Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,113
datasets available to search
ShareScore release 0.7.1
Dataset results
2,113 results for “High resolution”
ENTROPY DR1. High-resolution near-UV/optical spectra of 2MASS J11151597+1937266
<h1>ENTROPY Data Release 1 (v2.0)</h1> <h2>Summary</h2> <p>The files in this data release consist of the flux-calibrated, stacked 1D spectum of the isolated, free-floating accreting planetary-mass object 2MASS J11151597+1937266 (2M1115). The data results from the high-resolution (R~50,000) observations by the Ultraviolet and Visual Echelle Spectrograph (UVES) at ESO's Very Large Telescope (VLT) in Chile, between 10-11 June 2023, taken as part of the ESO programme 0111.C-0166(A). This is the underlying data used for the analysis in the publication <a href="https://doi.org/10.1051/0004-6361/202450881" target="_blank" rel="noopener">Viswanath et al. (2024)</a> </p> <h2>Details of Observations</h2> <p>The observations of 2M1115 with UVES were carried out between 10-11 June 2023 (MJD 60105, 60106) as part of the ExoplaNeT accretion mOnitoring sPectroscopic surveY (ENTROPY) in #Dichroic 1 mode with both the blue (390 nm) and red (580 nm) arms using a 0.8 arcsec wide slit without AO or chopping.</p> <p>A total of four frames at 740 s exposure (NDIT=1) each were obtained across the two nights, giving a total integration time of 0.82 hr at an average seeing of 1.43 arcsec and an average airmass 1.416. The total wavelength range covered by the spectrum is 320–680 nm.</p> <h2>Target</h2> <p>Identifier: 2MASS J11151597+1937266</p> <p>ICRS RA (ep=2016.0): 168.816234 deg</p> <p>ICRA DEC (ep=2016.0): +19.623939 deg</p> <p>Distance: 45.21 +- 2.20 pc</p> <p>Age: 5-45 Myr</p> <p>Mass: 6(+8, -4) Mjup</p> <p>Radius: 1.5 +- 0.1 Rjup</p> <p>Effective Temperature: 1816 +- 63 K</p> <h2>Data reduction</h2> <p>Each of the 4 raw data frames from the original set of exposures from the 2 nights were bias subtracted and flat-field corrected using the calibration files from ESO. Inter-order background was also subtracted. Cosmic rays were accounted for by using a horizontal median filtering and masking out pixels higher than 10 times the local variance. The flux was extracted order by order using standard aperture photometry, which included the subtraction of sky background and telluric lines.</p> <p>The flux was calibrated based on the observations of a standard star taken contemporaneously with the same observing setup as the target, and was corrected for the relative slit loss (~4%) from seeing. Wavelength calibration was performed using the arc lamp spectrum and a Th–Ar line list. The wavelengths listed in the spectrum are in air. A barycentric velocity correction was applied to the wavelength calibration to transform the spectral reference system to that of the source. The stacked 1D spectrum available in this data release is obtained by taking the weighted average of the 4 individual spectra, with the weights determined from the respective photon noise.</p> <h2>Description of files</h2> <p>The data release in this second version consists of:</p> <p>a) 1 merged spectrum UVES-2023.06.10-11.2M1115.merged-spectra.txt covering the entire UVES range of these observations (320–680 nm). The flux for the overlapping wavelengths in adjacent orders of each arm were averaged to get a single flux value per wavelength. This allows for easy access of the entire spectral range of all arms together. The ASCII file has 4 columns:</p> <p>Column1 : Wavelength, in ångstrøm</p> <p>Column2 : Flux, in units ergs/s/cm2/ångstrøm</p> <p>Column3 : Flux_stddev, the weighted standard deviation of the flux across the 4 individual spectra, in units of ergs/s/cm2/ångstrøm (This is the recommended error bar)</p> <p>Column4 : Flux_phot_err, Average of the photometric uncertainties in flux across the 4 spectra, in units of ergs/s/cm2/ångstrøm</p> <p>b) The compressed file UVES-2023.06.10-11.2M1115.non-merged-spectra.zip contains one folder per each of the three arms (Blue, RedL, RedU). The folders include ASCII files containing the spectrum for individual orders of each arm, provided for the purpose of preserving the signal-to-noise ratio (S/N) of all orders. Some important emission lines like H𝛽 occur in both adjacent orders but at different signal strengths. Merging the orders by averaging the flux as in file (a) will suppress the S/N of such emission lines.