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A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
<p><strong>Description</strong><br> This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br> The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br> The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br> In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br> We also include sample codes of baseline models for benchmarking of more elaborated models.</p> <p><strong>Data usage</strong><br> The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br> <em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p> <p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p> <p><strong>Sample code</strong><br> As part of the data release, we are also including the sample code written in Python 3. <br> The preprocessed data used in the scripts are also provided. <br> The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br> Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br> All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p> <p><strong>Units</strong><br> All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br> All irradiance and weather data are in SI units.<br> Sky image features are derived from 8-bit RGB (256 color levels) data.<br> Satellite images are derived from 8-bit gray-scale (256 color levels) data.</p> <p><strong>Missing data</strong><br> The string "NAN" indicates missing data</p> <p><strong>File formats</strong><br> All time series data files as in CSV (comma separated values)<br> Images are given in tar.bz2 files</p> <p><strong>Files </strong></p> <ul> <li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li> <li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li> <li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li> <li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li> <li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li> <li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li> <li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li> <li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li> <li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li> </ul> <p> </p>
Radar and Lidar scattering lookup tables for atmospheric hydrometeors using a T-Matrix method and a Mie theory
<h2>Overview</h2> <p>The database includes text files containing the scattering amplitude matrices for single spherical/nonspherical particles for radar and lidar. They are the lookup tables used for calculating radar and lidar observables in the Cloud-Resolving Radar Simulator (Oue et al. 2020). The radar scattering properties were calculated for several hydrometeor categories using a T-matrix method proposed by Mishchenko (2000) accounting for incident angles, scattering direction (forward and backward), polarimetry (horizontally (H) and vertically (V) polarized waves), particle aspect ratio, phase (liquid or ice), bulk density, temperature, particle size, and radar frequency. The lidar scattering properties at a vertical incidence were calculated for spherical liquid or ice particles using the BHMIE Mie code (Bohrean and Hyffman,1998) accounting for lidar wavelength, temperature, and bulk density. The hydrometeor categories are commonly used for cloud resolving models employing bulk microphysical schemes (e.g., cloud, rain, ice cloud, snow aggregates, and graupel). Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v3.34).</p> <h2>Data structure</h2> <p>The data files are arranged and zipped every hydrometeor types. The names of the tar-zipped directories under the top directory LLUT3 represents the hydrometer type.<br>For lidar scattering, the following directories are included:<br>ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm<br>mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm</p> <p>For radar scattering, the following hydrometer types are included:<br>cloud: Radar scattering for liquid cloud droplets (spherical shape)<br>raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)<br>rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)<br>ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9<br>ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2<br>smallice: Radar scattering for spherical cloud ice particles<br>snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6<br>graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6<br>graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8<br>graupel: Radar scattering for spherical graupel particles<br>gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)<br>unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4<br>unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6<br>unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8<br>unrimedice: Radar scattering for spherical unrimed ice particles<br>partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4<br>partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6<br>partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8<br>partrimedice: Radar scattering for partially rimed spherical ice particles </p> <h2>The file name convention </h2> <p>For lidar scattering data, each file name has the following format:<br>[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat<br>The hydrometeor type shows: 1) ‘cld’ for liquid cloud droplets, and 2) ‘ice’ for ice particles. The phase ID shows: 1) ‘p25’ for ceilometer liquid cloud, 2) ‘p20’ for MPL lidar liquid cloud, and 3) ‘m30’ for MPL lidar ice. </p> <p>For radar scattering data, each file name has the following format.