Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
363
datasets available to search
ShareScore release 0.9.0
Dataset results
363 results for “harmonics”
Reference grids (vector) and their centroids for harmonization of analysis
<p>This dataset contains reference grids (vector) and their centroids for harmonized analysis. All the files are <em>geoparquet</em>, they are described below. Production procedure is available at projects GitHub repository (https://github.com/aavotins/HiQBioDiv/blob/main/Templates/TemplateGrids_Vector.R):</p> <ul> <li>"tikls100_sauzeme.parquet" contains terrestrial territory of Latvia divided in 100-by-100 m polygon grid cells. Contains fields: <ul> <li>"id" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"rinda300" with ID's matching file "tikls300_sauszeme.parquet";</li> <li>"ID1km" with ID's matching file "tikls1km_sauszeme.parquet";</li> <li>"rinda500" with ID's matching file "tikls500_sauszeme.parquet";</li> <li>"geom" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls300_sauzeme.parquet" contains terrestrial territory of Latvia divided in 300-by-300 m polygon grid cells. Contains fields: <ul> <li>"rinda300" feature ID;</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls500_sauzeme.parquet" contains terrestrial territory of Latvia divided in 500-by-500 m polygon grid cells. Contains fields: <ul> <li>"rinda500" feature ID;</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls1km_sauzeme.parquet" contains terrestrial territory of Latvia divided in 1000-by-1000 m polygon grid cells. Contains fields: <ul> <li>"ID1km" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"geometry" a {sf} geometry definition field.</li> </ul> </li> <li>"pts100_sauszeme.parquet" contains centroids of file "tikls100_sauszeme.parquet" with their attribute fields;</li> <li>"pts300_sauszeme.parquet" contains centroids of file "tikls300_sauszeme.parquet" with their attribute fields and additionally "tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"pts500_sauszeme.parquet" contains centroids of file "tikls500_sauszeme.parquet" with their attribute fields;</li> <li>"pts1000_sauszeme.parquet" contains centroids of file "tikls1km_sauszeme.parquet" with their attribute fields;</li> <li>"tks93_50km.parquet" contains topographic map of Latvia pages (TKS-93 M:50000). Contains fields: <ul> <li>"NOSAUKUMS" with a page name;</li> <li>"NUMURS" with a page number;</li> <li>"Shape_Length" an attribute from ESRI File Geodatabase;</li> <li>"Shape_Area" an attribute from ESRI File Geodatabase;</li> <li>"Shape" a {sf} geometry definition field;</li> </ul> </li> <li>All the above mentioned files are stored also as layers in geopakage file "vector_grids.gpkg" having the same names and attributes.</li> </ul>
Dissecting the FAIR Guiding Principles - Key Categories, Core Concepts, Focus Elements, and Harmonized Indicators
<p>A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, requirements, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.</p>
Spherical harmonic models of the shape of the Moon (principal axis coordinate system) [LDEM128]
<p>This archive contains five spherical harmonic models of the shape of the Moon in a principal axis coordinate system, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 11519, which was generated from a lunar shape model sampled at 128 pixels per degree.</p> <p>The dataset used to generate these models is the file <a href="https://doi.org/10.60903/LOLA_PA">LDEM128_PA_gridline_202405.grd</a>. As described by Neumann (2024), this shape mode is based on a combination of laser altimeter data obtained by the LOLA instrument on the Lunar Reconaissance Orbiter spacecraft and the SLDEM2015 shape model that makes use of both LOLA and Kaguya terrain camera data. The netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The five files in this archive are</p> <ul> <li>Moon_LDEM128_shape_pa_11519.sh.gz</li> <li>Moon_LDEM128_shape_pa_5759.sh.gz</li> <li>Moon_LDEM128_shape_pa_2879.sh.gz</li> <li>Moon_LDEM128_shape_pa_1439.sh.gz</li> <li>Moon_LDEM128_shape_pa_719.sh.gz</li> </ul> <p>The numbers 11519, 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 128, 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>This shape model uses the same coordinate system as most lunar gravity models. The principal axis coordinate system differs from the more common mean Earth/polar axis system by about 1 km at the equator. For a mean Earth/polar axis model, use <a href="../records/10796823">Spherical harmonic models of the shape of the Moon</a>.</p>
Spherical harmonic models of the shape of asteroid (16) Psyche
