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5,155 results for “resolution”
Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution
<p>This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS. </p>
Dataset of H-alpha activity indices of F-, G-, and K-type stars observed by the LAMOST Medium-Resolution Spectroscopic Survey
<p>This dataset of H-alpha activity indices is for investigation of stellar H-alpha chromospheric activity based on the LAMOST Medium-Resolution Spectroscopic Survey (MRS). High signal-to-noise ratio MRS coadded spectra of F-, G-, and K-type stars (4000-7000 K) from LAMOST Data Release 8 are utilized in the dataset (329,294 spectra in total). The H-alpha activity index (I_halpha) and the H-alpha R-index (R_halpha) are evaluated for the MRS spectra. The values of the H-alpha indices and their uncertainties, as well as the related spectroscopic parameters from the LAMOST catalogs, are stored in a CSV-format file (file name: Halpha_activity_indices_LAMOST_MRS_DR8.csv).</p> <p> </p> <p>Columns included in the dataset:</p> <p>obsid - unique observation identifier of LAMOST MRS</p> <p>ra - right ascension of the observed object (unit: degree)</p> <p>dec - declination of the observed object (unit: degree)</p> <p>obsdate - UTC date of the observation (format: YYYY-MM-DD)</p> <p>fitsname - MRS FITS data file name</p> <p>sn_B - signal-to-noise ratio of the blue band of MRS</p> <p>sn_R - signal-to-noise ratio of the red band of MRS</p> <p>band_para - indicating which band of MRS is used by the LASP to determine the stellar atmospheric parameters; ‘B’ representing the blue band and ‘R’ representing the red band</p> <p>teff - effective temperature determined by the LASP (unit: K)</p> <p>teff_err - uncertainty of teff (unit: K)</p> <p>logg - surface gravity determined by the LASP (unit: dex)</p> <p>logg_err - uncertainty of logg (unit: dex)</p> <p>feh - metallicity determined by the LASP (unit: dex)</p> <p>feh_err - uncertainty of feh (unit: dex)</p> <p>rv_r0 - radial velocity determined based on the red band data of MRS (unit: km/s)</p> <p>rv_r0_err - uncertainty of rv_r0 (unit: km/s)</p> <p>category* - ‘main-sequence’ or ‘giant’ or ‘intermediate-zone’</p> <p>I_halpha* - H-alpha activity index</p> <p>I_halpha_err* - uncertainty of I_halpha</p> <p>chi* - χ factor for calculating R_halpha from I_halpha (unit: 1/Å)</p> <p>chi_err* - uncertainty of chi (unit: 1/Å)</p> <p>R_halpha* - H-alpha R-index</p> <p>R_halpha_err* - uncertainty of R_halpha</p> <p>(Note: The columns marked with an asterisk are provided by this dataset. Other columns are from the catalogs of LAMOST DR8 v1.1. LASP is an acronym for the LAMOST Stellar Parameter Pipeline. A value of -9999.0 in the dataset indicates that it is not available.)</p> <p> </p> <p> </p>
High resolution LiDAR dataset acquired using UAV (unmanned aerial vehicle) over two vineyards and two years located in 'Tomiño', Pontevedra, Spain.
<p>This dataset features an extensive collection of LiDAR data from vineyards in northern Spain, targeting vineyards to address the growing demand for public UAV LiDAR datasets in Agricultural Sciences. The data was gathered using a DJI M300 multi-rotor platform equipped with a DJI Zenmuse L1 LiDAR sensor, conducting UAV flights at 20, 30, and 50 meters Above Ground Level (AGL) across two vineyards during 2021 and 2022. The dataset comprises ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. This information is essential for studying vineyard morphology and development and plays a key role in refining vineyard management tactics. In addition, the dataset is valuable for agricultural robotics, providing detailed terrain and canopy data crucial for designing efficient flight paths and navigation algorithms. Finally, it serves as a true "ground truth" dataset to verify satellite-derived models, enabling the generation of high-precision digital elevation models (DEMs) and other derivatives.</p>
High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components
<p>Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm<sup>2</sup> and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.</p>
Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source