</p> <p>The order numbers of the respective arms containing detected emission lines from 2M1115 can be found in Tables 2 and G.1 in <a href="https://doi.org/10.1051/0004-6361/202450881" target="_blank" rel="noopener">Viswanath et al. (2024)</a>. For the following Balmer lines detected in <a href="https://doi.org/10.1051/0004-6361/202450881">Viswanath et al. (2024)</a>, the corresponding rest wavelengths appear in two consecutive orders as indicated below:</p> <table> <tbody> <tr> <td><strong>Line</strong></td> <td><strong>Arm</strong></td> <td><strong>Orders</strong></td> <td><strong>Detection in Viswanath et al. (2024)</strong></td> </tr> <tr> <td>H𝛽</td> <td>RedL</td> <td>2, 3</td> <td>Both at > 3σ S/N </td> </tr> <tr> <td>Hγ</td> <td>Blue</td> <td>34, 35</td> <td>Both at > 3σ S/N </td> </tr> <tr> <td>Hδ</td> <td>Blue</td> <td>28, 29</td> <td>Only in 28</td> </tr> <tr> <td>H7</td> <td>Blue</td> <td>24, 25 </td> <td>Both, but only ~2σ S/N in 24</td> </tr> <tr> <td>H9</td> <td>Blue</td> <td>20, 21</td> <td>At only ~2σ S/N in both</td> </tr> </tbody> </table> <p> </p> <p>Each ASCII file has 4 columns with same description as in file (a). To plot the spectrum of all the orders of all the arms, the following python code can be used:</p> <p><code>import numpy as np</code><br><code>from glob import glob</code><br><code>from natsort import natsorted</code><br><code>arms = ['BLUE', 'REDL', 'REDU']</code><br><code>plt.figure()</code><br><code>for arm in arms:</code><br><code> files = natsorted(glob(f'UVES-2023.06.10-11.2M1115.non-merged-spectra/{arm}/*.txt'))</code><br><code> print(files)</code><br><code> for i in range(len(files)):</code><br><code> data = np.loadtxt(files[i])</code><br><code> plt.plot(data[:, 0], data[:, 1])</code><br><code>plt.show()</code></p> <pre> </pre> <p>c) 2M1115-Photometry.txt, an ASCII file containing the existing photometry for the target</p> <h2> </h2> <h2>Changes from previous version V1.0</h2> <p>The merged spectrum UVES-2023.06.10-11.2M1115.merged-spectra.txt in this version has been modified to preserve the original spectral resolution in the overlapping wavelegth regions. In V1.0, the corresponding regions had double the resolution due to an error in the interpolation method used.</p> <p>The non-merged spectra of the individual orders have been replaced in this version as a compressed zip folder (instead of .npz files) containing ASCII files for spectra of each individual order to facilitate easier data handling.</p> <p>The photometry file has been updated from that of last version with more information, including two additional filter bands (<em>Gaia</em> Gbp and Grp) as well as effective filter widths of all provided filter bands.</p>
High-resolution Industrial Production Energy (HIPE)
<p>The High-resolution Industrial Production Energy (HIPE) data set contains smart meter readings of ten machines and the main terminal of a power-electronics production plant over three months.</p> <p><a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">The accompanying publication (published by ACM)</a> describes the data set and outlines open challenges and use cases with industrial energy data. This includes a dis­cussion of cha­rac­ter­is­tics of in­dus­tri­al machines and of differences to residential appliances.</p> <p>Links:</p> <ul> <li>Publication: <a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">https://doi.org/10.1145/3208903.3210278</a></li> <li>Description: <a href="https://www.energystatusdata.kit.edu/hipe.php">https://www.energystatusdata.kit.edu/hipe.php</a></li> </ul>
Six years of high-resolution monitoring data of 40 borehole heat exchangers
<p>This dataset provides six years of monitoring data of the ground heat exchanger that supplies heat and cold to the E.ON Energy Research Center located in Aachen, Germany. The data belong to the following publication:</p> <blockquote> <p>Heim, E., Stoffel, P., Müller, D., & Klitzsch, N. (2024). <em>Six years of high-resolution monitoring data of 40 borehole heat exchangers</em>. Scientific Data, 11(1), 1334. <a href="https://doi.org/10.1038/s41597-024-04241-9" rel="nofollow">https://doi.org/10.1038/s41597-024-04241-9</a></p> </blockquote> <p>The ground heat exchanger consists of 40 double-U-loop borehole heat exchangers (BHE), that are arranged in three subfields. Each subfield is connected to an underground vault, in which sensors for the inlet fluid temperature, the outlet fluid temperature and the volume flow of each BHE are placed. The sensors record data in 30-second intervals. Coherent data is provided from July 1, 2018 to June 30, 2024 in two time resolutions and processing levels:</p> <ul> <li><strong>Raw data</strong>: The raw data in its initial resolution, aligned to coherent 30-second timestamps.