<p>This archive contains spherical harmonic models of the shape of asteroid (16) Psyche.</p> <p><strong>Psyche-Shepard2017.sh</strong></p> <p>This is a degree and order 29 spherical harmonic model of the shape of Psyche that was constructed from the shape model of Shepard et al. (2017). The spherical harmonic coefficients were obtained from a least squares inversion that made use of the vertex coordinates from the file <code>psyche.v.final.mod.mod</code>. The least squares inversion was performed using the pyshtools routine <code>SHCoeffs.from_least_squares()</code> and tests show that the power spectrum is stable for maximum degrees up to, and including, 29. The coefficients are in meters and should be used with 4-pi normalized spherical harmonic functions.</p> <p><strong>Psyche-Shepard2021.sh</strong></p> <p>This is a degree and order 10 spherical harmonic model of the shape of Psyche that was constructed from the shape model of Shepard et al. (2021). The spherical harmonic coefficients were obtained from a least squares inversion that made use of the vertex coordinates from the file <code>psyche.vertex.obj</code>. The least squares inversion was performed using the pyshtools routine <code>SHCoeffs.from_least_squares()</code>, and tests show that the power spectrum decreases dramatically for maximum spherical harmonic degrees beyond 10. The coefficients are in meters and should be used with 4-pi normalized spherical harmonic functions.</p>
Half-kilowatt high energy third harmonic conversion to 50 J @ 10 Hz at 343 nm [dataset]
<p>Dataset relevant to the publication "Half-kilowatt high energy third harmonic conversion to 50 J @ 10 Hz at 343 nm" in HPLSE</p>
Third harmonic generation images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>Data set for 11 samples in 3 groups of Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains THG images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Graphic Illustration of Kendra Phelp's Talk: A harmonized taxonomic resource is critical for accurately interpreting host-pathogen interactions
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Kendra Phelps at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Harmonized Tree Species Occurrence Points for Europe
<p>This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.</p> <p>The <em>RDS </em>file is created from an sf-object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p><strong>The code producing this data set is <a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">publicly available on GitLab.</a></strong></p> <p>Data sets were last updated in September 2021.</p> <p>Variables:</p> <ul> <li><strong>id</strong> = unique point identifier</li> <li><strong>easting</strong> = x coordinate</li> <li><strong>northing </strong>= y coordinate</li> <li><strong>country </strong>= ISO country code</li> <li><strong>species </strong>= Latin species name</li> <li><strong>genus </strong>= genus name</li> <li><strong>scientific_name </strong>= long species name</li> <li><strong>gbif_taxon_key </strong>= taxon key from GBIF</li> <li><strong>gbif_genus_key </strong>= genus key from GBIF</li> <li><strong>taxon_rank </strong>= species or genus</li> <li><strong>year </strong>= year of observation</li> <li><strong>accessed_through </strong>= database through which data was accessed (GBIF, LUCAS, EU-Forest)</li> <li><strong>dataset_info </strong>= data set name (individual sub-data-set)</li> <li><strong>citation </strong>= DOI citation of the individual data set</li> <li><strong>license </strong>= distribution license</li> <li><strong>location_accuracy </strong>= spatial accuracy of observation (meters)</li> <li><strong>flag_location_issue </strong>= known location issues present</li> <li><strong>flag_date_issue </strong>= known date issues present</li> <li><strong>eoo </strong>= Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)</li> <li><strong>dbh </strong>= Diameter Breast Height (only recorded for observations from the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>))</li> <li><strong>lc1 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS</a> land cover type 1 (only recorded for observations from LUCAS data)</li> <li><strong>lc2 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS </a>land cover type 2 (only recorded for observations from LUCAS data)</li> <li><strong>landmask_country </strong>= land mask overlay 30 meters (NA = not on land)</li> <li><strong>corine </strong>= <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018">CORINE 2018</a> land cover type (extracted from the 100 meter raster data set)</li> <li><strong>nightlights</strong> = <a href="https://eogdata.mines.edu/download_dnb_composites.html">light pollution</a> observed by VIIRS (proxy for remoteness / distance to human structures)</li> <li><strong>canopy_height </strong>= <a href="https://zenodo.org/record/4057883#.X3sx4-2xW9I">canopy height</a> derived from GEDI waveform LiDAR point data</li> <li><strong>natura_2000 </strong>= Natura 2000 site code (if a point falls inside a protected area (<a href="https://www.eea.europa.eu/data-and-maps/data/natura-11">GIS-layer</a>) this variable contains the site identification code; all sites can be explored on an <a href="https://natura2000.eea.europa.eu/">interactive map</a>)</li> <li><strong>freq_location </strong>= number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)</li> <li><strong>geometry </strong>= point geometry in ETRS89 / LAEA Europe</li> </ul> <p>See <strong><a href="https://docs.google.com/spreadsheets/d/1WM0BIaVEKxTsCISEaF76RJ8F1iWiZlDfuyQCBiF2Sxw/edit?usp=sharing">this detailed documentation</a></strong> for more insights into each variable and individual GBIF data set citations.