<p>This repository contains the code and data underlying the publication "<em>Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source</em>" in Biomedical Optics Express 14, 3532-3554 (2023) (doi.org/10.1364/BOE.487345).</p> <p>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author. </p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes all the Matlab and Python scripts in this depository. Table 2 describes the datasets. The input datasets are the phase corrected datasets, as the raw data is large in size and phase correction using a coverslip as reference is rather straightforward. Processed datasets are also added to the repository to allow for running only a limited number of scripts, or to obtain for example the aberration corrected data without the need to use python. Note that the simulation input data (<em>input_simulations_pointscatters_SLDshape_98zf_noise75.mat</em>) is generated with random noise, so if this is overwritten de results may slightly vary. Also the aberration correction is done with random apertures, so the processed aberration corrected data (<em>exp_pointscat_image_MIAA_ISAM_CAO.mat</em> and <em>exp_leaf_image_MIAA_ISAM_CAO.mat</em>) will also slightly change if the aberration correction script is run anew. The current processed datasets are used as basis for the figures in the publication. For details on the implementation we refer to the publication.</p> <table> <caption>Table 1: The Matlab and Python scripts with their description</caption> <tbody> <tr> <td><strong>Script name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><em>MIAA_ISAM_processing.m</em></td> <td>This scripts performs the DFT, RFIAA and MIAA processing of the phase-corrected data that can be loaded from the datasets. Afterwards it also applies ISAM on the DFT and MIAA data and plots the results in a figure (via the scripts <em>plot_figure3, plot_figure5</em> and <em>plot_simulationdatafigure</em>).</td> </tr> <tr> <td><em>resolution_analysis_figure4.m</em></td> <td>This figure loads the data from the point scatterers (absolute amplitude data), seeks the point scatterrers and fits them to obtain the resolution data. Finally it plots figure 4 of the publication.</td> </tr> <tr> <td><em>fiaa_oct_c1.m, oct_iaa_c1.m, rec_fiaa_oct_c1.m, rfiaa_oct_c1.m</em> </td> <td>These four functions are used to apply fast IAA and MIAA. See <em>script MIAA_ISAM_processing.m</em> for their usage.</td> </tr> <tr> <td><em>viridis.m, morgenstemning.m</em></td> <td>These scripts define the colormaps for the figures.</td> </tr> <tr> <td><em>plot_figure3.m, plot_figure5.m, plot_simulationdatafigure.m</em></td> <td>These scripts are used to plot the figures 3 and 5 and a figure with simulation data. These scripts are executed at the end of script <em>MIAA_ISAM_processing.m.</em></td> </tr> <tr> <td>Python script: <em>computational_adaptive_optics_script.py</em></td> <td>Python script that applied computational adaptive optics to obtain the data for figure 6 of the manuscript.</td> </tr> <tr> <td>Python script: <em>zernike_functions2.py</em></td> <td>Python script that gives the values and carthesian derrivatives of the Zernike polynomials.</td> </tr> <tr> <td><em>figure6_ComputationalAdaptiveOptics.m</em></td> <td>Script that loads the CAO data that was saved in Python, analyzes the resolution, and plots figure 6.</td> </tr> <tr> <td>Python script: <em>OCTsimulations_3D_script2.py</em></td> <td>Python script simulates OCT data, adds noise and saves it as .mat file for use in the matlab script above.</td> </tr> <tr> <td>Python script: <em>OCTsimulations2.py</em></td> <td>Module that contains a python class that can be used to simulate 3D OCT datasets based on a Gaussian beam.</td> </tr> <tr> <td>Matlab toolbox DIPimage 2.9.zip</td> <td>Dipimage is used in the scripts. The toolbox can be downloaded online or this zip can be used.</td> </tr> </tbody> </table> <table> <caption>The datasets in this Zenodo repository</caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>input_leafdisc_phasecorrected.mat</td> <td>Phase corrected input image of the leaf disc (used in figure 5).</td> </tr> <tr> <td>input_TiO2gelatin_004_phasecorrected.mat</td> <td>Phase corrected input image of the TiO2 in gelatin sample.</td> </tr> <tr> <td>input_simulations_pointscatters_SLDshape_98zf_noise75</td> <td>Input simulation data that, once processed, is used in figure 4.</td> </tr> <tr> <td> <p>exp_pointscat_image_DFT.mat</p> <p>exp_pointscat_image_DFT_ISAM.mat</p> <p>exp_pointscat_image_RFIAA.mat</p> <p>exp_pointscat_image_MIAA_ISAM.mat</p> <p>exp_pointscat_image_MIAA_ISAM_CAO.mat</p> </td> <td>Processed experimental amplitude data for the TiO2 point scattering sample with respectively DFT, DFT+ISAM, RFIAA, MIAA+ISAM and MIAA+ISAM+CAO. These datasets are used for fitting in figure 4 (except for CAO), and MIAA_ISAM and MIAA_ISAM_CAO are used for figure 6.</td> </tr> <tr> <td> <p>simu_pointscat_image_DFT.mat</p> <p>simu_pointscat_image_RFIAA.mat</p> <p>simu_pointscat_image_DFT_ISAM.mat</p> <p>simu_pointscat_image_MIAA_ISAM.mat</p> </td> <td>Processed amplitude data from the simulation dataset, which is used in the script for figure 4 for the resolution analysis.</td> </tr> <tr> <td> <p>exp_leaf_image_MIAA_ISAM.mat</p> <p>exp_leaf_image_MIAA_ISAM_CAO.mat</p> </td> <td>Processed amplitude data from the leaf sample, with and without aberration correction which is used to produce figure 6.