<br>[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat<br>The hydrometeor type follows the directory name presented above.</p> <h2>Format of the data files</h2> <p>Line 1: Wavelength in mm<br>Line 2: Temperature in K<br>Line 3: Refractive index (real and imaginary)<br>Line 4: Number of radii calculated and number of elevation angles<br>Line 6: Incident angle and scattered angle in degrees<br>Line 7: Radius in mm and aspect ratio<br>Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)<br>Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number) <br>Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.</p>
A new method for approximating fractional derivatives/ integrals as a series of higher-integer-order derivatives - examples and results of applying the method to initial/boundary value problems
<p>The posted research data includes examples of the application of the author's fractional derivative/integral approximation method using the sum of higher integer derivatives. The attached text files contain the numerical solutions of the presented examples, recorded as a set of numerical values obtained from the performed computations.</p> <ul> <li>Example 4.1 <br> \(\begin{cases}<br> \displaystyle<br> ^{C}D^{\alpha}_{a+}\sin (x), \\<br> x \in \langle a, 3\pi \rangle \quad \hbox{and} \quad<br> \alpha = \{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},<br> \end{cases}\)<br><br></li> <li>Example 4.2 <br>\( \begin{cases}<br> \displaystyle<br> I^{\alpha}_{0+} e^{-x}\cos 7x, \\<br> x \in \langle 0,1\rangle \quad \hbox{and} \quad<br> \alpha =\{1.0,\ 1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0 \},<br> \end{cases} \)<br><br></li> <li>Example 5.1 <br>\(\begin{cases}<br> ^{C} D_{0+}y(x)+2y(x)=x+ \frac{2x^{\alpha+1}}{\Gamma(\alpha+2)},\\<br> x\in\langle0,1\rangle, \\<br> y(0) = 0; \quad y(1) = \frac{1}{\Gamma(\alpha+2)}, \\<br> \alpha = \{1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0\}. <br>\end{cases}\)<br><br></li> <li>Example 5.2 <br>\(\begin{cases}<br> ^{C}D_{0+}^{\alpha}y(x)+1.8 y(x)=0,\\<br> x\in \langle 0,2\rangle \quad \hbox{and} \quad \alpha=\{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},\\<br> y(0)=1.<br>\end{cases}\)</li> </ul>
Generated WSP: Validation of a water-sensitive paper-based method for the characterization of agricultural spray droplets
<p>Synthetic images were generated in a Python environment using the OpenCV library to replicate the distribution of droplets in WSP. The images display droplet stains represented by blue circles (255,0,0) on a yellow background (0,255,255) to enhance contrast and enable more precise analysis. The synthetic images were created in two distinct resolutions, namely 640x480 and 2560x1440 pixels, with the aim of reproducing the output of two specific digital microscopes: the Jiusion 640x480 and the Jiusion HD 2560x1440 (Shenzen, China). The resolution is chosen based on the expected practical application, ensuring that any image analysis algorithm developed can effectively process images with similar characteristics to those obtained under real conditions by these microscopes. Each pixel in this configuration corresponds to a physical size of 18.125 µm in images with a resolution of 640x480, and a size of 6.875 µm in images with a resolution of 2560x1440. Multiple patterns were created to simulate various configurations of droplet stains in WSP. The sizes of single droplet stains varied between 100 and 600 µm, with spacings of either 1000 µm or 2000 µm between drops (see attached figure). Furthermore, the same size range was utilised to generate patterns with double and overlaid droplet stains, with a consistent spacing of 2800 µm between each stain (see attached figure). The implementation of this systematic method guarantees the accurate calibration and application of image analysis algorithms in real-world situations. This allows for the representation of precise measurements and spacing that would be encountered in actual experimental conditions.</p>
Dataset for the publication "Implementation of an exact completing method of generation for face-milled spiral bevel gears with uniform depth taper"
<p>This dataset contains geometric and graphics data associated with the referenced paper, enabling the reproduction of the conducted research. </p>
Life Cycle Impact Assessment method for ozone depletion based on WMO 2022