</p> <p>If you would like to know more about the creation of this data set, see</p> <ol> <li>the R-Markdown documenting the process (<a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">GitLab repository</a>)</li> <li>the talk at OpenGeoHub Summer School 2020 (<a href="https://www.youtube.com/watch?v=5HhmLGcqXLs&list=PLXUoTpMa_9s0Ea--KTV1OEvgxg-AMEOGv&index=40">Youtube</a>)</li> </ol> <p>Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.</p> <p> </p> <p>This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (<a href="https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095">https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095</a>).</p>
A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices
<p>Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research.</p> <p>We provide here a benchmark dataset for time series reconstruction method based on the<strong> <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> </strong>collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview.</p> <p>Specifically, following the <strong><a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a></strong> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as:</p> <p><span class="math-tex">\(NDVI = {NIR - Red \over NIR + Red}\)</span></p> <p>only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif</em></p> <p>where <em>TILE_NAME</em> is one between the above specified ones, <em>YYYY</em> is the corresponding year (spanning from 2015 to 2022) and <em>DDD</em> is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of <strong>184</strong> images (23 images for 8 years) that can be easily manipulated, for example using the <strong><a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a></strong> in Python.</p> <p>Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both.</p> <p>The data including the artificial gaps are stored with the naming structure</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif</em></p> <p>following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations. </p> <p>This dataset was used to compare the performance of some gap-filling methods and we provide a <strong><a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a></strong> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619). </p> <p>If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you!</p> <p>References:</p> <ol> <li> <p>Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a> </p> </li> <li> <p>Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a> </p> </li> <li> <p>Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a> </p> </li> <li> <p>Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a> </p> </li> <li> <p>Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a></p> </li> <li> <p>Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a></p> </li> </ol>
Dataset exploring the use of quasi-harmonic approximation to understand the thermal properties of Bi2Se3
<p>This data set contains input and output files for DFT calculations on Bi<sub>2</sub>Se<sub>3</sub> for a number of different fixed unit cells with calculations performed using VASP and Phonopy. At each fixed volume, optimisations and phonon calculations have been performed. These have been used to understand the thermal properties of the material using the quasi-harmonic approximation. </p>
Harmonized Soil Database of Ecuador 2021
In Ecuador there have been two main projects that have collected national soil information. These projects are: a) “Generación de Geoinformación para la Gestión de territorio y valoración de tierras rurales de la Cuenca del Río Guayas, escala 1:25.000” (2007-2015) developed by the Instituto Espacial Ecuatoriano (IEE), and b) “Generación De Geoinformación Para La Gestión Del Territorio A Nivel Nacional" (2009-2012), developed by Sistema Nacional de Información de Tierras Rurales e Infraestructura Tecnológica (SIGTIERRAS). These projects followed a similar methodology to collect and analyze soil information. However, the resulting databases have different data structures, and they show differences in the way these projects store and present soil information. Only a portion of the original databases was digitized. Most of the available data was only available in PDF files. These PDF files need to be digitized into an easy-to-manage format (e.g., *csv). The difficulty is that each PDF represents one soil profile containing morphological and analytical soil information. Thus, given the volume of soil information available in hundreds of PDF files, manual extraction (e.g., capturing soil data one by one) was not feasible. Therefore, automatic extraction of soil information from each PDF file was developed using open-source programming for data management and statistical computing (in Python and R). This process was developed to optimize data extraction from PDF files. The soil information from both projects in PDF files has been digitized and unified into one harmonized database. We present a new database for Ecuador containing soil information from 13,542 soil profiles, 5 368 are from the IEE project and 8 174 profiles from the SIGTIERRAS project. The new database includes 5368 are from the IEE project and 8174 profiles from the SIGTIERRAS project. The new database includes data from 51,692 soil horizons and information of about 20 morphological and 46 analytical variabl