</li> <li><strong>Prepared data</strong>: The raw data was resampled to 5 minute intervals using the weighted mean, with the volume flow as weight. Moreover, data periods that are not representative of the thermal exchange process in the BHE were masked (e.g., no-flow periods).</li> </ul> <p>Both raw and prepared data are provided as .csv files for each month individually. Temperature measurements are given in °C, the volume flow rate in l/min. </p> <p>In addition, a <strong>supporting file</strong> indicating the BHE locations is provided. It also contains the length of the horizontal connecting pipes going from the underground vaults (where the sensors are placed) to the BHE heads. </p> <p><strong>Code </strong>explaining how to open and work with the data is provided in a separate github repository:<strong> </strong><a href="https://github.com/elimh/ERC_BHEfield_Data_Code">https://github.com/elimh/ERC_BHEfield_Data_Code</a></p>
High resolution reanalysis for the northern Adriatic Sea (CADEAU project - CMEMS Demonstration 32-DEM-L5)
<p><span><span>This dataset includes the output of the high resolution reanalysis carried out in the framework of the CADEAU project (CMEMS Demonstration 32-DEM-L5). </span></span></p> <p><span><span>The reanalysis covers the northern Adriatic Sea for the 2006-2017 time period; the simulation is characterized by a horizontal resolution of 1/128° and 27 non-equally spaced vertical layers.</span></span></p> <p><span><span>The dataset includes the following physical and biogeochemical variables (averaged every 5 days): (1) salinity (S), temperature (T), chlorophyll (Chla), nitrate (N3n), phosphate (N1p).</span></span></p>
Supplemental High Resolution IPWM Run for "Ion heating in the polar cap under northwards IMF Bz": SDOP_April2016
<p>Full high resolution output from the Ionosphere/Polar Wind Model (IPWM) for results published in "Ion heating in the polar cap under northwards IMF Bz", by L. J. Lamarche, R. H. Varney, and A. S. Reimer. This is a simulation of the April 2016 event, with IPWM driven by SuperDARN convection maps and Ovation Prime precipitation (SDOP_April2016). Scripts to visualize these model output and produce the figures in the article are available in a separate repository <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>.</p> <p>If you are configuring the Resen bucket available in <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>, mount this repository to /home/jovyan/mount/SDOP_April2016_output.</p>
Supplemental High Resolution IPWM Run for "Ion heating in the polar cap under northwards IMF Bz": SDOP_May2014
<p>Full high resolution output from the Ionosphere/Polar Wind Model (IPWM) for results published in "Ion heating in the polar cap under northwards IMF Bz", by L. J. Lamarche, R. H. Varney, and A. S. Reimer. This is a simulation of the May 2014 event, with IPWM driven by SuperDARN convection maps and Ovation Prime precipitation (SDOP_May2014). Scripts to visualize these model output and produce the figures in the article are available in a separate repository <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>.</p> <p>If you are configuring the Resen bucket available in <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>, mount this repository to /home/jovyan/mount/SDOP_May2014_output.</p>
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
<p>Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.</p>
A high-resolution finite element method (FEM) human head model for non-invasive brain stimulation
<p>High-resolution finite element method (FEM) model of a human head for non-invasive brain stimulation modeling using SimNIBS or other compatible software. The original head model (Ernie) was downloaded from the tutorial dataset of <a href="http://simnibs.org">www.simnibs.org</a> and further refined in grey matter and white matter regions.</p> <p>This supplementary dataset is released as part of the NeMo-TMS toolbox (<a href="https://github.com/OpitzLab/NeMo-TMS">https://github.com/OpitzLab/NeMo-TMS</a>). Please refer to the corresponding article for more information:</p> <p>Shirinpour, S., Hananeia, N., Rosado, J., Galanis, C., Vlachos, A., Jedlicka, P., Queisser, G., & Opitz, A. (2020). Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation. <em>BioRxiv</em>, 2020.09.23.310219. <a href="https://doi.org/10.1101/2020.09.23.310219">https://doi.org/10.1101/2020.09.23.310219</a></p>
High spatial resolution thickness changes of Pyrenean glaciers from 2011 to 2020
