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5,155 results for “resolution”
Horizontal-plane HRTFs of KEMAR mannequin spatially interpolated with a resolution of 0.5°
<p>HRTFs of the KEMAR manikin (Gardner and Martin, 1995):</p> <ul> <li>"hrtf_M_normal pinna.sofa": the original HRTF dataset from the KEMAR manikin at a lateral resolution of 5° stored as a SOFA file (Majdak et al., 2013)</li> <li>"hrtf_M_normal pinna resolution 0.5 deg.sofa": A super-resolution HRTF set with a directional up-sampling of the original HRTF set to the lateral resolution of 0.5°. To this end, for each ear's HRTF set: <ul> <li>The broadband timing was removed by replacing the HRTF's phase spectrum by the minimum-phase spectrum (Oppenheim et al. 1999) corresponding to HRTF's amplitude spectrum.</li> <li>For the interpolation of the amplitude spectra, the complex spectra of the minimum-phase HRTFs for two adjacent available directions were averaged according to a weighting that corresponded to the interpolated target direction. </li> <li>For the interpolation of the timing, a continuous-direction model of the time-of-arrival (TOA) was applied (Ziegelwanger & Majdak, 2014). TOA is the broadband delay arising from the propagation paths from the sound source to the listener's ear. For a given direction of a sound, the interaural difference of TOAs corresponds to the ITD. The TOA model parameters describe listener's geometry (head and ears) and configure a continuous-direction function of broadband TOA. We used this function to calculate TOAs for directions in steps of 0.5°. To this end, for each ear, the model was fit to an HRTF set as described by Majdak & Ziegelwanger (2013) using the implementation from the Auditory Modeling Toolbox (Søndergaard & Majdak, 2013). Then, each minimum-phase HRTF was temporally up-sampled by a factor of 64, circularly shifted by the TOA obtained from the continuous-direction TOA model for the target direction, and then down-sampled to the sampling rate of 44.1 kHz. Note that the temporal oversampling was required to achieve an interaural resolution of 0.35 µs. </li> </ul> </li> </ul> <p> </p>
Scripts developed to modelling the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery.
<p>The zipfile contains the scripts developed in the paper on the modelling of the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery, as published in Remote Sensing Applications: Society and Environment journal (RSASE - Elsevier). </p> <ul> <li>Scripts developed for the extraction of light sources from night orthophotography ; [SAFE Software Inc. (2017). FME Desktop Esri Edition.Version 2017.1.]</li> <li>ModelBuilder developed for the visibility modelling of light sources ; [ESRI (2017). ArcGIS Desktop Pro.Version 2.1.]</li> </ul>
Assessing the peatland hummock-hollow classification framework using high-resolution elevation models: Implications for appropriate complexity ecosystem modelling
<p>The hummock-hollow classification framework used to categorize peatland ecosystem microtopography is pervasive throughout peatland experimental designs and current peatland ecosystem modelling approaches. However, identifying what constitutes a representative hummock-hollow pair within a site and characterizing hummock-hollow variability within or between peatlands remains largely unassessed. Using structure-from-motion (SfM), high resolution digital elevation models (DEM) of hummock-hollow microtopography were used to: 1) examine how much area needs to be sampled to characterize site-level microtopographic variation; and 2) examine the potential role of microtopographic shape/structure on biogeochemical fluxes using data from 9 northern peatlands. This data set is comprised of plot DEMs, supporting data, and the script used to analyze data and produce figures presented in the manuscript submitted to Biogeosciences Discussion "ASSESSING THE PEATLAND HUMMOCK-HOLLOW CLASSIFICATION FRAMEWORK USING HIGH-RESOLUTION ELEVATION MODELS: IMPLICATIONS FOR APPROPRIATE COMPLEXITY ECOSYSTEM MODELLING".</p>