Global Pasture Watch - Grassland reference samples based on visual interpretation of VHR imagery and harmonized datasets (2000–2024)
<p>Reference point samples used in the production of the <a href="https://doi.org/10.5281/zenodo.13890401">global maps of annual grassland class and extent for 2000—2022</a><strong> </strong>within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Wath</a> initiative. </p> <p>The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises <strong>2.3M points</strong> visually classified (<em>using Very High Resolution imagery</em>) in:</p> <ol> <li><strong>Cultivated grassland,</strong></li> <li><strong>Natural/semi-natural grassland</strong></li> <li><strong>Other land cover</strong></li> </ol> <p>The file <code>gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:</p> <ul> <li>cluster_id: Cluster id defined by k-means (FSCS),</li> <li>cluster_distance: Distance from the sample tile to center of the cluster (FSCS),</li> <li>cluster_size: Size of cluster (strata) defined by the FSCS,</li> <li>priority: Priority used by the visual interpretation,</li> <li>tile_id: Sample tile id,</li> <li>imagery: VHR reference images used by the visual interpretation,</li> <li>min_year: Minimum of year covered by the reference samples,</li> <li>max_year: Maximum of year covered by the reference samples,</li> <li>n_years: Number of years covered by the reference samples,</li> <li>n_samples_c1: Number of reference samples for "Cultivated grass" (1),</li> <li>n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),</li> <li>n_samples_c3: Number of reference samples for "Open Shrubland" (2),</li> <li>n_samples_c4: Number of reference samples for "Not grass" (3),</li> <li>n_samples_all: Total number of reference samples,</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides individual points (with 60-m spatial support) and include the follow collumns:</p> <ul> <li>sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,</li> <li>x: Longitude in WGS84 (EPSG:4326),</li> <li>y: Latitude in WGS84 (EPSG:4326),</li> <li>vi_tile_id: 1-km tile id,</li> <li>tile_id: GLAD tile id (1x1 degree)</li> <li>imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),</li> <li>ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,</li> <li>year: Reference year of GPW samples (based on VHR image) and of other existing datasets,</li> <li>class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,</li> <li>class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,</li> <li>dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW) <br>dataset_class: Original land cover class provided by the maintainer of existing dataset</li> <li>esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,</li> <li>glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,</li> <li>glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,</li> <li>gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),</li> <li>ml_cv_group: spatial block CV group (based on vi_tile_id),</li> <li>ml_type: specify if the sample was used for (1) training or (2) calibration.</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides the grid samples (with 10-m spatial support) and include the follow collumns:</p> <ul> <li>tile_id: 1-km tile id,</li> <li>bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,</li> <li>bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,</li> <li>bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,</li> <li>google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,</li> <li>google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,</li> <li>google_image_end_date: End date of the Google Maps Images used in the visual interpretation,</li> <li>missing_image_date: No images available,</li> <li>same_image_bing_google: Images from the same date available in Google and Bing Maps.</li> </ul> <p>The dataset was produced through the <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS plugin Fast Grid Inspection</a>.</p> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Spherical harmonic models of the shape of Mercury