<p>Pyrenean glaciers are the largest in southern Europe. Because their very survival is threatened by climate change in the coming decades, it is urgently necessary to monitor their evolution. This dataset presents high spatial resolution observation of Pyrenean glaciers during the period 2011–2020. In 2011, the glacier surface was retrieved with an airborne lidar and in 2020 data were obtained with three different UAVs (fixed-wing or quad copters). Comparing these observations, glacier surface changes were computed for 17 out of the 24 remaining glaciers. Using high-resolution optical satellite imagery and field observations, glacier outline changes between 2011 and 2020 were also obtained. On average the glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m.</p>
Molecular dynamics simulations in: High-resolution structures with bound Mn2+ and Cd2+ map the metal import pathway in an Nramp transporter
<p>Transporters of the Nramp (Natural resistance-associated macrophage protein) family import divalent transition metal ions into cells of most organisms. By supporting metal homeostasis, Nramps prevent disorders related to metal insufficiency or overload. Previous studies revealed that Nramps take on a LeuT fold and identified the metal-binding site. We present high- resolution structures of <em>Deinococcus radiodurans</em> Nramp in three stable conformations of the transport cycle revealing that global conformational changes are supported by distinct coordination geometries of its physiological substrate, Mn2+, across conformations and conserved networks of polar residues lining the inner and outer gates. A Cd2+-bound structure highlights differences in coordination geometry for Mn2+ and Cd2+. Measurements of metal binding using isothermal titration calorimetry indicate that the thermodynamic landscape for binding and transporting physiological metals like Mn2+ is different and more robust to perturbation than for transporting the toxic Cd2+ metal.</p>
Supplementary Dataset for "A new mechanical perspective on a shallow megathrust near-trench slip from the high- resolution fault model of the 2011 Tohoku-Oki earthquake"
<p>This dataset contains the results obtained by the analysis in this study, such as the digital data of the slip distribution and stress drop estimated by Kubota et al.(2022)</p>
Tracing pathways from high-resolution tractography, transcription, and temporal dimensions
<p>The neural circuits supporting human cognition are topics of enduring interest. The lack of tools available to map circuits has precluded our ability to trace the evolution of the human connectome. We harnessed high-resolution connectomic, anatomic, and transcriptomic data to develop enhanced tools to test for modifications in developmental programs across species. We found corresponding ages across species and transcriptionally define neurons with stereotypical projections in humans and macaques. We used these data to test for modifications in frontal cortex circuit. Frontal cortex circuitry development is extended in primates, which is concomitant with an expansion in cortico-cortical pathways compared with mice in adulthood. These parameters varied little across humans and macaques. We identify a collection of conserved features in frontal cortex circuits in studied primates. We demonstrate that the integration of transcriptional and connectomic data across temporal dimensions is a robust approach to trace the evolution of connections in primates. This dataset contains scripts as well as diffusion MR scans of mouse brains.</p>
High resolution solar irradiance variability climatology dataset part 2: classifications, supplementary data, and statistics
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a></p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance series classification, cloud shadow and enhancement statistics, and satellite observations for studying intra-day surface solar irradiance variability.</p> <p><strong>Part 2 of 2</strong></p> <p>This dataset is the derived from the <a href="https://doi.org/10.5281/zenodo.7093164">1 Hz observational record of direct, diffuse, and global horizontal irradiance</a> measured by the Baseline Surface Radiation Network station at Cabauw, the Netherlands. More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>.</p> <p><strong>Methodology</strong></p> <p>An extensive dataset description is currently being written for Earth System Science Data. In the mean time, a more condensed description is available in preprint at <a href="https://arxiv.org/abs/2209.10284">Arxiv</a>.</p> <p>Processing scripts are published at this <a href="https://zenodo.org/record/7099491">Zenodo release</a>.