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2,113 results for “Very High Resolution”

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zenodo44/100

High resolution spectra of the spinning-top Be star Achernar

Achernar, the closest and brightest classical Be star, presents rotational flattening, gravity darkening, occasional emission lines due to a gaseous disk, and an extended polar wind. It is also a member of a close binary system with an early A-type dwarf companion. We aim to determine the orbital parameters of the Achernar system and to estimate the physical properties of the components. We monitored the relative position of Achernar B using a broad range of high angular resolution instruments of the VLT/VLTI over a period of 13 years (2006-2019). These astrometric observations are complemented with a series of more than 700 optical spectra for the period from 2003 to 2016. The present dataset contains the high resolution spectra of Achernar that were included in our study. They were collected using the BESO, BeSS, CHIRON, CORALIE, FEROS, HARPS, PUCHEROS, and UVES instruments. The spectra are provided in the form of standard FITS files, with the continuum flux normalized to unity.

opencc-by-4.0Sep 2022View details →

zenodo44/100

Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data

The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book. The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort. The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes. Contributions Notebook <ul> <li> Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a> </li> <li> Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> </ul> Dataset originator/creator <ul> <li> Met Office Informatics Lab (creator) </li> <li> Microsoft (support) </li> <li> European Regional Development Fund (support) </li> </ul> Dataset authors <ul> <li> Met Office </li> </ul> Dataset documentation <ul> <li> Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>. </li> </ul> Note this data should be used only for non-commercial purposes.

opencc-by-4.0Feb 2022View details →

zenodo44/100

High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)

A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives.  Note that due to the underlying ship activity data and the geographic boundaries, the time index in the Datetime column in the ship_emissions_YYYYMMDD.csv files is not equidistant. For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area. The underlying model source code is available on Github, with a release of the associated version on Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6951672.svg)](https://doi.org/10.5281/zenodo.6951672)

opencc-by-4.0Jul 2022View details →

zenodo44/100

Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset

This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print: Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a>   The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a>   .   The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used during this study, please refer to the methods section of the paper. Description of the files. 202007_to_202107_nocs - contains the data from the low-cost sensors It contains the following headers: - "sensor" - sensor id - "site" - name of the air quality monitor hosting the sensor - "median_PM1" - PM1 mass concentration (ug/m3) - "median_PM10" - PM10 mass concentration (ug/m3) - "median_PM25" - PM25 mass concentration (ug/m3) - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30) - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um - "median_gr100um" - particle number concentration (PMS5003) of particles >10um - "median_gr10um" - particle number concentration (PMS5003) of particles >1um - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um - "median_gr50um" - particle number concentration (PMS5003) of particles >5um - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003 - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003 - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003 - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"      df_pm_2min - contains the PM mass concentration data from the Fidas 200S. It contains the following headers: - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"     df_weather_2min - contains the weather data from the Fidas 200S It contains the following headers: - "rh" - relative humidity (%) - "dew_point_temperature" -  dew point temperature (Celsius) - "air_pressure" - Air pressure (hPa) - "temperature" - temperature (Celsius) - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"

opencc-by-4.0Oct 2022View details →

zenodo44/100

High-resolution topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)

Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs. Please note for all DTMs Format: GeoTiff Projection: Equirectangular Datum: Mars 2000 Sphere Bit depth: 32bit Spatial resolution: 1m/pixel HiRise images avalable at:https://hirise.lpl.arizona.edu/ (use one of the image numbers listed below in the search box) Stereo pairs: HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif) PSP_002590_1765 PSP_002946_1765 HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif) PSP_006915_1760 PSP_007060_1760 HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif) ESP_016646_1760 ESP_015934_1760 HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif) ESP_020470_1755 ESP_014378_1755 HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif) PSP_002379_1755 PSP_002023_1755 HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif) ESP_016567_1755 ESP_017279_1755 HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif) PSP_003790_1755 PSP_004291_1755 HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif) ESP_016145_1775 ESP_017424_1775 HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif) PSP_008708_1780 PSP_008998_1780 HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif) ESP_019613_1760 ESP_011688_1760

opencc-by-4.0Jul 2017View details →

zenodo44/100

High-resolution digital topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)