</td> </tr> <tr> <td> <p>exp_leaf_zernike_coefficients_CAO_normal_wmaf.mat</p> <p>exp_pointscat_zernike_coefficients_CAO_normal_wmaf.mat</p> </td> <td>Estimated Zernike coefficients and the weighted moving average of them that is used for the computational aberration correction. Some of this data is plotted in Figure 6 of the manuscript.</td> </tr> <tr> <td>input_zernike_modes.mat</td> <td>The reference Zernike modes corresponding to the data that is loaded to give the modes the proper name.</td> </tr> <tr> <td> <p>exp_pointscat_MIAA_ISAM_complex.mat</p> <p>exp_leaf_MIAA_ISAM_complex</p> </td> <td>Complex MIAA+ISAM processed data that is used as input for the computational aberration correction. </td> </tr> </tbody> </table> <p> </p>
Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography
<p>Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe<sub>3</sub>O<sub>4</sub> metamaterial, Co<sub>3</sub>O<sub>4</sub> - Mn<sub>3</sub>O<sub>4</sub> core-shell nanocrystals, and ZnS-Cu<sub>0.64</sub>S<sub>0.36</sub> nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.</p>
Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) for simultanouse chromatin, RNA-chromatin and transcriptome mapping at single cell resolution
<p><a href="https://doi.org/10.1101/2023.06.28.546457">MUSIC manuscript:</a> Joint profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations in single cells of the human brain.</p> <p>MUSIC-docker is the customized pipeline to process the raw fastq files to bam files: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/intro.html.</p> <p>Each bam file records the final output of our single cell mixed species analysis. Read name recods the cell barcode, complex barcode and I7 index. For each DNA/RNA read, read header contains <code>raw read name</code>| <code>BC3</code>_<code>BC2</code>_<code>BC1</code> - <code>10x barcode</code> # <code>UMI</code>. Details of the read name can be found here: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/step.html#demultiplexing.</p> <p>merge_DNA [RNA]_human [mouse].sort.bam: bam file of DNA [RNA] reads from the mix species library (H1+E14) that can uniquely mapped to the human [mouse] genome. PCR duplicates have been removed. </p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets for Supervised Matching in Clean-Clean Entity Resolution
<p>The repository includes 13 established datasets for evaluating ML- and DL-based matching algorithms:</p> <ol> <li>Structured DBLP-ACM</li> <li>Structured DLBLP-Scholar</li> <li>Structured iTunes-Amazon</li> <li>Structured Walmart-Amazon</li> <li>Structured BeerAdvo-RateBeer</li> <li>Structured Amazon-Google Products</li> <li>Strucutred Fodors-Zagats</li> <li>Dirty DBLP-ACM</li> <li>Dirty DBLP-Scholar</li> <li>Dirty iTunes-Amazon</li> <li>Dirty Walmart-Amazon</li> <li>Textual Abt-Buy</li> <li>Textual CompanyA-CompanyB</li> </ol> <p>Additionally, the repository includes five new benchmark datasets that are drawn from the following databases using a principled approach based on DeepBlocker:</p> <ol> <li>Abt-Buy</li> <li>Amazon-Google Products</li> <li>DBLP-ACM</li> <li>IMDB-TMDB</li> <li>IMDB-TVDB</li> <li>TMDB-TVDB</li> <li>Walmart-Amazon</li> <li>DBLP-Google Scholar</li> </ol> <p>The datasets are available in different formats so that they can be processed by the following matching algorithms:</p> <ol> <li>EMTransformer</li> <li>GNEM</li> <li>HierMatcher</li> <li>Magellan</li> <li>ZeroER</li> </ol>
Candidate High-Resolution Mass Spectrometry-Based Reference Method for the Quantification of Procalcitonin in Human Serum Using a Characterized Recombinant Protein as a Primary Calibrator
<p>Dataset related to Huu-Hien Huynh, Vincent Delatour, Maxence Derbez-Morin, Qinde Liu, Amandine Boeuf, and Joëlle Vinh, (2022) Candidate High-Resolution Mass Spectrometry-Based Reference Method for the Quantification of Procalcitonin in Human Serum Using a Characterized Recombinant Protein as a Primary Calibrator. Anal. Chem. 2022, 94, 10, 4146–4154.</p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </p>
Data from "Understanding and controlling spatial resolution, sensitivity, and surface selectivity in resonant-mode photothermal-induced resonance spectroscopy"
<p>Dataset of PTIR spectra used for the article "Understanding and controlling spatial resolution, sensitivity, and surface selectivity in resonant-mode photothermal-induced resonance spectroscopy", L. Quaroni, Ana. Chem. 2020, 92, 5, 3544-3554</p>
Dataset to publication: ''Magnetstein: An open source tool for quantitative NMR mixture analysis robust to low resolution, distorted lineshapes and peak shifts''