</p> <p><strong>Dataset contents</strong></p> <p>This dataset contains daily time series with the following data, from 2011-02 until 2020-12-31:</p> <ol> <li>Cloud shadow and cloud enhancement time series classifications (see methodology)</li> <li>Overcast, clear-sky and variable time series classifications (see methodology)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> for clear-sky global horizontal irradiance (version 3.5)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> atmospheric composition input (aerosols, ozone, and total column water vapour)</li> <li>Solar elevation and azimuth angles (calculated using <a href="https://github.com/pingswept/pysolar/releases/tag/0.10">PySolar</a>)</li> <li>Quality flags: non-official 1 Hz and official 1-minute (from <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">BSRN at PANGAEA</a>)</li> <li>Cabauw observatory<a href="https://dataplatform.knmi.nl/dataset/cesar-tower-meteo-lb1-t10-v1-2"> tower wind speed and direction</a></li> </ol> <p>Additional satellite data time series from 2014-01 until 2016-12:</p> <ol> <li>MSGCPP satellite data for an area over central Netherlands (<a href="https://essd.copernicus.org/articles/9/415/2017/">CLAAS2 source</a>)</li> <li>Post processed timeseries of cloud types over Cabauw derived from this MSGCPP satellite data</li> <li>A nubiscope + satellite derived validation dataset for overcast and clear-sky classifications</li> </ol> <p>Statistics files:</p> <ol> <li>Cloud shadow and cloud enhancement event detection and event statistics based on the time series for 2011-2020</li> <li>Daily radiation statistics for 2011-2020</li> </ol> <p>And finally, for all days there are quicklooks available that visualize the irradiance time series, classification, and if available satellite data.</p> <p><strong>Version History</strong></p> <p><em>New in v1.1</em><strong> </strong></p> <ul> <li>CAMS McClear updated from v3.1 to v3.5 (2011-2020)</li> <li>Fix incorrect dominant cloud type in CLAAS2 timeseries (`claas2_processed.zip`, 2014-2016)</li> <li>Update timeseries statistics and quicklooks with new clear-sky data (2011-2020)</li> <li>Added official quality flags (2011-2020)</li> <li>Added preprocessed validation dataset (2014-2016)</li> <li>Added nubiscope to quicklooks (2014-2016)</li> </ul>
High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula
<p><strong>Abstract:</strong></p> <p>Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017.</p> <p><strong>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</strong></p> <p>This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below:</p> <p><strong>Raster value Land cover/use category</strong></p> <p>1 Forest</p> <p>2 Marsh</p> <p>3 Salt patch</p> <p>4 Built</p> <p>5 Open water</p> <p>6 Farmland</p> <p>7 Bare soil</p> <p>8 Other vegetation</p> <p> </p> <p><strong>Input Data:</strong></p> <p>These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE.</p> <p><strong>Methods:</strong></p> <p>NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel.</p> <p>NDVI = (Near-infrared – Red) / (Near-infrared + Red)</p> <p>NDWI = (Green – Near-infrared) / (Green + Near-infrared)</p> <p>SI = (256 – Blue) * (256 + Blue)</p> <p>In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel.</p> <p>EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1)</p> <p>A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands.</p> <p>Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits.</p> <p><strong>Accuracy assessment:</strong></p> <p>A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below.</p> <table> <tbody> <tr> <td> <p><strong>Delaware 2013</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.46%</p> </td> <td> <p>90.89%</p> </td> <td> <p>0.90</p> </td> <td> <p>86.37%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>84.03%</p> </td> <td> <p>80.13%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.02%</p> </td> <td> <p>71.18%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>94.56%</p> </td> <td> <p>95.06%</p> </td> <td> <p>0.95</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>91.01%</p> </td> <td> <p>96.63%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.16%</p> </td> <td> <p>86.19%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.70%</p> </td> <td> <p>87.30%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>82.46%</p> </td> <td> <p>84.20%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Delaware 