<p>The data used to generate these spherical harmonic models is the global digital elevation model (DEM) of Mercury, produced by the U.S. Geological Survey (USGS). The DEM was derived from from stereo image pairs (stereo photogrammetry) captured by the Mercury Dual Imaging System (MDIS) narrow-angle camera (NAC) and multispectral wide-angle camera (WAC) on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. </p> <p>The global DEM was downloaded throught the <a href="https://astrogeology.usgs.gov/search/map/Mercury/Topography/MESSENGER/Mercury_Messenger_USGS_DEM_Global_665m_v2">Astropedia catalog</a> in geoTIFF format and equirectangular projection. Using the <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> package, the dataset was loaded in python, scaled to the local height and radius (described in the Astropedia documentation), and exported into .dat format. Then, the file was converted into a netcdf format and resampled into a gridline registration using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> as follows:<br><code>gmt xyz2grd filename.dat -Gfilename.grd -R0/360/-90/90 -I0.015625/0.015625 -ZTLd -fg -rp</code><br><code>gmt grdsample filename.grd -Gfilename_gridline.grd -T</code></p> <p>The resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software with the <code>SHGrid.from_netcdf()</code> and expanded into spherical harmonics using the function <code>SHGrid.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)m. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <p>- Mercury_shape_5759.sh.gz<br>- Mercury_shape_2879.sh.gz<br>- Mercury_shape_1439.sh.gz<br>- Mercury_shape_719.sh.gz</p> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>The spherical harmonic coefficients can be loaded with pyshtools as follows:<br><code>SHCoeffs.from_file("filename.sh.gz", format='bshc')</code></p>
Spherical harmonic models of the gravity field of Jupiter
<p>This archive contains published spherical harmonic models of the gravity field of Jupiter. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Kaspi2023.sh (value of GM provided by Y. Kaspi, personal communication)</p>
Integrated Harmonized Dataset Adolescent Substance Use, Psychosocial Constructs, and Demographics
<p>This dataset contains final analysis cases used in our paper Psychosocial Constructs Related to Alcohol, Cigarette, and Marijuana Use: An Integrated and Harmonized Analysis. We assembled raw data from 25 longitudinal research projects. We collected data from our own research projects (7 projects) as well data provided by 18 researchers. Datasets included epidemiological studies and prevention studies. For the latter, only control group and pretest data were included. All data, including surveys and projects have been de-identified.</p>
Harmonized Cultural Access & Participation Dataset for Music
<p>Changes since the last version: in the .csv export there was a naming problem.</p> <p>- `visit_concert`: This is a standard CAP variables about visiting frequencies, in numeric form. <br> - `fct_visit_concert`: This is a standard CAP variables about visiting frequencies, in categorical form. <br> - `is_visit_concert`: binary variable, 0 if the person had not visited concerts in the previous 12 months.<br> - `artistic_activity_played_music`: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable. <br> - `fct_artistic_activity_played_music`: The `artistic_activity_played_music` in categorical representation.<br> - `artistic_activity_sung`: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music.<br> - `fct_artistic_activity_sung`: The `artistic_activity_sung` variable in categorical representation.<br> - `age_exact`: The respondent’s age as an integer number. <br> - `country_code`: an ISO country code<br> - `geo`: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.]<br> - `age_education`: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, `age_education` and `is_student`. <br> - `is_student`: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute `age` in this case to `age_education`, but we will show why this is not a good strategy.<br> - `w`, `w1`: Post-stratification weights for the 15+ years old population of each country. Use `w1` for averages of `geo` entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use `w` when treating the United Kingdom and Germany as one territory.<br> - `wex`: Projected weight variable. For weighted average values, use `w`, `w1`, for projections on the population size, i.e., use with sums, use `wex`.<br> - `id`: The identifier of the original survey.<br> - `rowid``: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.</p>
Adapting the Harmonized Data Quality Framework for Ontology Quality Assessment