<p>This dataset provides the most recent <a>characterization factors</a> for ozone depletion based on the latest ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. The dataset is formatted for easy import into life cycle assessment (LCA) software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are available for both 100-year and infinite time horizons.</p> <p>When using the dataset, please cite the folllowing publication:</p> <p>van den Oever, A. E.M., Puricelli, S., Costa, D., Thonemann, N., Lavigne Philippot, M., Messagie, M., Dataset with updated ozone depletion characterization factors for life cycle impact assessment, Data in Brief (in press), 2024, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.111103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.111103</a></p>
Database for RailRad calculation method for simulating sound radiated by railway track vibrations
<p>This dataset contains precalculated acoustic transfer functions for efficiently calculating the sound radiated by railway track vibrations.</p> <p>The transfer functions contained in each file describe the complex sound pressure produced at a number of receiver locations given a unit velocity at a source element on the railway track surface, per frequency and at a fixed wavenumber along the track.</p> <p>Four different acoustic geometries are included: (1) a standard UIC60 rail in free space, (2) the rail in an acoustic half space, (3) the rail located above a slab track surface, and (4) identical geometry to (3) but including an acoustically hard hull of a passenger train geometry above the track.</p> <p>More information about the exact location of source and receiver coordinates can be found in the .hdf5 files, in the subgroup 'info'. The transfer functions themselves are located in the dataset 'tfs', which are matrices of size (Number of frequency lines x number of sources x number of receivers).</p> <p>More information can be found here https://github.com/janniktheyssen/railrad</p> <p>This collection of databases is part of ongoing work at CHARMEC / Chalmers University of Technology, Gothenburg, Sweden (https://www.charmec.chalmers.se/). Parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the In2Track3 project under grant agreements No 101012456. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p>
Post-remediation evaluation of contaminated site using geophysical methods: Ortophotomosaic Olkusz (Poland) 20220629
<p>The orthophotomap is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is an orthophotomap with a 2.57 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326).</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Rare type III responses: data & data methods (v1.0.0)
<p>This repository includes the data (data-rare-type3-responses.csv) for Kalinkat et al. (2023).</p> <p>The data comprises a literature review on type III functional responses between 2002 and 2022 with 12 variables and 107 observations. Please read the accompanying document (Rall_et_al_2023_Zenodo_Rare-type3-responses_data_methods_v1_0_0.pdf) for the methods and a description of the variables. </p> <p><strong>Reference</strong></p> <p>Kalinkat, G. <em>et al.</em> (2023) ‘Empirical evidence of type III functional responses and why it remains rare’, <em>Frontiers in Ecology and Evolution</em>, 11:1033818. Available at: https://doi.org/10.3389/fevo.2023.1033818.</p>
Simulation dataset to benchmark 3D force inference methods
<p>Dataset of 47 artificial images (.tif) and corresponding segmentation masks (.tif), generated from simulations of foam-like cell structures (early embryos) of various cell numbers (2 to 11), cell sizes and interfacial tensions.<br> The ground truth simulation tensions and pressures to be inferred are provided as Numpy arrays (.npy).</p> <p>This dataset was used to benchmark a method to infer cellular forces in 3D from microscopy images of multicellular contours, that is available on <a href="https://github.com/VirtualEmbryo/foambryo">https://github.com/VirtualEmbryo/foambryo</a>.<br> Non-manifold multimaterial meshes corresponding to artificial microscopy images are also provided as binary files (.rec) and may be opened with our delaunay-watershed Python code, available on <a href="https://github.com/VirtualEmbryo/delaunay-watershed">https://github.com/VirtualEmbryo/delaunay-watershed</a>.</p> <p><strong>Credits, contact, citations</strong><br> If you use this dataset, please cite the published version of the following preprint: <br> <em>Ichbiah, S., Delbary, F., McDougall, A., Dumollard, R., & Turlier, H. (2023). Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy. bioRxiv, 2023-04. </em><a href="https://doi.org/10.1101/2023.04.12.536641">https://doi.org/10.1101/2023.04.12.536641</a><br> <br> We hope that this dataset may be useful to benchmark future 3D force inference methods.<br> If you have any question on this dataset, please contact <a href="mailto:herve.turlier@college-de-france.fr?subject=%5BZenodo%5D%203D%20tension%20inference%20benchmark%20dataset">Hervé Turlier</a>.</p> <p><strong>License</strong><br> Copyright (c) 2023 Turlier Lab - <a href="https://www.turlierlab.com/">https://www.turlierlab.com/</a><br> This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Bibliographic Data from the Computational Methods Applied to Earthen Historical Structures Review
