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242 results for “Spatial Dataset”
ChinaSoyArea10m: a dataset of soybean planting areas with a spatial resolution of 10 m across China from 2017 to 2021
<p>This dataset provides 10m-resolution maps of soybean planting areas in China during 2017-2021.</p><p>*** The data file is in ".tif" format</p><p>*** Temporal Resolution: Annually</p><p>*** Temporal coverage: 2017-2021</p><p>*** Pixel size: 10 m</p><p>*** Projection information: EPSG: 4326</p><p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Accompanying dataset for: "IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues"
<p>Mouse datasets were acquired using the manual IBEX multiplex imaging protocol and accompany the manuscript “IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues”, A. Radtke <em>et al.</em>, 2020, PNAS.</p> <p>All image data are stored using the <a href="https://imaris.oxinst.com/support/imaris-file-format">Imaris file format</a>. To view these multi-channel images, you can either use one of these free viewers, <a href="https://imaris.oxinst.com/imaris-viewer">Imaris viewer</a>, <a href="https://imagej.net/Fiji">Fiji</a>.</p> <p>Each experiment has an associated imaging meta-data file in xlsx format and the resulting image in Imaris format.</p> <p><strong>Mouse spleen (Manual)</strong></p> <p>Dataset is a 16 parameter IBEX experiment performed on a mouse spleen section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse thymus (Manual)</strong></p> <p>Dataset is a 26 parameter IBEX experiment performed on a mouse thymus section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse lung (Manual)</strong></p> <p>Dataset is a 23 parameter IBEX experiment performed on a mouse lung section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.379 µm), y (0.379 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse small intestine (Manual)</strong></p> <p>Dataset is a 20 parameter IBEX experiment performed on a mouse small intestine section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse liver (Manual)</strong></p> <p>Dataset is an 18 parameter IBEX experiment performed on a liver section from a LysM-tdtomato reporter mouse labeled with antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse naive lymph node (Manual)</strong></p> <p>Dataset is a 41 parameter IBEX experiment performed on a mouse lymph node section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse immunized lymph node (Manual)</strong></p> <p>Dataset is a 41 parameter IBEX experiment performed on a mouse lymph node section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p>
Spatial dataset on global forest loss and gain across decades from 1960 to 2019
<p>This spatial dataset was used for creating Fig. 1a in the following research article.</p> <p>Estoque RC, <span>Dasgupta R, </span><span>Winkler K,</span><span> Avitabile V, </span><span>Johnson BA, </span><span>Myint SW, </span><span>Gao Y, </span><span>Ooba M, </span><span>Murayama Y, </span>and <span>Lasco RD</span> (2022). Spatiotemporal pattern of global forest change over the past 60 years and the forest transition theory. <em>Environmental Resesearch Letters, </em><strong>17</strong>:084022. https://doi.org/10.1088/1748-9326/ac7df5</p>
Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"
<p>This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics. <br>It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content. <br>A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past. <br>Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis. </p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
Dataset for 'Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes'
<p>Dataset for the manuscript entitled: Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes</p> <p>includes:</p> <p>1) PSF from numerical simulations</p> <p>2) CSF measured in subjects</p> <p>3) Michelson contrast from numerical simulations</p>
Dataset of the determination of the topographic spatial resolution of a confocal point sensor with a type ASG material measure