opencc-by-4.0Sep 2017View details →

zenodo44/100

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

<h2>Description (V2 - Latest):</h2> To enable easy integration in the workflows, we have provided the main datasets in the following formats:   <ul> <li>Vector dataset:<code> Folder - Vector</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>Geopackage (.gpkg)</code> file (Results_Vis.gpkg) with polygon geometries at 1/8-degree spatial resolution in an EPSG:4326 coordinate system. The attribute table of this file contains FN_ID column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul>   <ul> <li>Raster datasets: <code> Folder - Raster</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>geotiff (.tif)</code> files with LZW compression in an EPSG:4326 coordinate system. The assessed gross rooftop area datasets are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units.</li> </ul>   <ul> <li>Numerical dataset: <code> Folder - Numerical</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>parquet (.parquet)</code> file (Results.parquet). This file contains FN_ID column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul>   In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <code> Folder - Models</code> <ul> <li>M2_Model.json: This file contains the frozen parameters of the M2 model in <code>.json</code> format generated from <code>XGBoost version 2.0.3</code></li> <li>SSP_drivers.parquet: This file contains the driver data used for generating the main dataset in our study</li> <li>FN_MAP.parquet: This file contains the boundary information for each fishnet grid tile in a Well Known Text (WKT) format.</li> <li>Prediction.ipynb: This file provides a python notebook interface to generate inferencing from <code>M2_Model.json</code> using <code>SSP_drivers.parquet</code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code>FN_MAP.parquet</code><code> </code>file.</li> <li>environment.yaml: This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul>   <h2>Version history:</h2> This version corresponds to the revised journal submission (Round 1). The version will be updated upon the completion of the review of the main manuscript. <ul> <li>This version V2 is supersedes V1 to correspond with round 1 of review.</li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 ", submitted to Scientific Reports Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul>   <h2>Changelog:</h2> The following files from version V1 of this dataset are now archived based on the reviews (Round 1). <ol> <li> <blockquote>1_Geospatial_Dataset_V1.gpkg</blockquote> </li> <li> <blockquote>2_Countrylevel_gross_rooftop_area_V1.parquet</blockquote> </li> <li> <blockquote>3_Analytics_Scripts_V1.ipynb</blockquote> </li> </ol>

opencc-by-4.0Sep 2023View details →

zenodo44/100

High-resolution basin-wide correlations with dynamic time-warping: code and data for a case study from the Usseln Limestone (Late Devonian, Rhenish Massif, Germany)

This dataset accompanies the manuscript of Wichern et al. (GRL, 2024), entitled "Decoding Deep-Time Rhythms: Probing the limit of Stratigraphic Correlation in the Usseln Limestone's (Late Devonian) Time-Specific Facies". It contains both datasets and code.  The dataset concerns samples collected from the Usseln Limestone, a rock unit that underlies the Late Devonian Kellwasser Crisis deposits in the Rhenish Massif, western Germany. The data consists of high-resolution thin-section composite photos, as well as micro-XRF scanning data (both maps and depth records) for three localities. The code contains the workflow to structure and plot the micro-XRF maps and convert them to depth records, as well as the workflow to analyse these depth records using dynamic time warping. Further analytical details can be found in the supporting material of the associated manuscript.

opencc-by-4.0Apr 2024View details →

zenodo44/100

A global synthesis of high-resolution stable isotope data from benthic foraminifera of the last deglaciation

In paleoceanography, carbon and oxygen stable isotope ratios from benthic foraminifera are used as tracers of physical and biogeochemical properties of the deep ocean. We present the first version of the Ocean Carbon Cycling working group database,  of stable isotope ratios of oxygen and carbon from benthic foraminifera from deep ocean sediment cores from the Last Glacial Maximum (LGM, 23-20 ky before present (BP)) to the Holocene (<10 ky BP) with a particular focus on the early last deglaciation (20-15 ky BP). It includes 287 globally distributed coring sites, with metadata, isotopic and chronostratigraphic information, and age models. A quality check was performed for all data and age models. Sites with at least millennial resolution were preferred, because the main goal is to resolve ocean changes associated with the last deglaciation on at least millennial timescales. Software tools were produced to access and analyze the data, and are included with this publication. Deep water mass structure as well as differences between the early deglaciation and LGM are captured by the data in the compilation, even though its coverage is still sparse in many ocean regions. We find high correlations among time series calculated with different age models at sites that allow such analysis. The database provides a useful dynamical approach to map physical and biogeochemical changes of the ocean throughout the last deglaciation.

opencc-by-4.0Oct 2022View details →

zenodo44/100

High resolution microsection images for: Common juniper, the oldest living non-clonal woody species across the tundra biome and the European continent

Two high resolution images of the stem section are available as .czi files. These images are from a living Juniperus communis L. branch from Abisko (Sweden) sampled in August 2021. These high-resolution photographs (2.89 pixel/μm) were created using Axio Scan 7, Zeiss, Germany.  One high resolution image of the same stem section is archived as a .tif file (49835x25587 pixels). This image is a composition of the two .czi images created using Axio Scan 7, Zeiss, with a reduced resolution and edited adding the ring-count reference points and the reference scale.

opencc-by-4.0Feb 2024View details →

zenodo44/100

Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model

This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.   This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.