<p>This database contains all the bibliographic information about the 293 records found after applying the Search Strategy used for the Computational Methods Applied to Earthen Historical Structures Review. Such strategy consisted on using relevant keywords grouped into three different search queries within ”TITLE-ABS-KEY”, for the years 2019-2023:</p> <ol> <li>(”earthen heritage” OR ”earthen historical building*” OR ”earthen historical structure*” OR ”earthen architect*” OR ”earthen monument*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (”computational method*” OR ”numerical analy*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (fem OR dem OR la OR ”finite element” OR ”discrete element” OR ”limit analysis”).</li> </ol> <p>The search was conducted on April 7, 2023.</p>
Processing of MODIS-Aqua data with Self-Organizing Maps NeuroVaria method for the southern canary upwelling system
<p>Abstract</p> <p>This ocean color dataset is derived from MODIS_Aqua sensor measurements covering the Southern Canary upwelling system. The raw L1A measurements were downloaded from NASA's Ocean Color web site and then processed using the Ocean Biology Processing Group's (OBPG) Multi-Sensor Level-1 to Level-2 (MSL12) code. The l2gen program, based on its standard process, generates Level-2 parameters consisting of the top of atmosphere radiance, the radiance of each ocean and atmosphere component, the measurement angles, Level-2 flags, ... The top of atmosphere radiance is pre-corrected to keep only a dependence on the diffuse transmittance, the aerosol contribution and the water leaving radiance.</p> <p><br> The pre-corrected product and measurement angles are assimilated using the Self-Organizing Map<br> NeuroVaria (SOM-NV) code (Diouf et al., 2013). SOM-NV is an algorithm based on two statistical models<br> that classify a dataset into a map, and then use the information from that map to deliver atmospheric and oceanic parameters from the satellite observation.</p> <p>The parameters of interest are the remote sensing reflectance spectra (Rrs(λ)) and the aerosol optical thickness (AOT) at 869 nm (aot_869). The Rrs at blue (443 and 488 nm) and green (547 nm) are used to calculate chlorophyll-a concentration from the OBPG OCx algorithm (chl_ocx, O'Reilly et al., 1998; Mobley et al., 2016).</p> <p>These geophysical parameters are projected onto a fixed grid at 1/96° resolution and archived in a daily netcdf format files. Each file contains five visible reflectances Rrs(λ) (with λ = 412, 443, 488, 531, and 547 nm), chl_ocx, aot_869, and latitude and longitude coordinates. These parameters are described in the files, along with the global attributes.</p> <p><br> The netcdf files are formatted as follows: SOM-NV-Ayyyydddhhmmss.nc; where yyyy = year; ddd = Julian<br> day; hh = hour; mm = minute; ss = second. The extension "Ayyyydddhhmmss.nc", corresponds to the name<br> of the MODIS_aqua file of the day. When two input files exist for the same day, within 5 minutes, the two<br> scans are concatenated and the orbit keeps the name of the second file.<br> All files are compressed internally to a size of 4, to facilitate transfers.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Résumé</p> <p>Ce jeu de données de couleur de l’eau est issu des mesures du capteur MODIS_Aqua sur la partie sud du système d’upwelling des Canaries. Les mesures brutes L1A ont été téléchargées du site Ocean Color de la NASA, puis traitées à l’aide du code de traitement « Multi-Sensor Level-1 to Level-2 (MSL12) » du groupe Ocean Biology Processing Group (OBPG). La version standard du programme l2gen génère les paramètres de niveau 2 constitués de la luminance totale mesurée, de la luminance de chaque composante du système océan-atmosphère, des angles de mesures, des masques de niveau 2, …. La luminance totale est pré-corrigée pour ne garder qu’une dépendance à la transmittance diffuse, à la contribution des aérosols et à la luminance marine.<br> <br> Le produit pré-corrigé et les angles de mesure sont assimilés à l’aide du code Self-Organizing Map NeuroVaria (SOM-NV) de Diouf et al. (2013). SOM-NV est un algorithme basé sur deux modèles statistiques qui permettent de classer un ensemble de données sur une carte, puis d’utiliser les informations de cette carte pour restituer les paramètres atmosphériques et océaniques de l’observation satellite.<br> <br> Les paramètres restitués sont les spectres de réflectance marine (Rrs(λ)) et l’épaisseur optique des aérosols (AOT) à 869 nm (aot_869). Les Rrs au bleu (443 et 488 nm) et au vert (547 nm) servent à calculer la concentration en chlorophylle-a à partir de l’algorithme OCx de OBPG (chl_ocx).<br> <br> Ces paramètres géophysiques sont projetés sur une grille fixe à 1/96° de résolution et archivés au format de fichiers netcdf journaliers. Chaque fichier netcdf contient cinq réflectances du visible Rrs(λ) (avec λ = 412, 443, 488, 531 et 547 nm), la chl_ocx, l’aot_869, et les coordonnées latitude et longitude. Ces paramètres sont décrits dans les fichiers, ainsi que les attributs globaux.</p> <p><br> Les fichiers netcdf sont formatés comme suite : SOM-NV-Ayyyydddhhmmss.nc ; avec yyyy = année ; ddd =<br> jour julien ; hh = heure ; mm = minute ; ss = seconde. L'extension "Ayyyydddhhmmss.nc", correspond au<br> nom du fichier MODIS_aqua du jour. Dans le cas où deux fichiers existent pour un même jour, à 5 minutes<br> près, les deux scans sont concaténés et l'orbite garde le nom du deuxième fichier.<br> Tous les fichiers sont compressés en interne à un niveau 4, pour faciliter le transfert.</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>