A high-resolution speleothem record of Marine Isotope Stage 11 as a natural analog to Holocene Asian summer monsoon variations
<p>A full-spectrum characterization of past interglacial climate is a necessary prerequisite for the detection and attribution of climate changes during the current interglacial. Here we present a speleothem record of Asian summer monsoon (ASM) during Marine Isotope Stage (MIS) 11 interglacial (MIS 11c), from Yongxing cave, China. The record’s unprecedented chronologic constraints and decadal-scale temporal resolution allow a precise and direct comparison of ASM between the MIS 11c and the Holocene. Our data suggest that orbital–centennial patterns of ASM were remarkably similar during both interglacial, including their pacing and structure. Notably, a multi-millennial stronger monsoon late in MIS 11c, the ‘Late-MIS 11c shift’, is similar to the Late Holocene strengthening of the ASM, the ‘2-kyr shift’. Thus the multi-centennial ASM weakening at the end of the ‘Late-MIS 11c shift’ could imply that the current century-long ASM waning trend may persist into the future, if only natural forcings are considered.</p>
Crystal structure of olive flounder [Paralichthys olivaceus] interferon gamma at 2.3 Angstrom resolution - diffraction data
<p>Diffraction images, Bessy II (Berlin), MX 14.1, 12.5.2017, PDB ID 6F1E</p>
Resolution limit of data-driven coarse-grained models spanning chemical space
<p>This repository contains all databases referenced in the supporting information of the paper titled "Resolution limit of data-driven coarse-grained models spanning chemical space" by Kiran H. Kanekal and Tristan Bereau.</p>
In silico prediction of high-resolution Hi-C interaction matrices (part III)
<p>The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line.</p> <p>This is part III of the dataset which contains Nhek.tgz and Data.tgz.</p>
In silico prediction of high-resolution Hi-C interaction matrices (part I)
<p>The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line.</p> <p>This is part I of the dataset which contains Gm12878.tgz and Hmec.tgz.</p>
In silico prediction of high-resolution Hi-C interaction matrices (part II)
<p>The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line.</p> <p>This is part II of the dataset which contains K562.tgz and Huvec.tgz.</p>
High-resolution ptychotomography dataset of nanoporous glass
<p>This repository contains nanoporous glass tomography dataset, acquired at the cSAXS beamline at the Swiss Light Source at the Paul Scherrer Institute in Villigen (Switzerland). Details about the data acquisition can be found in Ref. [1].</p> <p><strong>Citation and acknowledgements</strong></p> <p>For use of the ptychographic X-ray computed tomography dataset on nanoporous glass:</p> <p><em>[1] M. Holler, A. Diaz, M. Guizar-Sicairos, P. Karvinen, E. Färm, E. Härkönen, M. Ritala, A. Menzel, J. Raabe and O. Bunk. Scientific Reports, 4, 3857 (2014).</em></p> <p>Alignment and tomographic reconstruction provided in "<em>aligned_phase_sinogram" and "tomogram_delta" </em> were performed by a method described in</p> <p><em>[2] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</em></p> <p><strong>Dataset description</strong></p> <p>The provided dataset is saved in Matlab MAT v7.3 format.</p> <p><em>stack_object</em> - complex valued unaligned projections with dimensions [Npix_vertical, Npix_horizontal, number_of_angles]</p> <p><em>rotation_angle</em> - vector of corresponding projection angles in degrees</p> <p><em>lambda</em> - wavelength of the illumination beam [m]</p> <p><em>pixel_size</em> - size of the reconstruction pixel [m]</p> <p><em>probe_size</em> - size of the reconstruction probe in pixels</p> <p><em>aligned_phase_sinogram</em> - aligned and unwrapped phase projections</p> <p><em>tomogram_delta</em> - reconstructed tomogram of real part of refractive index delta, n = 1-delta-i*beta</p>
High-resolution Canopy Height Model of Hawaii Island 2018-2020