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>95.07%</p> </td> <td> <p>90.30%</p> </td> <td> <p>0.93</p> </td> <td> <p>91.37%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>88.86%</p> </td> <td> <p>92.44%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>91.82%</p> </td> <td> <p>85.59%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>87.58%</p> </td> <td> <p>93.54%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.68%</p> </td> <td> <p>90.48%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>91.61%</p> </td> <td> <p>93.61%</p> </td> <td> <p>0.93</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>95.67%</p> </td> <td> <p>87.67%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>91.16%</p> </td> <td> <p>90.24%</p> </td> <td> <p>0.91</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2011</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.32%</p> </td> <td> <p>90.97%</p> </td> <td> <p>0.90</p> </td> <td> <p>87.20%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.14%</p> </td> <td> <p>82.08%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>96.74%</p> </td> <td> <p>78.76%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>89.02%</p> </td> <td> <p>88.50%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.74%</p> </td> <td> <p>96.10%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>84.31%</p> </td> <td> <p>89.83%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.53%</p> </td> <td> <p>90.05%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.11%</p> </td> <td> <p>80.57%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.66%</p> </td> <td> <p>88.66%</p> </td> <td> <p>0.89</p> </td> <td> <p>87.34%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.65%</p> </td> <td> <p>88.35%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>93.29%</p> </td> <td> <p>68.30%</p> </td> <td> <p>0.79</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>93.44%</p> </td> <td> <p>86.92%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.36%</p> </td> <td> <p>92.36%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.84%</p> </td> <td> <p>94.55%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>92.86%</p> </td> <td> <p>82.61%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.56%</p> </td> <td> <p>76.00%</p> </td> <td> <p>0.81</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2012</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>91.18%</p> </td> <td> <p>0.88</p> </td> <td> <p>86.88%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.03%</p> </td> <td> <p>84.39%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.67%</p> </td> <td> <p>72.41%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>90.97%</p> </td> <td> <p>86.24%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>94.17%</p> </td> <td> <p>87.39%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>86.87%</p> </td> <td> <p>91.81%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>85.82%</p> </td> <td> <p>83.04%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>83.60%</p> </td> <td> <p>80.59%</p> </td> <td> <p>0.82</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2016</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>86.00%</p> </td> <td> <p>0.85</p> </td> <td> <p>85.83%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>86.25%</p> </td> <td> <p>90.72%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>90.61%</p> </td> <td> <p>62.12%</p> </td> <td> <p>0.74</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>88.70%</p> </td> <td> <p>77.72%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.12%</p> </td> <td> <p>84.41%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>85.17%</p> </td> <td> <p>90.36%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>83.30%</p> </td> <td> <p>88.54%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>84.49%</p> </td> <td> <p>84.17%</p> </td> <td> <p>0.84</p> </td> </tr> </tbody> </table> <p>While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.</p> <p> </p> <p><strong>Data format:</strong></p> <p>The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Code:</strong> Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes.</p> <p><strong>Datasets for download:</strong></p> <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> <p>These data layers cover 3 counties: Kent, New Castle, Sussex.</p> <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> <p>These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester.</p> <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> <p>These data layers cover 2 counties: Accomack, Northampton.</p> <p>We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files. </p> <p><strong>Data citation:</strong></p> <p>Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.</p>