Data publication supplementing "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale"
<p>This data publication contains the results of nanoindentation tests on Fused silica, nanocrystalline nickel, a nanocrystalline FeCr alloy, a bulk metallic glass, the superplastic alloy Zn-22%Al and single crystalline aluminum as well as the method files developed for the G200 nanoindenter. It supplements the publication "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale". The materials are described in more detail in the respective publication. The data publication takes over the sample naming convention from the related publication. </p><p>Nanoindentation measurement were performed by H. Holz at the Max-Planck Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237 Düsseldorf, Germany using a G200 nanoindenter (KLA, Milpitas, CA, USA), equipped with a modified Berkovich diamond indenter tip of the type 171-561-500 with a serial number of C-0040446 from Synton MDP (Nidau, Switzerland). Constant strain rate tests, strain rate jump tests, strain rate sweep tests and strain rate sweep reversal tests were performed on each material in Continuous Stiffness Measurement (CSM) mode. The maximum indentation depth was 2200 nm, the CSM amplitude 2 nm and the CSM frequency 45 Hz. Strain rates were varied within the range 0.001 – 0.1 s-1. Further information on the test protocol can be found in the corresponding publication.</p><p>The subfolder "Nanoindentation data" contains the raw data for all valid indents as output by the NanoSuite © software v 7.1.7 and converted to the semicolon-separated format. The naming convention for the folder in which the CSV files are located in gives first the used material, then the method used with additional information to the parameters inputted for the method such as strain rate and indentation depth all separated by an underscore. An example can be "FS_CSR_01s-1" for a constant strain rate tests performed on fused silica with a strain rate target of 0.1 s-1 or "Nc-Ni_SweepReversal_005s-1_0005s-1" for a sweep reversal test performed on the nanocrystalline nickel sample with a targeted initial and ending strain rate of 0.05 s-1 and a strain rate target at which the strain rate direction gets reversed of 0.005 s-1. The CSV files are named either "Results" giving the average results of each test, "Required Inputs" giving information about the parameters used for the experiments, "Inputs Editable Post Test" giving information about the analysis parameters to obtain the results from, and "Test XXX" which include the Raw data of the corresponding test number. In each file the first row gives the data description e.g., "Time", the second row the physical unit e.g., "s" for seconds and from the third row the measured values.</p><p>The Nano Suite method files to perform the experiments on KLA G200 instruments is provided in the folder "G200 methods". The method for the strain rate sweep experiments is called "Strain Rate Sweep.msm" and the method for the strain rate sweep reversal experiments "Strain Rate Sweep Reversal.msm". This method is provided as is and shall be used at your own risk. The authors explicitly decline responsibility for any physical or immaterial damage resulting from the use of this method. Should minor issues occur, some feedback to the authors would be greatly appreciated.</p>
Data from “A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water”
Objectives We have approached the problem of low well water testing rates in Maine and New Hampshire communities by developing the All About Arsenic (AAA) project, which engages secondary school teachers and students as citizen scientists in collecting well water samples for analysis of arsenic and other toxic metals and supports their outreach efforts to their communities. Methods We assessed this project’s public health impact by analyzing student data relative to existing well water quality datasets in both states. In addition, we surveyed private well owners who contributed well water samples to the project to determine the actions taken to mitigate arsenic in well water. Data The data presented here are used in the analyses performed for the publication: "A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water.” Additional data may be available at: The Anecdata Project Page: https://anecdata.org/projects/view/299 The project website: https://www.allaboutarsenic.org/
Temperature logger deployment methods and irradiance-biased temperature data, King Abdullah University of Science and Technology, Red Sea, 2023.
