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69 results for “data harmonization”
SOils DAta Harmonization database (SoDaH): an open-source synthesis of soil data from research networks
This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.
Spherical harmonic model of the Moon's magnetic field derived from gridded data in Tsunakawa et al. (2015)
<p><strong>T2015_449</strong> is a 449 degree and order spherical harmonic model of the magnetic potential of the Moon. This model was used in Wieczorek (2018) and is a spherical harmonic expansion of the global magnetic field model of Tsunakawa et al. (2015). The original gridded data are from the file "globalSVM20150511/LunarSVM_000_02_v01.dat" and the spherical harmonic coefficients use the standard Schmidt semi-normalization, excluding the Condon-Shortley phase factor of (-1)<sup>m</sup>. The coefficients are in units of Teslas.</p>
Data set accompanying: Topology of the Warm plasma dispersion relation at the second Harmonic Electron Cyclotron Resonance Layer
<p>The Warm Plasma Dispersion Relation, for waves in the electron cyclotron resonance range of frequencies, can be cast into the form of a bi-quadratic equation for $N_\perp$, where the coefficients are a function of $N_\perp^2$ and an iterative procedure is required to obtain a solution. However, this iterative procedure is not well understood and fails to converge towards a solution at the second harmonic resonance layer. In particular at higher densities where the wave can couple to an electron Bernstein wave.<br> This paper focuses on a solution to the poor convergence of the iterative method, enabling determination of the topology of the dispersion relation around the second harmonic using a fully relativistic code for oblique waves.<br> A feed-forward controller is proposed with the ability to adjust the rotation of a step of $N_\perp^2$ within the complex plane, while also limiting the step-size.<br> It is shown that implementation of the controller stabilizes unstable solutions, while improving overall robustness of the iteration. This allows the evaluation of the coupling between the fast extraordinary mode and electron Bernstein waves at the second harmonic electron cyclotron resonance layer, for non-perpendicularly propagating waves.</p>
Data from: Integration and harmonization of trait data from plant individuals across heterogeneous sources
<p>Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.</p>
Supporting Data for: A Multimer Embedding Approach for Molecular Crystals up to Harmonic Vibrational Properties
<p>Accurate calculations of molecular crystals are crucial for drug design and crystal engineering. However, periodic high-level density functional calculations using hybrid functionals are often prohibitively expensive for relevant systems. These expensive periodic calculations can be circumvented by the usage of embedding methods in which for instance the periodic calculation is only performed at a lower-cost level and then monomer energies and dimer interactions are replaced by those of the higher-level method. Herein, we extend upon such a multimer embedding approach to enable energy corrections for trimer interactions and the calculation of harmonic vibrational properties up to the dimer level. We evaluate this approach for the X23 benchmark set of molecular crystals by approximating a periodic hybrid density functional (PBE0+MBD) by embedding multimers into less expensive calculations using a generalized-gradient approximation (GGA) functional (PBE+MBD). We show that trimer interactions are crucial for accurately approximating lattice energies within 1 kJ/mol and might also be needed for further improvement of lattice constants and hence cell volumes. Finally, vibrational properties are already very well captured at the monomer and dimer level, making it possible to approximate vibrational free energies at room temperature within 1 kJ/mol.</p><p>This supporting dataset includes results of PBE0+MBD, PBE+MBD, and multimer embedding calculations for the X23 set of molecular crystals. See the included README.md file for more details. The related preprint can be found at <a href="https://doi.org/10.48550/arXiv.2209.02687">https://doi.org/10.48550/arXiv.2209.02687</a>.</p>
Harmonized data and R code for "Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum"