National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets
<p>Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.</p>
A Daily High-Resolution (1 km) Human Thermal Index Collection over the North China Plain from 2003 to 2020
<p>The daily <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection over the <strong>North China Plain </strong>(<strong>HiTIC-NCP</strong>) includes <em><strong>near-surface air temperature</strong></em><strong> </strong>(SAT) and 11 commonly used<strong> </strong><em><strong>human-perceived temperature</strong></em><strong> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), Wet-Bulb Temperature (WBT), simplified Wet-Bulb Globe Temperature (sWBGT), and Wind Chill Temperature (WCT). This daily dataset has a high spatial resolution of 1 km × 1 km and covers the North China Plain<strong> </strong>from January 2003 to December 2020. It has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. The dataset is stacked by year and each stack consists of 365 daily images in NetCDF format by day of the year. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) to save storage space, and thus need to be divided by 100 to get the values in degree Celcius when in use. The geographic coordinate system of the dataset is World Geodetic System (WGS) 1984 Coordinate System. Naming rules and other details can be found in "README.pdf".</p> <p>If you have any questions when using the HiTIC-NCP dataset, please feel free to contact Mr. Xiang Li via <a href="mailto:lixiang97@mail2.sysu.edu.cn">lixiang97@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:zhaoyq66@mail.sysu.edu.cn">yqzhao@link.cuhk.edu.hk</a>. More details on the procedure of producing the HiTIC-NCP dataset and its accuracy assessment can be found in:</p> <p>Li, X., Luo, M*., Zhao, Y*., Zhang, H., Ge, E., Huang, Z., Wu, S., Wang, P., Wang X., Tang Y. (2023). A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020. <em>Scientific Data</em>, 10, 634. https://doi.org/10.1038/s41597-023-02535-y</p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the results of photogrammetric processing (Digital Elevation Models, Orthomosaics and subset data used for volumetric calculation and visualization) named: “DEM_1967.7z”: inside the zipped folder “1967_DEM.tif” (digital elevation model produced for the year 1967), “DEM_1984.7z”: inside the zipped folder “1984_DEM.tif” exist (digital elevation model produced for the year 1984). In addition, under “1967_Orthomosaic.7z" and "1984_Orthomosaic.7z” zipped folders, there are orthomosaic files produced namely, “1967_orthomosaic.tif” and "1984_orthomosaic.tif” for the year 1967 and 1984, respectively. The DEMs and Orthomosaics subset from the results for sites (data example 1 and data example 2) reside under "Data_Examples.zip". Accuracy of the resulted data were assessed and the extracted elevation values are under "Accuracy_assessment.zip". All DEMs and Orthomosaics are in GeoTIFF format in the Adindan UTM Zone 37 N (EPSG: 20137) projected coordinate system.</p> <p> Potential application of the presented dataset include:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p>The inputs for the above resulted DEMs and Orthomosaics are found under Zenodo repository "10.5281/zenodo.7271617". </p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the inputs used for Structure from Motion Multiview Stereo photogrammetry processing for the year 1967 and 1984 i.e Unprocessed scanned historical aerial Photographs, camera position coordinates, flight index and Ground Control Points. All the scanned historical aerial photographs data are in TIFF format except four photographs in JPEG format under a zipped folder ("1967_Scanned_HAPs_Part1.7z and 1967_Scanned_HAPs_Part2.7z" for the 1967 Historical Aerial Photographs and "1984_Scanned_HAPs_Part1.7z and 1984_Scanned_HAPs_Part2.7z" for the 1984 Historical Aerial Photographs). The "Flight_Index.Zip" contains shapefiles of the camera position and polygon of consecutive aerial photograph index; "GCP.Zip" contains text file of the GCPs used for the 1967 and 1984; and "Camera_Position.Zip" contains the file of the camera position (Label, Easting, Northing and Altitude) of each historical aerial photographs. </p> <p>The results of the above dataset could be accessible on Zenodo repository "10.5281/zenodo.7269999".</p> <p>Anyone can reuse the presented dataset to produce DEMs and Orthomosaics; and use for the following application areas:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p> </p>
High Resolution Greenspace Land Cover in Philadelphia, Pennsylvania