<p>These original measurement data relate to the publication: J. Schaude, A. C. Gröschl, T. Hausotte: Effect of a Misidentified Centre of a Type ASG Material Measure on the Determined Topographic Spatial Resolution of an Optical Point Sensor, Metrology 2(1), p. 19-32, 2022, <a href="https://doi.org/10.3390/metrology2010002">https://doi.org/10.3390/metrology2010002</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p> <p>All data are in ASCII-format. Each file contains four columns, where column one to three are the <em>x</em>, <em>y</em>, and <em>z</em>-coordinates of the positioning system and column four is the signal of the photodetector.</p> <p><strong>Content of the folders</strong></p> <p>10_Plane: Axial probings on 18 points just outside the grooves.</p> <p>20_Edges: Lateral probings from each point just outside the grooves in the direction of the roughly determined centre of the material measure.</p> <p>30_PlaneArea: Repeated axial probing on a plane area near the material measure.</p> <p>40_RadialMeasurement: Radial measurement of the material measure by lateral (radial) probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), and radii (referring to the centre of the circle fitted to the edges determined in 20_Edges). Each radial probing has its own data file, with the name of the data file being “radial_probing {radius in m} {height in m} {data and time of probing}.txt”.</p> <p>50_LineMeasurement: Lateral probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), different lateral offsets (referring to the centre of the circle fitted to the edges determined in 20_Edges) and on two angles (on the groove (0°) and the adjacent top level (10°)). Please refer to sec. 5.2 of the aforementioned publication for a detailed description. Each lateral probing has its own data file, with the name of the file being “lateral probing {angle in °} {offset in m} {height in m} {data and time of probing}.txt”.</p> <p><strong>Acknowledgement</strong></p> <p>This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR); Funder ID: 10.13039/10001413</p>
Dataset - Spatially explicit linkages between redox potential cycles and soil moisture fluctuations
<p>This repository holds data collected during three lysimeter experiments where laboratory column scale lysimeters have been subjected to different wetting/drainage cycles. The lysimeters have been filled with forest soil in the Lausanne forest characterised by the SwissMEX project </p> <p>The following dataset contains soil redox potential and soil moisture measurements that have been continuously monitored in time for different depths. Pore water samples of specific dissolved chemicals have been collected at the end of each cycle. </p> <p> </p> <p>Specifically, this dataset is composed by the following files:</p> <ul> <li> "Lysimeter_configuration.png" illustrates the lysimeter used and the sensors scheme adopted.</li> <li>"METADATA.txt" contains specific information about each recorded variable and data point collected throughout the three experiments SM-B, SM-I1 and 2.</li> <li>Pore water analysis data</li> <li>Soil moisture and tension data</li> <li>Soil redox potential data</li> </ul> <p>We thank Pascal Froidevaux for providing the lysimeter used for SM-B experiment. The authors acknowledge key funding provided by the Swiss National Science Foundation through its grant number CRSII5 186422.</p> <p> </p> <p> </p>
A dataset of measured spatial room impulse responses in different rooms including visualization
<p>An open-source dataset of captured spatial room impulse responses (SRIRs) is presented. The<br> data was collected in different enclosed spaces at the Technische Universität Ilmenau using an open self-build<br> microphone array design following the spatial decomposition method (SDM) guidelines. The included rooms<br> were selected based on their distinctive acoustical properties resulting from their general build and furnishing as<br> required by their utility. Three different classes of spaces can be distinguished, including seminar rooms, offices,<br> and classrooms. For each considered space different source-receiver positions were recorded, including 360°<br> images for each condition. The dataset can be utilized for various augmented or virtual reality applications, using<br> either a loudspeaker or headphone-based reproduction alongside the appropriate head-related transfer function sets.<br> In future, we plan to add more rooms and more source-receiver positions.</p> <p>Please cite our corresponding paper:</p> <p>Klein, F., Surdu, T., Aretz, A., Birth, K., Edelmann, N., Seitelman, F., Ziener, C., Werner, S., and Sporer, T., “A dataset of measured spatial room impulse responses in different rooms including visualization,” in 152nd AES Convention, 2022, https://www.aes.org/e%E2%80%90lib/browse.cfm?elib=21728</p> <p> </p>
Dataset: A conflict between spatial selection and evidence accumulation in area LIP
<p>This dataset (packaged as the zip file cdots_share.zip) accompanies the article titled "A conflict between spatial selection and evidence accumulation in area LIP" by JA Seideman, TR Stanford, and E Salinas, in Nature Communications (2022).</p> <p>The experimental results in the paper are based on behavioral and single-neuron data collected from two subjects during performance of visuomotor tasks, as described in the article. The trial-by-trial data arrays (matrices inside .mat files) included here are the basis for most analyses reported in the article. </p> <p>In addition to the trial-wise data matrices, the dataset includes Matlab functions and scripts (*.m files) used to analyze the data and generate figures in the article. Instructions and specifics are detailed in the README file. </p>