<p>Forest canopy height model for Hawaii Island using lairborne lidar data collected by NOAA in 2018, 2019 and 2020. The maps are produced by year at the resolution of 1 m. The raw point cloud data had am average point cloud density of 8 pulses per squre m. https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/9635/index.html</p> <p>ALS 2018 data was reprocessed using Lastools software to reclassify ground class (2)</p> <p>ALS 2019_20 was also reprocessed using Lastools software to reclassify unclassified (1) points to vegetation (5)</p> <p>The CHM’s generation procedure is composed by four steps. It starts by the creation of 500m x 500m tiles using a 50m buffer, resorting to the lastile function, followed by the lasheight function that is used to compute the elevation of each point above the ground. Then, the lastile function is used again to remove the buffer from the normalised point clouds. These first three steps resort to the LASTools software. The fourth, and final step, consists in the generation of the CHM with a 1 m resolution resorting to the pit-free algorithm implemented in the rasterize_canopy function from the lidR package.</p> <p>The file is a GeoTIFF with LZW compression in ArcGIS pro 3.3 </p> <p>EPSG:6635</p> <p>Use of these data requires citation of this dataset </p>
Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm
<p>The 41-year (1982-2022) daily DNI dataset (CHDNI) reconstructed in this study has been uploaded, and stored in netcdf format. The one-year dataset comprises daily DNI estimates for either 365 or 366 days, with individual data files separately organized by year. Each daily file is stored in mat format and labeled as "pred_xxxxxyymm," where ‘xxxx' denotes the year, ‘yy' represents the month, and ‘mm' stands for the day. The geographical scope of CHDNI dataset spans from 3°N to 54°N in latitude and from 72°E to 136°E in longitude. The mat matrix, encapsulating the data, is configured with dimensions of 361 rows and 641 columns, measured in W/m2.</p> <p>If you want to use the CHDNI dataset for related scientific research, please contact us (Email: WHU_wjy@whu.edu.cn).</p> <ul> <li>Wu J, Niu J, Qi Q, Gueymard CA, Wang L, Qin W, et al. Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm. Renewable and Sustainable Energy Reviews 2024; 204: 114805.</li> </ul>
Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features
<p>This dataset supports the study titled <em>"Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features"</em>, published in <em>Urban Climate</em> (<a href="https://doi.org/10.1016/j.uclim.2024.102102" target="_new" rel="noopener">DOI: 10.1016/j.uclim.2024.102102</a>).</p> <p> </p> <p><strong>Content Overview:</strong></p> <ul> <li> <p><strong>Building Label Data for Footprint Detection</strong>:</p> <ul> <li><em>Amsterdam_BDG_Label.rar</em></li> <li><em>MiamiDade_BDG_Label.rar</em></li> </ul> <p>These are the label datasets used for training the building detection segmentation models. They have been instrumental in accurately detecting building footprints in Amsterdam.</p> </li> <li> <p><strong>Amsterdam_3D_Buildings.rar</strong>: CityGML file of 3D building models for Amsterdam, derived from LiDAR data and U-Net3+ model.</p> </li> </ul> <ul> <li> <p><strong>Morphological Features.rar</strong>: Contains urban morphological features (in raster format) extracted from LiDAR data used in the study.</p> </li> <li> <p><strong>Training and Test Data for Air Temperature Estimation</strong>:</p> <ul> <li><em>Train_Test_AvgTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MaxTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MinTemp_Amsterdam.rar</em></li> </ul> <p>This dataset includes training and testing data for estimating air temperatures in three scenarios: average daily temperature, minimum daily temperature, and maximum daily temperature for the city of Amsterdam.</p> </li> </ul>
Datasets supporting the paper 'City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal.'