opencc-by-4.0May 2024View details →

zenodo44/100

High Resolution 30m Land Surface Parameters for Europe

General Description The High Resolution 30m Land Surface Parameters for Europe  dataset is derived from <a href="../records/7676373">Global Ensemble DTM</a>. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Below is the list of land-surface parameters. Hillshade and Slope in degree are available to download through this resporitory, others are available to access from public S3 server. All files are in COG. slope in degree (slope): steepness at each cell <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif" target="_blank" rel="noopener ugc nofollow">https://s3.eu-central-1.wasabisys.com/arco/slope_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> hillshade: visualizing of terrain determined by a light source and the slope and aspect of the elevation surface <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> minimum curvature (minic): valleys in negative value and local convex landform in positive value <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/minic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> maximum curvature (maxic): ridges in positive values and local concave landform in negative value <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/maxic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> positive openness (pos.openness):  the "dominance" of an elevated location over its surroundings <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/pos.openness_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> negative openness (neg.openness): the "enclosure" of a lower location by elevated surroundings <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/neg</a><a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">.openness</a><a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> Data Details <ul> <li>Time period: January 2000 – December 2022</li> <li>Type of data: Land surface parameters of geomorphometry</li> <li>How the data was collected or derived: Derived from <a href="../records/7676373">Global Ensemble DTM</a> in 30m using GRASS GIS and SAGA GIS running in a local HPC.</li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900000 899000 7401000 5501000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216700, 153400</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">https://github.com/AI4SoilHealth/SoilHealthDataCube/issues</a> Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: <ol> <li>generic variable name: slope = slope in degree</li> <li>variable procedure combination: edtm = Ensemble digital terrain model</li> <li>Position in the probability distribution / variable type: m = measurement</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = Europe</li> <li>EPSG code: epsg.3035 = EPSG:3035</li> <li>Version code: v20240528 = 2024-05-28 (creation date)</li> </ol>

opencc-by-4.0Jul 2024View details →

zenodo44/100

Dakar very-high resolution land cover map

This land cover map of Dakar (Senegal) was created from a Pléiades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3]. Description of the files: <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level8_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level8_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level8_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level9_Shadowsback.zip" : Corresponds to the "level8_Splitbuildings" with shadows coming from the original classification.</li> <li>"Dakar_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul>   References: [1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” Remote Sensing 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>. [2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>. [3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In Proceedings of the 2017 Conference on Big Data from Space (BiDS’17).   Founding:  This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).

openmit-licenseJun 2018View details →

zenodo44/100

Ouagadougou very-high resolution land cover map

This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3]. Description of the files: <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level5_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level5_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level5_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level6_Shadowsback.zip" : Corresponds to the "level5_Splitbuildings" with shadows coming from the original classification.</li> <li>"Ouaga_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul>   References: [1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” Remote Sensing 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>. [2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>. [3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In Proceedings of the 2017 Conference on Big Data from Space (BiDS’17).   Founding:  This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).

openmit-licenseJun 2018View details →

zenodo44/100

Dataset of High Resolution Mammographic Images

This dataset contains 138 high resolution mamographic images. Contrast Limited Adjustment Histogram Equalization (CLAHE) was used to enhance selected raw  mamographic images.available in mammographic image analysis society (MIAS) database.

opencc-by-4.0Oct 2018View details →

zenodo44/100

A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)

The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation).  The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.

opencc-by-4.0Apr 2018View details →

Figshare44/100

High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset

This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper.

opencc-by-4.0Dec 2017View details →

zenodo44/100

Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins

Data repository for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins Data description.pdf describes the uploaded data. Data.xlsx is the data represented in the paper. Esrfit_Npeak.m, Esrfit_xN.m, GaussianFunc.m, Gaussian_xN_Func.m, Lorentz_Func.m, Lorentz_xN_Func.m, Rabifit_xN.m, Rabi_xN_Func.m, FourierTransformRabi.m are Matlab code files to transform and fit the data.

opencc-by-4.0Jul 2019View details →

zenodo44/100

D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'

Cite the source of the dataset as: <blockquote> Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html </blockquote>

openlgpl-3.0Nov 2023View details →

zenodo44/100

Ultra-high Resolution Land Use Data Set of Typical Villages in Northeastern Tibetan Plateau

This dataset was collected by a research team during a field investigation in the Hehuang Valley of Qinghai Province from July to August 2022. Using the DJI Mavic2pro equipped with a Hasselblad L1D-20c camera, 55 typical villages were selected in the Hehuang Valley and over 4600 aerial photographs were obtained using drone photogrammetry technology as raw data. Using AgisfphotoScan 1.25 software to synthesize orthophoto images with a spatial resolution of 0.05m. The vector data of human settlement boundaries in villages was extracted through visual interpretation. Based on the object-oriented human-machine interaction interpretation method, 55 typical village land use datasets in 2022 were obtained (including forests, grasslands, forest land, cultivated land, water bodies, roads, unused land, and building land, totaling 8 categories). By establishing 1050 sample points and using confusion matrix analysis, it was found that the overall accuracy of the dataset was 96.86%, with a Kappa coefficient of 0.95. It can accurately reflect the spatial form, land use composition, and surrounding environment of typical villages. Aerial photographs all have longitude, latitude, and altitude information, providing ultra-high resolution data sources for village spatial structure analysis, land use mapping, and analysis work, effectively assisting in the improvement of human housing and rural revitalization strategies.

opencc-by-4.0Jul 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record