Solar irradiance can offset the temperature recorded by underwater sensing instruments (aka "loggers"). We collected temperature and PAR (photosynthetic active radiation) data during two short-term in situ deployments on a shallow fringing reef adjacent to the King Abdullah University of Science and Technology (KAUST) in the Red Sea. The first deployment quantified the measurement bias due to solar heating over five days in February 2023 while the second compared the effect of different shading methods on logger performance over 24 hours in June 2023. We also recorded temperature in a controlled calibration bath in the lab with ten of the most widely used loggers to further assess their accuracy, response time, and intra-logger variation. Finally, to understand current practices of measuring temperature on coral reefs, we summarized logger deployment method details from a literature review of coral reef studies published from 2013 to 2022. Such details included how often loggers recorded the temperature, the depth where loggers were deployed, and whether the authors reported shading or protecting their loggers. This data package is complete and part of a larger project that aims to develop an instrument deployment framework for restoration-based reef monitoring, which includes instrument recommendations and deployment guidelines.
Sediment Elevation Change (Feldspar Marker Horizon Method) from Northeastern Florida Bay (FCE) from 1996 to Present
The long term goal of the South Florida Water Management District's research program focuses on the following central objective: Regional processes mediated by water flow control population and ecosystem level dynamics at any location within the coastal Everglades landscape. This phenomenon is best exemplified in the dynamics of an estuarine oligohaline zone where fresh water draining phosphorus-limited Everglades marshes mixes with water from the more nitrogen-limited coastal ocean. Data in this data package are specifically to monitor soil elevation change relative to current sea level.
Sediment Elevation Change (SET Method) from Northeastern Florida Bay (FCE) from 1996 to Present
The long term goal of the South Florida Water Management District's research program focuses on the following central objective: Regional processes mediated by water flow control population and ecosystem level dynamics at any location within the coastal Everglades landscape. This phenomenon is best exemplified in the dynamics of an estuarine oligohaline zone where fresh water draining phosphorus-limited Everglades marshes mixes with water from the more nitrogen-limited coastal ocean. Data in this data package are specifically to monitor soil elevation change relative to current sea level using the SET method. The Feldspar Marker Horizon method was used in conjunction with the SET data. The Feldspar data are in dataset FCE1213.
Percent cover of vegetation measured by the line-intercept method at multiple locations on long-term ecosystem transects (including nitrogen fertilization treatments) at Jornada Basin LTER, 1982-2014
This data package contains data on the average percent cover of plant species on three permanent LTER-I transects at the Chihuahuan Desert Rangeland Research Center (CDRRC) in southern New Mexico. Prior to becoming a livestock exclosure, the study site was moderately to heavily grazed for the past 100 years. The purpose of this study was to document the vegetation changes associated with the addition of nitrogen fertilizer following nearly a century of grazing. Three permanent, parallel transects (2.7 km in length) run from the middle of the College Playa up to the foot of Mt. Summerford. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. Each plant intercept line is perpendicular to a 2.7 km transect and is 30 meters in length broken into 5 meter segments. The included dataset gives calculated average percent cover of each plant species for each of these plant intercept lines on all three LTER-I permanent transects (91 lines each). Measurements were made biannually from 1982 - 1988. After this, they are measured every 5 years. Data consists of the date, week number, transect, station number, JRN species code, USDA Plants code, species binomial, photosynthetic pathway, habit, form, and average percent cover. This study is ongoing.
Jornada Basin and Experimental Range Mesquite Herbicide Project (JERHM) Core Methods Data, 2020-2022
This dataset includes contains line-point intercept, plant height, gap, and species inventory data collected over three years (2020-2022) as part of the Jornada Experimental Range Herbicide Mesquite Project (JERHM). Data were collected to assess plant community composition and structural change to herbicide application across a Black grama (Bouteloua eriopoda) grassland to Honey mesquite (Neltuma glandulosa [=Prosopis glandulosa]) shrubland encroachment gradient. Twenty sets of paired, 5-hectare plots (n=40 plots total) were established across a N. glandulosa encroachment gradient in 2020. One plot within each plot pair received an aerial application of herbicide in 2021, with the second plot left untreated by herbicide as a control. Data were collected annually following the following the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems (Herrick et al. 2017) on each of three, 50m permanent transects established on each plot. These data are also available within the Landscape Data Commons (https://landscapedatacommons.org/) under ProjectKey=Jornada_JERHM. There are no immediate plans to continue data collection.
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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.