<p>Harmonized data and R code for "<em>Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum</em>"<br>by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Karl-Heinz Baumann, Helmut Hillebrand and Michal Kucera (submitted to <em>Global Ecology and Biogeography</em>, 2024).</p> <p><strong>STRUCTURED ABSTRACT</strong><br><em>Aim</em>: We use the fossil record of different marine plankton groups to determine how their biodiversity changed during past climate warming comparable to projected future warming.<br><em>Location</em>: North Atlantic Ocean and adjacent seas. Time series cover a latitudinal range of 75°N to 6°S.<br>Time period: Past 24,000 years, i.e., from the Last Glacial Maximum (LGM) to the current warm period covering the last deglaciation.<br><em>Major taxa studied</em>: Planktonic foraminifera, dinoflagellates and coccolithophores.<br><em>Methods</em>: We analyse time series of fossil plankton communities using principal component analysis and generalised additive models to estimate the overall trend of temporal compositional change in each plankton group and identify periods of significant change. We further analyse local biodiversity change by analysing species richness, species gains and losses, and the effective number of species in each sample and compare alpha diversity to the LGM mean.<br><em>Results</em>: All plankton groups show remarkably similar trends in the rates and spatio-temporal dynamics of local biodiversity change and a pronounced non-linearity with climate change in the current warm period. Assemblages of planktonic foraminifera and dinoflagellates started to significantly change with the onset of global warming around 15,500 to 17,000 years ago and continued to change at the same pace during the current warm period until at least 5,000 years ago, while coccolithophores assemblages changed at a constant rate throughout the past 24,000 years seemingly irrespective of the prevailing temperature change.<br><em>Main conclusions</em>: The climate change during the transition from the LGM to the current warm period led to a long-lasting reshuffling of the zoo- and phytoplankton assemblages likely associated with the emergence of new ecological interactions and possibly a shift in the dominant drivers of plankton assemblage change from more abiotic-dominated causes during the last deglaciation to more biotic-dominated causes with the onset of the Holocene.</p> <p><strong>CONTENT</strong><br>This dataset includes the harmonized assemblage data of the three investigated plankton groups (planktonic foraminifera, dinoflagellates and coccolithophores) as well as all the R code needed to re-produce the results of this study and it's main figures.</p> <p>Scripts written by Tonke Strack</p> <p><br><strong>DATA SOURCES</strong><br>1) GMST: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. <br> <em>Nature</em> 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>2) WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, <em>Technical Editor. </em><br><em> NOAA Atlas NESDIS</em> 81, 52 (2019).<br>3) plankton assemblage data: individual data references provided in CoreList.csv</p> <p><br><strong>DATA</strong><br>1. Harmonized assemblage data<strong>*</strong>: <em>FullDataTable_PF_harmonized.txt</em><br>2. Core list of additional information on time series: <em>CoreList.csv</em><br>3. Reference lists for species names names: <em>ReferenceList_PlanktonicForaminifera.csv, ReferenceList_Dino.csv, ReferenceList_Cocco.csv</em></p> <p><br><strong>CODE</strong><br>1. <em>01_LoadData.R</em>: loads harmonized assemblage data from planktonic foraminifera, dinocyst and coccolithophores<br>2. <em>02_GMST_import.R</em>: loads loads the globally resolved surface temperature since the LGM from Osman et al. (2011)<br>3. <em>03_DataAnalysis_PCA_GAM.R</em>: PCA/GAM analysis on the plankton assemblage data (results shown in Figure 2 and 3), sensititvity analysis (results shown in Figure 4), and some summary statistics<br>4. <em>04_DataAnalysis_MH_GAM_AlternativeApproach.R</em>: alternative GAM approach using Morisita-Horn index (results shown in Figure S2, S3 and S4)<br>5. <em>05_DataAnalysis_BiodiversityChange.R</em>: local biodiversity change analysis of individual time series (results shown in Figure 5, 6 and S9)</p> <p><br>*Assemblage data of individual time series were manually downloaded, quality checked, taxonomically harmonized, and combined into one data file.<br>Planktonic foraminifera data were harmonized following Siccha and Kuchera (2017). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber </em><br><em>albus</em>, because some studies only reported them together as<em> Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological<br>intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>.