<p>Ontologies play an important role in the representation, standardization, and integration of biomedical data, but are known to have data quality (DQ) issues. We aimed to understand if the Harmonized Data Quality Framework (HDQF), developed to standardize electronic health record DQ assessment strategies, could be used to improve ontology quality assessment. A novel set of 14 ontology checks was developed. These DQ checks were aligned to the HDQF and examined by HDQF developers. The ontology checks were evaluated using 11 Open Biomedical Ontology Foundry ontologies. 85.7% of the ontology checks were successfully aligned to at least 1 HDQF category. Accommodating the unmapped DQ checks (n=2), required modifying an original HDQF category and adding a new Data Dependency category. While all of the ontology checks were mapped to an HDQF category, not all HDQF categories were represented by an ontology check presenting opportunities to strategically develop new ontology checks. The HDQF is a valuable resource and this work demonstrates its ability to categorize ontology quality assessment strategies.</p>
Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"
<p>Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in <em>Nature Ecology & Evolution</em>, 2022, https://doi.org/10.1038/s41559-022-01888-8).</p> <p>Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years.</p> <p>Scripts written by Tonke Strack</p> <p>DATA SOURCES<br>* WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019).<br>* LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>* MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005).<br>* planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv</p> <p>DATA<br>1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt<br>2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv<br>3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv</p> <p>CODE<br>1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2)<br>2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series<br>3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4)<br>4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5)</p> <p>*Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber albus</em>, because some studies only reported them together as <em>Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>. In total, 41 species of planktonic foraminifera were included in this study.</p> <p>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. <em>Sci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).</p>
Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project
<p>Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript:</p> <blockquote> <p>"Harmonized, gap-filled dataset from 20 urban flux tower sites" </p> <p><a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a></p> </blockquote> <p>Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata. </p> <p><strong>Data can be downloaded from the bottom of this page. </strong></p> <table> <tbody> <tr> <td> <p><strong>Sitename</strong></p> </td> <td> <p><strong>City</strong></p> </td> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Observed period</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>AU-Preston</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Aug 2003 – Nov 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>AU-SurreyHills</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Feb 2004 – Jul 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>CA-Sunset</p> </td> <td> <p>Vancouver</p> </td> <td> <p>Canada</p> </td> <td> <p>Jan 2012 – Dec 2016</p> </td> <td> <p>(Christen et al., 2011; Crawford and Christen, 2015)</p> </td> </tr> <tr> <td> <p>FI-Kumpula</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Karsisto et al., 2016)</p> </td> </tr> <tr> <td> <p>FI-Torni</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Järvi et al., 2018; Nordbo et al., 2013)</p> </td> </tr> <tr> <td> <p>FR-Capitole</p> </td> <td> <p>Toulouse</p> </td> <td> <p>France</p> </td> <td> <p>Feb 2004 – Mar 2005</p> </td> <td> <p>(Masson et al., 2008; Goret et al., 2019)</p> </td> </tr> <tr> <td> <p>GR-HECKOR</p> </td> <td> <p>Heraklion</p> </td> <td> <p>Greece</p> </td> <td> <p>Jun 2019 – Jun 2020</p> </td> <td> <p>(Stagakis et al., 2019)</p> </td> </tr> <tr> <td> <p>JP-Yoyogi</p> </td> <td> <p>Tokyo</p> </td> <td> <p>Japan</p> </td> <td> <p>Mar 2016 – Mar 2020</p> </td> <td> <p>(Hirano et al., 2015; Ishidoya et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Jungnang</p> </td> <td> <p>Seoul</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jan 2017 – Apr 2019</p> </td> <td> <p>(Jo et al., n.d.; Hong et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Ochang</p> </td> <td> <p>Ochang</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jun 2015 – Jul 2017</p> </td> <td> <p>(Hong et al., 2019, 2020)</p> </td> </tr> <tr> <td> <p>MX-Escandon</p> </td> <td> <p>Mexico City</p> </td> <td> <p>Mexico</p> </td> <td> <p>Jun 2011 – Sep 2012</p> </td> <td> <p>(Velasco et al., 2011, 2014)</p> </td> </tr> <tr> <td> <p>NL-Amsterdam</p> </td> <td> <p>Amsterdam</p> </td> <td> <p>Netherlands</p> </td> <td> <p>Jan 2019 – Oct 2020</p> </td> <td> <p>(Steeneveld et al., 2020)</p> </td> </tr> <tr> <td> <p>PL-Lipowa</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013; Pawlak et al., 2011)</p> </td> </tr> <tr> <td> <p>PL-Narutowicza</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013, 2006)</p> </td> </tr> <tr> <td> <p>SG-TelokKurau</p> </td> <td> <p>Singapore</p> </td> <td> <p>Singapore</p> </td> <td> <p>Feb 