Bibliometric and spatially georeferenced datasets of beach research in Mexico from 1993 to 2023
<p><span>These datasets resulted from a systematic review of published investigations on Mexican sandy beaches from 1993 to 2023. The literature search was performed on late December 2023 using three bibliographic repositories: Scopus, Web of Science and Redalyc. In the first dataset all records were standardized following the format of the Scopus database, including the following bibliographic metrics: authors, title, publication year, source, citations, authors' affiliations, and keywords. </span></p> <p><span>The second dataset includes the georeferenced beach location according to information provided in the articles listed in the first dataset. On this regard, the dataset includes the following fields: citation of the literature source; name of the beach as provided in the literature sources; latitude and longitude in grades, minutes and seconds using the geodetic datum WGS84; the research theme; and the research subtheme.</span></p>
Assessment of the spatial extent of local active noise control - dataset
<p>Experiment setup, data and supplementary scripts for the listening experiment "Assessment of the spatial extent of local active noise control" in Feb. 2023 at the Institute of Electronic Music and Acoustics in Graz, Austria</p> <p>A listening experiment has been conducted, trying to find the spatial extents of local active noise control (ANC). Participants were instructed to move their heads along the x-axis or rotate clock- and counterclockwise (yaw). A button had to be pressed if the perceived zone of comfort is left, and the current position was recorded. This task was repeated for different controlled bandwidths, signals and source directions. Additionally, scripts to generate the ANC filters, a brief statistical analysis and recordings at several positions for different stimuli/conditions are provided. Acoustic measures at the point of cancellation and at various other positions are further analyzed with and without ANC.</p>
Multi-Purpose Room Impulse Response Dataset Measured on a 3D Spatial Grid
<h1>Introduction</h1> <p>The sound field inside a room depends on many factors, such as the room shape, the absorption characteristics of the materials that comprise the bounding surfaces, the furniture present in the room, and the source position and its acoustic characteristics. An increasing number of publicly available room impulse response (RIR) databases that aim to provide detailed descriptions of interior sound fields can be found in the literature. These databases can be utilized in research as well as in the development and verification of signal processing algorithms that use this information on the acoustic environment. The availability of many RIR databases covering diverse scenarios is beneficial to the community.</p> <p>We provide a database of RIRs, namely the <strong>M</strong>ulti-<strong>P</strong>urpose <strong>RIR</strong> (<strong>MP-RIR</strong>) dataset, which contains 68736 RIRs measured on a dense 3D grid inside a complex-shaped room. We used a measurement robot with a rotating arm that operates as a linear guide and is capable of moving a vertical, linear array of eight omni-directional microphones. Four different sources have been used and were placed at eight different positions inside the room. A detailed desciption of the measurement campaign and the dataset is presented in the paper (https://aes2.org/publications/elibrary-page/?id=22515). </p> <h1>Contents of the MP-RIR dataset</h1> <p>In the following, the contents and the structure of the provided dataset are described:</p> <ul> <li>Sk_Mrir.npy:<br>Matrix, which contains the RIRs for all measured grid points for the loudspeaker Sk, k = 1, 2, ..., 8.<br>The matrix has the shape [N_xy, N_z, N] = [1074 x 8 x 100096], where N_xy is the number of 2D grid positions to which the robot is moving the vertical microphone array of N_z microphones. The length of each RIR is described by N.</li> <li>Mxyz.npy:<br>Matrix, which contains the microphone coordinates of the measured RIRs and corresponds to the matrices Sk_Mrir.<br>The matrix has the shape [N_xy, N_z, N_d] = [1074 x 8 x 3]. The indexing for the first two dimensions is the same as for the matrices Sk_Mrir, so that the microphone coordinates can be immediately retrieved for the provided RIRs. The third dimension with the length N_d gives access to the x-, y- and z-coordinate values in meters.