<p>This dataset provides a high resolution (1-m) land cover map for Philadelphia, Pennsylvania in the United States of America during the summer of 2017. This dataset was created to differentiate two types of green space in Philadelphia: tree and grass cover. The dataset includes four numerically coded land cover classes.</p> <p><strong>Input data:</strong></p> <p>This classification is derived from National Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in the State of Pennsylvania during June of 2017. To improve classification accuracy, NAIP data was stacked with Sentinel-2 level 1C 10-m and 20-m data using the .addBands() function in Google Earth Engine. For the Sentinel-2 data, a median composite was calculated from cloud-masked images collected between April and October of 2017. Sentinel-2 input bands included blue, green, red, red edge 1, red edge 2, red edge 3, near infrared, and shortwave infrared 1. An additional normalized difference vegetation index (NDVI) was calculated from the NAIP and Sentinel-2 bands using the formula:</p> <p>NDVI = (Near infrared - Red) / (Near infrared + Red)</p> <p><strong>Classification methods:</strong></p> <p>We classified the input data using a Random Forest classifier with 200 trees. Data was classified into four coded land cover classes:</p> <p>1 - Tree</p> <p>2 - Grass</p> <p>3 - Human-built structures</p> <p>4 - Open water</p> <p>8,961 land cover reference points were collected with 70% used to train and 30% to test the classifier. Results were smoothed using a 3x3 square kernel based on the mode of a pixel’s neighbors.</p> <p><strong>Accuracy:</strong></p> <p>Measures of accuracy including overall accuracy and per class user’s (UA) and producer’s accuracy (PA) of the random forest classifier were calculated.</p> <p>Overall accuracy: 93%</p> <p>Tree: UA = 89.73% PA = 93.90%</p> <p>Grass: UA = 93.41% PA = 88.21%</p> <p>Human-built structures: UA = 98.28% PA = 97.47%</p> <p>Open water: UA = 93.56% PA = 98.95%</p> <p><strong>Code link:</strong></p> <p>The Google Earth Engine code used in this analysis is publicly available.</p> <p><a href="https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f">https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f</a></p> <p><strong>Data for download:</strong></p> <p>Two files are available for download.</p> <ol> <li>Philadelphia_classification_points.zip</li> </ol> <p>Contains a shapefile of the 8,961 reference points used to train and test the classifier.</p> <p> 2. Philadelphia_Landcover_2017.zip</p> <p>Contains a GEOTIFF of the classified image over Philadelphia, Pennsylvania for the summer of 2017.</p> <p> </p>
Data from: An expanded Smithian-Spathian (Early Triassic) boundary from a reefal build-up record in Oman: Implications for conodont taxonomy, high-resolution biochronology and the carbon isotope record
<p><span>Some 2.7 Ma after the Permian-Triassic boundary mass extinction (PTME), a stepwise extinction of the nekton (ammonoids and conodonts) ended at the Smithian-Spathian boundary (SSB) during an episode of climate cooling. SSB records from continental shelves are usually affected by an unconformity, suggesting a forced regression of glacio-eustatic origin. Here, we document a new 30 m-thick SSB section from Jebel Aweri (Batain Plain, Oman) that provides an exceptionally complete and expanded record preserved in an exotic block. Most of this SSB section consists of metazoan reefal build-ups that formed in shallow water on an offshore sea mount. In Wadi Musjah (Hawasina nappes, Oman), another exotic block records the SSB in a deeper water setting represented by Hallstatt-type facies. These two sections provide a unique perspective on the early Spathian rapid re-diversification of conodonts. They led to a thorough revision of conodont taxonomy around the SSB and to the construction of the highest resolution biochronological scheme for this time interval in the Tethys. A total of five SSB sections from Oman representing both offshore sea mounts and lower slope deposits were included in a high-resolution, quantitative Unitary Associations analysis. The resulting 8 conodont biozones are intercalibrated with ammonoid zones and with the carbonate carbon isotope record ultimately placing the SSB in the interval of separation between UAZ<sub>3</sub> and UAZ<sub>4</sub>. Only the association of <em>Nv. pingdingshanensis</em> with <em>Ic. crassatus</em> can be used to unambiguously characterize the base of the Spathian.</span></p>
ScienceDex guides
Understand access before you commit
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.