<br>Dinocyst taxonomy was harmonized following de Vernal et al. (2020) with slight additions following Zonneveld et al. (2013). Names that could not be<br>resolved using synonym lists and assigned a harmonized name following de Vernal et al. (2020) and Zonneveld et al. (2013) were treated as unidentified<br>specimens and were excluded from the assemblage analyses. These specimens were present in 4 time series and were rare taxa (relative abundances < 3%).<br>The protoperidinoids were also excluded from further assemblage analyses as this category includes all unidentified brownish cysts (de Vernal et al., 2020).<br>Coccolithophore taxonomy follows Young et al. (2003) and coccolith countings were conducted on a scanning-electron microscope (SEM) to ensure that all<br>specimens are resolved to the species level. We merged <em>Coccolithus pelagicus</em> subspecies, because they were not distinguished in all studies. <br>Species not reported in the time series data were assumed to be absent (that is, zero abundance) which is in accordance with the completeness of the counts<br>reported in the original studies. The original data were either given in absolute or relative abundances, and after excluding unnecessary columns<br>(unidentified or rare taxa that could not be harmonised) the abundances were recalculated to 100 %. In total, 41 species of planktonic foraminifera,<br>30 species of coccolithophores and 53 species of organic-walled dinocysts were observed in our study.</p> <p><strong>REFERENCES</strong><br>de Vernal, A., Radi, T., Zaragosi, S., Van Nieuwenhove, N., Rochon, A., Allan, E., . . . Richerol, T. (2020). Distribution of common modern dinoflagellate cyst taxa in surface sediments of the Northern Hemisphere in relation to environmental parameters: The new n=1968 database. <em>Mar. Micropaleontol.</em>, 159. doi:10.1016/j.marmicro.2019.101796<br>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. S<em>ci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br>Young, J. R., Geisen, M., Cros, L., Kleijne, A., Sprengel, C., Probert, I., & Østergaard, J. B. (2003). A guide to extant coccolithophore taxonomy. <em>Journal of Nannoplankton Research Special Issue</em>, 1, 1-125. doi:10.58998/jnr2297<br>Zonneveld, K. A. F., Marret, F., Versteegh, G. J. M., Bogus, K., Bonnet, S., Bouimetarhan, I., . . . Young, M. (2013). Atlas of modern dinoflagellate cyst distribution based on 2405 data points. <em>Rev. Palaeobot. Palynol.</em>, 191, 1-197. doi:10.1016/j.revpalbo.2012.08.003</p>
Data referring to the Article "Harmonized Skies: A Survey on Drone Acceptance across Europe" published in Drones Journal
<p><span>The material provided is part of the article "Harmonized Skies: A Survey on Drone Acceptance across Europe," published in the Drones Journal (https://doi.org/10.3390/drones8030107). This article describes a study investigating civil drone acceptance in six different EU countries. This study was part of the USpace4UAM project (Grant Agreement No 101017643). The material includes the data set for the study described and the Python codes for the random forest analysis carried out to investigate the influence of demographic and personnel factors on drone acceptance.</span></p>
Harmonized INSEE socio-demographic IRIS-level data and IRIS conversion file (2010-2020)
<p>The smallest level of aggregation for sociodemographic data made publicly available by the French INSEE is the IRIS. Data is downloadable on INSEE websites but decomposed by type of variable and by year. I thus combined all datasets into a single homogenous dataset for practicality. It includes on the period 2010-2020 :</p> <ul> <li>Population structure (age, gender...) data</li> <li>Economic occupation CSP data</li> <li>Available income data</li> <li>Family data</li> </ul> <p>Since a sizeable amount of IRIS change each year, timewise comparisons are limited. I therefore created a conversion file. Each IRIS is expressed as a % combination of previous IRIS. This allows to track IRIS merge, IRIS split and border changes. Measurement errors linked to border overlap were detected when the overlap of 2 IRIS was less than 1 percent. The IRIS overlap without the measurement error is the variable _ajuste (pardon my French). This allows to express the IRIS of a year as the wieghted sum of the IRIS of any other given year.</p> <p>You will also find the codes I used to create each of the files included in this project on my github. The annotation may be lacking, it is currently being improved. </p>
Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation"
<p>This package contains all required scripts to generate the simulations and figures from the study "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation" by Volker Hohmann, published in Acta Acustica:</p> <p>The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation<br>Volker Hohmann<br>Acta Acust. 5 56 (2021)<br>DOI: 10.1051/aacus/2021050</p> <p>When referring to this work, please cite the journal paper.</p> <p>Note that additive noise is generated at random, i.e., small differences in the estimation accuracy occur when repeating a simulation. For further details see the journal paper.</p> <p>Thank you for downloading the package. Your comments are very welcome!</p> <p>Method patented: DE Patent DE102021207339B3</p> <p>Author: Volker Hohmann, Carl von Ossietzky Universität Oldenburg, Germany</p>
Dataset and data processing tools of EPL 139 (2022) 55002 — Harmonic calibration of quadrature phase interferometry
<p>Dataset for <em>EPL</em> <strong>139</strong> (2022) 55002 — Harmonic calibration of quadrature phase interferometry</p> <p><strong>Scripts: </strong>the main Matlab script to analyse all data is <strong>analyseall.m</strong>, and uses all other scripts and functions (<strong>*.m</strong>). See comments in this main script for details. The script <strong>DefineDatasets.m,</strong> and its output <strong>Dataset.mat</strong>, were run before to define the main parameters of the 20 datasets, and define the frequency range where to estimate the background noise to be subtracted from the spectra of the driven cantilever when computing the total harmonic distortion.</p> <p><strong>Data:</strong> the 100 data files (.mat) have the following name scheme: [driving-frequency]-[Amplitude]-(low frequency external phase)-001 to 005.mat. For example 1kHz-10nm-w1Hz150nm-001.mat correspond to a driving of the cantilever at 1kHz, with an oscillation amplitude close to 10nm, with a low frequency driving of the external optical phase at 1Hz and equivalent 150nm amplitude. 001 stand for the first of the five files corresponding to this measurement. Note that the low frequency driving is optional. The main driving frequency is either directly given in kHz, or corresponds to one of the 2 first resonance modes of the cantilever (mode1 at 13.4kHz, and mode2approx at 84.6 kHz). Each of these data files contains 5 variables: Cx and Cy are the 2 outputs of the quadrature phase interferometer (1s of acquisition, 2e6 samples), fs is the sampling frequency (2 MHz), Ix and Iy are the mean total intensities on the photodiodes corresponding to Cx and Cy (units: A, this information can be useful to estimate the expected shot noise floor on the measurement).</p>
Data from: Manipulation of Miniature and Microminiature Bodies on a Harmonically Oscillating Platform by Controlling Dry Friction
<p>Data from the paper "Manipulation of Miniature and Microminiature Bodies on a Harmonically Oscillating Platform by Controlling Dry Friction" <a href="https://doi.org/10.3390/mi12091087">https://doi.org/10.3390/mi12091087</a></p> <p>Currently used nonprehensile manipulation systems that are based on vibrational techniques employ temporal (vibrational) asymmetry, spatial asymmetry, or force asymmetry to provide and control a directional motion of a body. This paper presents a novel method of nonprehensile manipulation of miniature and microminiature bodies on a harmonically oscillating platform by creating a frictional asymmetry through dynamic dry friction control. To theoretically verify the feasibility of the method and to determine the control parameters that define the motion characteristics, a mathematical model was developed, and modeling was carried out. Experimental setups for miniature and microminiature bodies were developed for nonprehensile manipulation by dry friction control, and manipulation experiments were carried out to experimentally verify the feasibility of the proposed method and theoretical findings. By revealing how characteristic control parameters influence the direction and velocity, the modeling results theoretically verified the feasibility of the proposed method. The experimental investigation verified that the proposed method is technically feasible and can be applied in practice, as well as confirmed the theoretical findings that the velocity and direction of the body can be controlled by changing the parameters of the function for dynamic dry friction control. The presented research enriches the classical theories of manipulation methods on vibrating plates and platforms, as well as the presented results, are relevant for industries dealing with feeding, assembling, or manipulation of miniature and microminiature bodies.</p>