</li> <li>Setup.npz<br>Dictionary, which contains parameters related to the measurement setup, with the following keys:<br> <ul> <li>angles_speaker<br>Dictionary of azimuth angles in degrees of the loudspeakers, with the keys S1, S2, ..., S8.</li> <li>coord_speaker_center<br>Dictionary, which contains the x-, y- and z-coordinates of the loudspeaker positions at the center of the base of each loudspeaker. The coordinate arrays can be accessed with the keys S1, S2, ..., S8.</li> <li>coord_polygon<br>Array of shape [4,2], which contains the x- and y-coordinates in meters of the room corners C_q, q=0,1,2,3.<br>The first dimension of the array relates to the room corners and the second dimension relates to the coordinates. </li> <li>fs<br>Sampling rate in Hz.</li> <li>T_guard<br>Guard time in samples. The guard time provides additional samples at the beginning of the RIR to increase the quality of the RIR.</li> <li>T_system<br>Delay of the measurement system in samples.</li> </ul> </li> </ul> <h1>Further Information</h1> <p>The delay of the RIRs is composed of the guard time T_guard, the system delay T_system and the acoustic delay T_ac. The guard time and system delay can be retrieved from the file Setup.npz described above.</p> <p>A gain alignment procedure was applied to align the output SPL between the loudspeakers, as described in the paper. Additionally, all RIRs were scaled by the same value, the maximum absolute peak of all measured RIRs. As a result, the maximum absolute value in each individual RIR is less or equal to 1.</p>
Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"
<p>Supplementary Datasets and Movies for the Paper <br><strong>Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax</p> <p>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a></p> <p> </p> <p><strong>Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022.</strong> Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details.</p> <p><strong>Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence.</strong> The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.</p> <p> </p> <p><strong>CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study:</strong></p> <p>Parkfield_2022_NLL-SSST-coherence_20231201A.csv<br>Parkfield_2022_NLL-SSST-coherence_20231201A.hyp</p> <p>AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv<br>AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp</p> <p>Anchorage_2018_NLL-SSST-coherence_20231125A.csv<br>Anchorage_2018_NLL-SSST-coherence_20231125A.hyp</p> <p> </p>
A Multi-Challenge Clustering Benchmark Dataset Embedding Large Differences in Spatial Extent
<p>This artificial clustering benchmark dataset was designed manually and draws its inspiration from structural aspects that can be seen in principal component plots of hyperspectral image data. Distance-separated, density-separated, gradient-separated as well as connected clusters have been placed into the dataset. Following the notion that clusters may vary significantly with respect to their spatial extent the respective separability problems are scaled at different levels and only become visible by magnifying certain parts of the dataset. Another special aspect of this dataset is that cluster borders have been kept rather ambiguous which, in our opinion, better resembles the situation in spectroscopic data.</p>
Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset
<p>This dataset contains supplementary files uploaded as part of the above journal article.</p> <p>5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few<br> records contain negative peak delay times due to change in waveform shape from filtering, these<br> were excluded during further calculations. The other 4 sub-datasets contain files as well as the script<br> to generate the results of Δlog <em>t<sub>p</sub></em> , κ, ε<sub>param</sub> and P(<em>m<sub>l </sub></em>) as shown in figures 7, 8, 9 and 10 respectively<br> of the main article.</p> <p>Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (<em>t<sub>p</sub> </em>) in 2-4 Hz, 4-8 Hz, 8-16 Hz<br> and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent<br> origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude,<br> event longitude, event depth, station code, station latitude, station longitude, <em>t<sub>p</sub></em> in 2-4 Hz, <em>t<sub>p</sub></em> in 4-<br> 8 Hz, <em>t<sub>p</sub></em> in 8-16 Hz and <em>t<sub>p</sub></em> in 16-32 Hz in a sequential manner.</p> <p><br> Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and<br> nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt)<br> written in BASH. The text files contain Δlog <em>t<sub>p</sub></em> values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands<br> respectively. The columns in the text files represent node index, node latitude, node longitude,<br> node depth and Δlog <em>t<sub>p</sub></em> value in a sequential manner. The GMT script uses GSHHG coastline<br> data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ .<br> Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of<br> the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5<br> (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt,<br> kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The<br> text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively.<br> The columns in the text files represent node latitude, node longitude and κ value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of κ using GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt,<br> aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain<br> ε<sub>param</sub> values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in<br> the text files represent node latitude, node longitude and ε<sub>param</sub> value of the node sequentially.<br> This GMT script also uses GSHHG coastline data whose path can be added to the script, same as<br> in data set S2 case. The GMT script file can be run to see the spatial distribution of ε<sub>param</sub> using<br> GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt,<br> psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain<br> psdf (P(<em>m<sub>l</sub></em><sub> </sub>)) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The<br> columns in the text files represent node latitude, node longitude and P(<em>m<sub>l</sub></em> ) value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of P(<em>m</em><sub><em>l </em></sub>) using GMT-5 (Wessel et al., 2013) and above.</p>
Spatial Feature Engineering Dataset for Forest Aboveground Biomass Estimation Using Landsat Imagery
<p><strong>Study Area:</strong><br>The dataset covers forested regions in Oregon, Washington, Idaho, and eastern Montana, characterized by diverse climatic conditions due to orographic effects. The forests in the Coast Range and western slopes of the Cascades, with high precipitation (800-3000 mm annually), contrast with the drier forests in Idaho and Montana, which receive over 400 mm annually. The dataset includes highly productive Douglas-fir and western hemlock forests, with aboveground biomass (AGB) densities exceeding 1200 Mg ha⁻¹, as well as fire-adapted lodgepole and ponderosa pine forests in the rainshadow regions.</p> <p><strong>LiDAR AGB Estimates:</strong><br>The dataset includes 176 lidar-derived AGB maps from 2002 to 2016, covering various regions in Oregon, Washington, Idaho, and Montana. A Random Forest (RF) model was used to estimate AGB at a 30m² resolution, utilizing lidar height features, DEM features, and climate data. Non-forested areas and buildings were masked using binary forest cover maps from the LCMS dataset and the Microsoft Building Footprints dataset.</p> <p><strong>Reference Dataset:</strong><br>A composited AGB map, derived from the 176 lidar maps, was created to develop Landsat-based AGB models, covering 9,361,622 ha of forested land. The AGB layer was stratified into 30 bins, and training, development, and testing sets were constructed for model validation. The dataset includes 7500 test samples and 300,000 training and development samples, with a 500m buffer around test set locations to prevent spatial autocorrelation.</p> <p><strong>Landsat Satellite Imagery:</strong><br>Landsat imagery from 1990 to 2022 was utilized, with three time series derived: all scenes, scenes from May to November, and annual medoid composites. The imagery was processed using the Google Earth Engine (GEE) platform, focusing on periods of maximum phenological activity.</p> <p><strong>Feature Engineering:</strong><br>Extensive feature engineering was performed, generating spectral, spatial, temporal, and topographic features from Landsat imagery and DEM data. Features were extracted over the reference AGB map's domain, synchronized with the lidar acquisition dates.</p> <ul> <li><strong>LandTrendr Fitted Imagery:</strong> Spectral features were derived from LandTrendr-fitted imagery, smoothing variations in the time series.</li> <li><strong>LandTrendr Disturbance and Recovery Features:</strong> Temporal features were derived from LandTrendr models, characterizing disturbance and recovery events.</li> <li><strong>CCDC Disturbance and Recovery Features:</strong> CCDC algorithm-derived features characterized disturbances and recovery using harmonic models.</li> <li><strong>Buffer Features:</strong> Local variations were captured using buffer statistics around each pixel.</li> <li><strong>GLCM Features:</strong> GLCM texture features summarized the joint distribution of gray-tone values.