developed from fecal samples, age estimation can be performed without touching or disrupting individuals, thus establishing an entirely non-invasive method. </span><span>We developed an age estimation model using the methylation rate of two gene regions, <em>GRIA2</em> and <em>CDKN2A,</em> measured through methylation-sensitive high-resolution melting (MS-HRM) from fecal samples of wild Indo-Pacific bottlenose dolphins (<em>Tursiops aduncus</em>). The age of individuals was known through conducting longitudinal individual identification surveys underwater. Methylation rates were quantified from 36 samples. Both gene regions showed a significant correlation between age and methylation rate. The age estimation model was constructed based on the methylation rates of both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, <em>R<sup>2</sup></em> = 0.34) for the ecological studies of the Indo-Pacific bottlenose dolphins, with a lifespan of 40-50 years. This is the first study to report the use of non-invasive fecal samples to estimate the age of marine mammals.</span></p>
Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)
<p>Age is an important parameter for bettering the understanding of biodemographic trends-development, survival, reproduction and environmental effects-critical for conservation. However, current age estimation methods are challenging to apply to many species, and no standardised technique has been adopted yet. This study examined the potential use of methylation-sensitive high-resolution melting (MS-HRM), a labour, time, and cost-effective method to estimate chronological age from DNA methylation in Asian elephants (<em>Elephas maximus</em>). The objective of this study was to investigate the accuracy and validation of MS-HRM use for age determination in long-lived species, such as Asian elephants. The average lifespan of Asian elephants is between 50-70 years but some have been known to survive for more than 80 years. DNA was extracted from 53 blood samples of captive Asian elephants across 11 zoos in Japan, with known ages ranging from a few months to 65 years. Methylation rates of two candidate age-related epigenetic genes, <em>RALYL</em> and <em>TET2,</em> were significantly correlated with chronological age. Finally, we established a linear, unisex age estimation model with a mean absolute error (MAE) of 7.36 years. This exploratory study suggests an avenue to further explore MS-HRM as an alternative method to estimate the chronological age of Asian elephants.</p>
High-resolution figures of Braig et al. 2023
<p>High-resolution figures of Braig et al. 2023 "The diversity of larvae with multi-toothed stylets from about 100 million years ago illuminates the early diversification of antlion-like lacewings" in Diversity (MDPI)</p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </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.