<p>###########################################################<br>Datasets supporting the publication:<br><strong>Watson, C.S., Gyawali, J., Creed, M., and Elliott, J.R. City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal. Geocarto International. DOI: <a href="https://doi.org/10.1080/10106049.2024.2387073">https://doi.org/10.1080/10106049.2024.2387073</a><br></strong></p> <p><strong>-Please refer to the publication for details on the production of each dataset.</strong><br>-<strong>Please cite the publication and this dataset repository when using the data.</strong><br>###########################################################</p> <p><strong>Contents:</strong></p> <p><strong>Stream centrelines:</strong></p> <p>streams_fabdem.gpkg</p> <p>streams_GLO30.gpkg</p> <p>streams_kh9_1974.gpkg</p> <p>streams_merit.gpkg</p> <p>streams_merit_hydro.gpkg</p> <p>streams_pleiades.gpkg</p> <p>streams_reference.gpkg</p> <p><strong>Flood maps:<br></strong></p> <p>fastflood_1in100year_flood_depth_metres.tif</p> <p>flood_depth_difference__fastflood_Shrestha_et_al_2023_metres.gpkg</p> <p>Height_above_channel_metres.tif</p> <p><strong>1974 Orthoimage:</strong></p> <p>KH9_1974_orthoimage_DZB1209_500101L007001_DZB1209_500101L008001.tif</p>
Multi-year high time resolution measurements of fine PM at 13 sites of the French Operational Network (CARA program)
<p>These datasets correspond to long-term measurements of atmospheric aerosol components from Aerosol Chemical Speciation Monitor (ACSM) and multi-wavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA program. </p> <p>The datasets contain the mass concentrations of major chemical species within PM1, namely organic aerosols (OA), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl-), and equivalent black carbon (eBC). </p> <p>Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both the guidance from the French reference laboratory for air quality monitoring and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) standard operating procedures.</p> <p>These data are discussed in an article in submission, please cite it when using the data.</p>
FIGURE 2 in DNA extracted from museum specimens of the 19 century provides a taxonomic resolution on the identity of the characid fish Psalidodon jequitinhonhae (Ostariophysi: Characiformes)
FIGURE 2 | Lectotype of Tetragonopterus rutilus jequitinhonhae, NMW 57760:2, 67.52 mm SL.
Temperature, precipitation and drought code at 1 km resolution over Italy from start of 2017 to end of 2022 (6 years)
<p>These files are products from an investigation related to understanding seasonal vertical ground movement through InSAR and how it is correlated to drought and temperature. </p> <p><strong>All the raster stacks have one band per day from 1 January 2017 to 1 January 2023 for a total of 2192 bands</strong>. All raster are in geographic coordinate systems with a WGS84 datum and aligned to an extent of 6.6116°, 18.51428°, 35.48345°, 47.09867° with a cell ground sampling distance of 0.008983153° degrees.</p> <p> (i) PrecipitationCHIRP.tif - daily precipitation in Italy, from the CHIRP dataset.</p> <p> (ii) correctedMODIS_LST_2018_2024.tif - daily air temperature interpolated values from MODIS corrected from LST to air temperature using a simple correction factor (see https://www.mdpi.com/2072-4292/16/22/4123) </p> <p>(iii) droughtCodeMODIS_CHIRP_2018_2024.tif - calculated drought code (see paper).</p> <p>(iv) italyEGMS_DCcor.gpkg - all Measurement Points with absolute value of Spearman correlation above 0.7 for Italy.</p> <p>An interactive webgis is available at <a href="https://www.cirgeo.unipd.it/appVM/EGMS/" target="_blank" rel="noopener">https://www.cirgeo.unipd.it/appVM/EGMS/</a> (not always active, please email authors if not accessible). This portal allows the user to click each measurment point that was detected as having significant correlation with drought codes, and see the time-series of the vertical movement and of the detrended drought code and temperature and precipitation (see preview image).</p>
Preliminary data and analysis from ultrafast calcium or voltage imaging recordings at 8-bits resolution using a Kinetix camera
<p><span>This dataset was obtained from brain slices of the mouse. Data are from transversal hippocampal slices from </span><span>30-40 postnatal days old C57Bl6 mice (of both genders), stained with the Ca<sup>2+</sup> indicator Fluo-4 AM; or from layer-5 pyramidal neurons loaded intracellularly either with the Ca<sup>2+</sup> indicator Oregon Green BAPTA-5N or with the voltage sensitive dye JPW1114. <span> </span>Details are in the Read_me file. This dataset cannot be used for publications without permission of the contact person.</span></p>
Five-minute resolution electricity data from households on the NSW South Coast
<p>These data were collected from 37 households in the Eurobodalla region on the South Coast of NSW, Australia. Data was recorded using Wattwatcher devices that monitored the total electricity consumed by households, as well as collecting data from dedicated circuits - connected to rooftop solar systems, water heaters, and air-conditioners - if these were available.</p>
Numerical data and higher resolution images for Abdulkareem et al (2024), Thermal stabilisation of lysozyme through ensilication, Molecules
<p>Numerical data and higher resolution images for Abdulkareem et al (2024), Thermal stabilisation of lysozyme through ensilication, Molecules</p> <p><em><strong>Please cite our publication in any use of this data</strong></em></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.