</li> <li><strong>Edge Detectors:</strong> Various edge detection operators captured spatial derivatives and edges.</li> <li><strong>Morphological Operations:</strong> Morphological features were derived using multi-channel image processing techniques.</li> <li><strong>Neighborhood Vectorization:</strong> Direct vectorization of satellite measurements in pixel neighborhoods.</li> <li><strong>Neighborhood Similarity:</strong> Similarity features characterized the relationship between pixel neighborhoods and their centroids.</li> <li><strong>Topographic Features:</strong> Topography was characterized using elevation, slope, aspect embeddings, and hillshade layers from the NED DEM.</li> </ul> <p>This comprehensive dataset enables robust analysis of AGB models and their performance across diverse forested landscapes in the Pacific Northwest</p>
Four spatial prediction datasets of susceptibility to gully erosion, comparing machine learning models, in the Piraí drainage basin, southeastern Brazil
<p>The data in this repository refer to the article published in the journal Land, entitled: Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba do Sul Middle Valley, Southeast Brazil.</p>
Spatial Room Impulse Response Dataset: A Robot's Journey Through Coupled Rooms of a Reverberant University Building
<p>This is a dataset of Spatial Room Impulse Responses obtained by a robot equipped with a microphone array.</p> <p>The measurements were conducted in a reverberant university building, the <em>Helmholtz</em> building at<em> Technische Universität Ilmenau</em> (coordinates: N50.6815788133375°, E10.939294371903342°). All the floors in the building are covered with bare stone tiles, the walls are not acoustically treated. Only the hallway has a suspended acoustic ceiling. The file "Pictures Overview.jpg" shows some impressions of the building. Note that the floorplan only shows parts of the building that were connected to the measurement area by open doors.</p> <p>The area covered by the robot is in a hallway on the top floor (2nd floor starting with ground floor) with two stairwells at both ends. To specifically study the behavior of coupled rooms and occluded sources, the sound sources were placed in adjacent sections of the building and on multiple floors. See the file "Measurement Overview.jpg" for an overview of the source positions and the receiver areas covered. Areas 2 and 3 were captured with a higher spatial resolution than area 1 to analyze the transition between the hallway and the staircases. The receiver positions form a uniform grid, the pitch between positions is shown in the following table. Due to time and technical constraints, only a maximum of 3 sources were used per run, so there are not all combinations of sources and receiver areas. Refer to the following table to see which source was active for which area and which zip file contains the according data:</p> <table> <tbody> <tr> <th>Filename</th> <th>Sources</th> <th>Receiver Area</th> <th>Receiver Positions [ct]</th> <th>Pitch [cm]</th> </tr> </tbody> <tbody> <tr> <td>Helmholtzbau_OG2_HM_HS.zip</td> <td>HM, HS</td> <td>Area 1</td> <td>143</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SML_SSL_SSU.zip</td> <td>SML, SSL, SSU</td> <td>Area 1</td> <td>154</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SMU_SML_HM.zip</td> <td>SMU, SML, HM</td> <td>Area 2</td> <td>88</td> <td>25</td> </tr> <tr> <td>Helmholtzbau_OG2_SSU_SSL_HS.zip</td> <td>SSU, SSL, HS</td> <td>Area 3</td> <td>92</td> <td>25</td> </tr> </tbody> </table> <p>As an example "Plot Reverberation Time.jpg" shows the reverberation times for all measured positions of Area 1 and 2 with speaker HM.</p>
Dataset for: 'Patterns in the Plankton – Spatial distribution and long-term variability of copepods on the Agulhas Bank'
<p>This dataset contains environmental data (in situ temperature and chlorophyll <em>a</em>) and integrated biomass (mg C m<sup>-2</sup>) data for a number of copepod taxa, as well as total copepod biomass and abundance, on the Agulhas Bank, South Africa, as predicted by a Generalized Additive Model (GAM), during late austral spring (October-December) from 1988 to 2011. Mean environmental and copepod biomass parameters for each area and year are also provided. Relevant information on sampling and statistical analysis of spatial distributions has been extracted from the paper. Please see paper for full details and figures, including supplementary data; <a href="https://doi.org/10.1016/j.dsr2.2023.105265">https://doi.org/10.1016/j.dsr2.2023.105265</a>. Please see the Word document Huggett_et_al_2023_README.docx for a list of the data files and descriptions of the contents.</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.