2015 – Feb 2016</p> </td> <td> <p>(Roth et al., 2017)</p> </td> </tr> <tr> <td> <p>UK-KingsCollege</p> </td> <td> <p>London</p> </td> <td> <p>UK</p> </td> <td> <p>Apr 2012 – Jan 2014</p> </td> <td> <p>(Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b)</p> </td> </tr> <tr> <td> <p>UK-Swindon</p> </td> <td> <p>Swindon</p> </td> <td> <p>UK</p> </td> <td> <p>May 2011 – Apr 2013</p> </td> <td> <p>(Ward et al., 2013)</p> </td> </tr> <tr> <td> <p>US-Baltimore</p> </td> <td> <p>Baltimore</p> </td> <td> <p>USA</p> </td> <td> <p>Jan 2002 – Jan 2007</p> </td> <td> <p>(Crawford et al., 2011)</p> </td> </tr> <tr> <td> <p>US-Minneapolis</p> </td> <td> <p>Minneapolis</p> </td> <td> <p>USA</p> </td> <td> <p>Jun 2006 – May 2009</p> </td> <td> <p>(Peters et al., 2011; Menzer and McFadden, 2017)</p> </td> </tr> <tr> <td> <p>US-WestPhoenix</p> </td> <td> <p>Phoenix</p> </td> <td> <p>USA</p> </td> <td> <p>Dec 2011 – Jan 2013</p> </td> <td> <p>(Chow, 2017; Chow et al., 2014)</p> </td> </tr> </tbody> </table> <p>For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>.</p> <p>For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>.</p> <p><strong>Data</strong></p> <p>Two data archives are available on this page.</p> <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> <p><strong>Full collection</strong></p> <p>The full archive includes site folders with:</p> <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> <p><strong>"Obs Only"</strong></p> <p>This archive contains duplicate data from the full collection (observations after QC):</p> <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> <p><strong>Site references</strong></p> <p>Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015.</p> <p>Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017.</p> <p>Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014.</p> <p>Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b.</p> <p>Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015.</p> <p>Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011.</p> <p>Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006.</p> <p>Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013.</p> <p>Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019.</p> <p>Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015.</p> <p>Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020.</p> <p>Hong, J.-W., Hong, J., Chun, J., Lee, Y. H., Chang, L.-S., Lee, J.-B., Yi, K., Park, Y.-S., Byun, Y.-H., and Joo, S.: Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements, Carbon Balance and Management, 14, 13, https://doi.org/10.1186/s13021-019-0128-6, 2019.</p> <p>Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020.</p> <p>Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018.</p> <p>Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d.</p> <p>Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016.</p> <p>Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a.</p> <p>Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b.</p> <p>Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008.</p> <p>Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017.</p> <p>Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013.</p> <p>Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011.</p> <p>Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011.</p> <p>Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017.</p> <p>Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019.</p> <p>Steeneveld, G.-J., Horst, S. van der, and Heusinkveld, B.: Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu2020-1547, 2020.</p> <p>Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L. T., and Lamb, B.: Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign, Theor Appl Climatol, 103, 501–517, https://doi.org/10.1007/s00704-010-0314-7, 2011.</p> <p>Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmospheric Chemistry and Physics, 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, 2013.</p> <p>Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., and Molina, L. T.: Sources and sinks of carbon dioxide in a neighborhood of Mexico City, Atmospheric Environment, 97, 226–238, https://doi.org/10.1016/j.atmosenv.2014.08.018, 2014.</p> <p>Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmospheric Chemistry and Physics, 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013.</p>
study of second harmonic generation in periodically poled fiber in double pass configuration
<p>This dataset includes the experimental measurements and the numerical simulations of the power of second harmonic generated inside a periodically poled fiber traversed in single and double pass by a fundamental signal whose wavelength is included in a certain range of values. This measurements are the preliminary study for situation where the PPSF can be exploited in multiple pass configuration, such as in a cavity. </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.