<p>MANIQ (plugin for Mnova 14.3.1 software (Mestrelab Research, S. L., Spain)) and ACD/TP (Targeted Profiling tool for ACD/Spectrus 2020.1.1 (Advanced Chemistry Development, Inc., ACD/Labs Ontario, Toronto, ON, Canada)) project files and raw experimental NMR data to:</p> <table> <caption>Experiments obtained to Magnetstein with optimized parameters, Magnetstein with default parameters, ACD/TP and MANIQ.</caption> <tbody> <tr> <td>Exp. no.</td> <td>Problem</td> <td>Ingredients</td> </tr> <tr> <td>1</td> <td>large intensity differences<br> between peaks</td> <td>α-pinene<br> benzyl benzoate<br> (in CDCl<sub>3</sub>)</td> </tr> <tr> <td>2</td> <td>peak overlap<br> & contamination (extra peaks)</td> <td>imonene<br> α-pinene<br> (in CDCl<sub>3</sub>)</td> </tr> <tr> <td>3</td> <td>low resolution (43 MHz<br> benchtop NMR spectrometer)</td> <td>benzyl benzoate<br> isopropyl myristate<br> limonene<br> pinene</td> </tr> <tr> <td>4</td> <td>lineshape distortion<br> (shim z1 & z2)</td> <td>actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O)</td> </tr> <tr> <td>5</td> <td>peak position<br> mismatch<br> between library<br> and mixture<br> (different<br> temperatures)</td> <td>actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O)</td> </tr> <tr> <td>6</td> <td>test sample</td> <td>α-pinene<br> benzyl benzoate<br> (in CDCl<sub>3</sub>)</td> </tr> <tr> <td>7</td> <td>large intensity<br> differences<br> & peak overlap</td> <td><br> benzyl benzoate<br> m-anisaldehyde<br> (in CDCl<sub>3</sub>)</td> </tr> <tr> <td>8</td> <td><br> peak overlap &<br> different solvents<br> for single<br> ingredients (in CDCl<sub>3</sub>)<br> and mixture (DMSO-d<sub>6</sub>)</td> <td>benzyl benzoate<br> m-anisaldehyde</td> </tr> <tr> <td>9</td> <td>ineshape<br> distortion<br> (shim z2)</td> <td>actate<br> alanine<br> creatine<br> creatinine<br> choline chloride<br> (in D<sub>2</sub>O)</td> </tr> </tbody> </table> <p> </p>
Learning curve - varying Resolutions
<p>deep learning model's mAP & Accuracy curve for varying Resolutions</p> <p>Git: <a href="https://github.com/aphil22/Chocolate_Anomaly_Detection/blob/main/03_02_Train_Test_Resolutions.ipynb">Link to Git repository</a></p>
Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data
<p>This dataset contains a land cover map of Poitiers in 1993 over an area of 225km².</p> <p>The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution.</p> <p>Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset.</p> <p>As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users.</p> <p>This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.</p>
Texas Statewide Landcover, Ecological Systems, and Percentage Canopy Classifications (10-meter Resolution) (2021)
<p>Statewide landcover, ecosystem, and percentage canopy cover classifications for Texas were mapped at a spatial resolution of 10-meters. Classifications were run for 16 zones across the state corresponding to available cloud-free multitemporal Sentinel-2 satellite imagery for each zone. For each zone, RandomForest classifications were run using data stacks comprised of spectral bands from three dates (winter, early growing season, late growing season/leaf-off) of imagery, as well as multiple vegetation indices (NDVI, EVI2, MSAVI2). Over 50,000 training points were selected from ground trips and high-resolution aerial image surveys to run the entire pixel-based classification. The overlapping zones were merged using a feathering algorithm to produce a single statewide land-cover classification map. The landcover mapping results were further refined using multiple spatial masks (e.g., urban, water, crop) along with logical rulesets and ancillary data. To map ecological systems, the land-cover classification was then intersected with an enduring features dataset derived primarily from soil map-unit polygons (gSSURGO) and other geophysical variables. Additionally, we produced a statewide percentage canopy cover map at a 10-meter spatial resolution using multiple techniques. For the western 2/3 of the Texas, a nested machine learning approach was used (<a href="https://doi.org/10.1016/j.rse.2020.111748">Sunde et al., 2020</a>), and for the eastern 1/3 of the state, a combination of LiDAR derived training data and machine learning was used.</p> <p>This dataset includes four items:</p> <ol> <li><strong>"TX_10m_landcover_2021.zip" - Statewide landcover classification for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_10m_ecoclass_map_2021.zip" - Statewide ecological systems classification for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_10m_canopy_cover_2021.zip" - Statewide percentage canopy cover map for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_lu_ecoclass_key.xlsx" - Table containing keys for the mapped landcover, canopy, and ecological systems classes</strong></li> </ol> <p>(To facilitate display of the datasets within ESRI software, .lyr files are included in the respective archive folders)</p> <p><em>This work was funded by the Texas A&M Forest Service.</em></p>
Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development
<p class="MsoNormal"><span>With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called 'genoscapes') for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.</span></p>
Tornado Detection From Full-Resolution Polarimetric Weather Radar Data (SAMPLE)
<p>This dataset contains a small sample of the tornado dataset described in the talk "A Tornado Detection Algorithm using Deep Neural Networks, Full-Resolution Polarimetric Weather Radar Data, and Explainable AI" presented at the 40th Conference on Radar Meteorology on Aug 31st 2023.</p> <p>The files contained in this data represent approximately 1% of the full dataset that will be released upon final publication.</p> <p>For questions please contact</p> <p>James.Kurdzo@ll.mit.edu and mark.veillette@ll.mit.edu</p>
High-Resolution Vegetation Height Maps for Switzerland in 2017-2020
<p>This dataset comprises 10m-resolution vegetation height maps for Switzerland spanning from 2017 to 2020. It encompasses both mean and maximum vegetation height data, generated through the integration of Sentinel-2 and airborne laser scanning information.</p>
Phylogenomics improves the phylogenetic resolution and provides strong evidence of mito-nuclear discordance in two genera of a New Zealand cicada (Hemiptera: Cicadidae) species radiation
<p>Rapid species radiations present difficulties for phylogenetic reconstruction due to lack of phylogenetic information and processes such as deep coalescence/incomplete lineage sorting and hybridization. Phylogenomic data can overcome some of these difficulties. In this study, we use Anchored Hybrid Enrichment (AHE) nuclear phylogenomic data and mitochondrial genomes recovered from AHE bycatch with several concatenated and coalescent approaches to reconstruct the poorly-resolved radiation of the New Zealand cicada species in the genera <em>Kikihia</em> Dugdale and <em>Maoricicada</em> Dugdale. Compared to previous studies using only three to five Sanger-sequenced genes, we find increased resolution across our phylogenies, but several branches remain unresolved due to topological conflict among genes. Some nodes that are strongly supported by traditional support measures like bootstraps and posterior probabilities still show significant gene and site concordance conflict. Additionally, we find strong mito-nuclear discordance; likely the result of interspecific hybridization events in the evolutionary history of <em>Kikihia</em> and <em>Maoricicada</em>.</p>
GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning
<p>The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.</p> <p>Data Information: </p> <p>GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning</p> <p>Publication: </p> <p><span>He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. <em>Geography and Sustainability</em>, <em>6</em>(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007</span></p> <p></p> <p>----------------------------------------------------data description---------------------------------------------</p> <p>This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:</p> <p>Region: China</p> <p>Temporal Extent: January 1901 to December 2020</p> <p>Spatial resolution: 0.0083° (~1 km)</p> <p>Temporal resolution: month</p> <p>Timescales: 1-month, 3-month, 6-month and 12-month</p> <p>Data format: GeoTIFF</p> <p>Unit: unitless (0.01)</p> <p>Geographic coordinate system: WGS 1984</p> <p>---------------------------------------------------dataset filename---------------------------------------------</p> <p>The file name specifically shows the data information.</p> <p>For example,</p> <p>“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.</p> <p>“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.</p> <p>All the file names are formatted in “SPEI_timescale_year_month”</p> <p>timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively</p> <p>year: from 1901 to 2020</p> <p>month: from 1 to 12</p> <p>--------------------------------------------------storage information-------------------------------------------</p> <p>The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.</p> <p>The data were compressed into .rar format every 10 years for each timescale SPEI.</p>
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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)
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DANDI Archive for NWB datasets
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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.