Data for Human sperm steer with second harmonics of the flagellar beat
<p>Files containing all data processed for the article “Human sperm steer with second harmonics of the flagellar beat” by Guglielmo Saggiorato, Luis Alvarez, Jan F. Jikeli, U. Benjamin Kaupp, Gerhard Gompper, and Jens Elgeti</p> <p><br> collected_data.zip<br> compressed containing one folder per experiment. Each folder features the raw data (original movie) and the corresponding flagellar parameters measured: trajectory and curvature. </p> <p>movie.tbz2<br> files to make the movie comparing original sperm recording and simulation</p> <p>steer_with_phase.bz2<br> xyz file of Supplementary Movie 2 where sperm steers with the second-harmonic phase</p> <p>analysis_scripts.zip contains<br> scripts used for data analysis: curvature.py, pma.py, and spectrogram.py </p> <p>compare_2nd_harmonic_to_average_curvature.tar.bz2<br> files comparing the contribution of the 2nd harmonic and average curvature</p> <p> </p>
Data set: A solid-state high harmonic generation spectrometer with cryogenic cooling
<p>Solid-state high harmonic generation (sHHG) spectroscopy is a promising technique for studying electronic structure, symmetry, and dynamics in condensed matter systems. Here, we report on the implementation of an advanced sHHG spectrometer based on a vacuum chamber and closed-cycle helium cryostat. Using an in situ temperature probe, it is demonstrated that the sample interaction region retains cryogenic temperature during the application of high-intensity femtosecond laser pulses that generate high harmonics. The presented implementation opens the door for temperature-dependent sHHG measurements down to a few Kelvin, which makes sHHG spectroscopy a new tool for studying phases of matter that emerge at low temperatures, which is particularly interesting for highly correlated materials.</p>
Raw Data for "Synthesis and functionalisation of biodegradable Second Harmonic Generation nanoprobes for cell targeting"
<p>Raw data files used in the "Synthesis and functionalisation of biodegradable Second Harmonic Generation nanoprobes for cell targeting" manuscript.</p>
Data for: A harmonized database of European forest simulations under climate change
<p>This repository contains the database presented in the publication "A harmonized database of European forest simulations under climate change". It contains a collection of harmonized forest simulation model outputs from 17 different models covering 1.1 million individual simulation runs, over 136 million simulation years across over 13,599 unique locations in Europe.</p> <p>Detailed description can be found in the publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110384" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110384</a>). The file "forest_simulation_db_v1.7z" contains all raw simulation outputs and a metadata table of all simulations including information about locations and harmonized soil conditions for those locations. Simulation outputs with harmonized climate data are stored in one SQLite database per climate scenario.</p> <p>The code that was used to create the database, as well as to access and explore the data can be found here: https://github.com/magrueni/forest_simulation_database.git</p> <p>Note: Please be cautious with the use of the simulations with unique identifiers 1037-1047. There were some abrupt species compositions changes reported that suggest that in a small number of the original simulations there was an underlying issue in the compilation of the raw simulation data.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p> <p>Unfortunately we found an bug in the daily climate extraction process of the previous version, leading to inconsistencies in the harmonized climate data. We corrected the harmonized daily climate data for all scenarios. Additionally, the LAI values in the raw data of the simulations with the unique identifier 1016 were calculated wrongly and therefore corrected in this version. Please use the updated version for all analyses. We apologize for any inconveniences.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p>
Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization
<h2><strong>INTRODUCTION</strong></h2> <p>This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results.</p> <p><strong>Keywords:</strong> Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization</p> <p> </p> <h2><strong>OBJECTIVES</strong></h2> <ul> <li> <p>Create a global inventory of existing reference samples for land use and land cover (LULC);</p> </li> <li> <p>Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes;</p> </li> <li> <p>Develop automated scripts for data harmonization and integration.</p> </li> </ul> <p> </p> <h2><strong>DATA COLLECTION </strong></h2> <p>Datasets incorporated:</p> <table> <tbody> <tr> <td><strong>Datasets</strong></td> <td> <p><strong>Spatial distribution</strong></p> </td> <td><strong>Time period</strong></td> <td><strong>Number of individual samples</strong></td> </tr> <tr> <td>WorldCereal</td> <td>Global</td> <td>2016-2021</td> <td>38,267,911</td> </tr> <tr> <td>Global Land Cover Mapping and Estimation (GLanCE)</td> <td>Global</td> <td>1985-2021</td> <td>31,061,694</td> </tr> <tr> <td>EuroCrops</td> <td>Europe</td> <td>2015-2022</td> <td>14,742,648</td> </tr> <tr> <td>GeoWiki G-GLOPS training dataset</td> <td>Global</td> <td>2021</td> <td>11,394,623</td> </tr> <tr> <td>MapBiomas Brazil</td> <td>Brazil</td> <td>1985-2018</td> <td>3,234,370</td> </tr> <tr> <td>Land Use/Land Cover<br>Area Frame Survey (LUCAS)</td> <td>Europe</td> <td>2006-2018</td> <td>1,351,293</td> </tr> <tr> <td>Dynamic World</td> <td>Global</td> <td>2019-2020</td> <td>1,249,983</td> </tr> <tr> <td>Land Change Monitoring,<br>Assessment, and Projection (LCMap)</td> <td>U.S. (CONUS)</td> <td>1984-2018</td> <td>874,836</td> </tr> <tr> <td>GeoWiki 2012</td> <td>Global</td> <td>2011-2012</td> <td>151,942</td> </tr> <tr> <td>PREDICTS</td> <td>Global</td> <td>1984-2013</td> <td>16,627</td> </tr> <tr> <td>CropHarvest</td> <td>Global</td> <td>2018-2021</td> <td>9,714</td> </tr> </tbody> </table> <p><strong>Total:</strong> 102,355,642 samples</p> <p> </p> <h2><strong>WORKFLOW</strong></h2> <h3><strong>Harmonization Process</strong></h3> <p>We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow.</p> <p>We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region.</p> <p>Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:</p> <p> 0. Other land cover</p> <p> 1. Natural and Semi-natural grassland</p> <p> 2. Cultivated grassland</p> <p> 3. Crops and other related agricultural practices</p> <p>We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes.</p> <p>The harmonized dataset includes these columns:</p> <table> <tbody> <tr> <td><strong>Attribute Name</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>dataset_name</td> <td>Original dataset name</td> </tr> <tr> <td>reference_year</td> <td>Reference year of samples from the original dataset</td> </tr> <tr> <td>original_lulc_class</td> <td>LULC class from the original dataset</td> </tr> <tr> <td>gpw_lulc_class</td> <td>Global Pasture Watch LULC class</td> </tr> <tr> <td>sample_weight</td> <td>Sample's weight based on the mixture level within the original LULC class</td> </tr> </tbody> </table> <p> </p> <h2><strong>ACKNOWLEDGMENTS</strong></h2> <p>The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.</p> <p> </p> <h2><strong>REFERENCES</strong></h2> <ul> <li> <p>Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023)</p> </li> <li> <p>Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963</p> </li> <li> <p>d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020)</p> </li> <li> <p>Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012)</p> </li> <li> <p>Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017)</p> </li> <li> <p>Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023)</p> </li> <li> <p>Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020)</p> </li> <li> <p>Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023) </p> </li> <li>Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).</li> <li> <p>Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021).</p> </li> <li>Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.</li> </ul>
WorldCereal open global harmonized reference data repository (CC-BY-NC licensed data sets)
<p>Within the <strong>ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY-NC license or a license similar to CC-BY-NC. See document "_In-situ-data-World-Cereal - license - CC-BY-NC.pdf" for an overview of the original data sets. Currently this publication only includes a few small data sets for Tanzania originating from a disease monitoring program of the International Maize and Wheat Improvement Center (CIMMYT). CIMMYT made more data available for countries like Kenya, Ethiopia, Rwanda and, Malawi. However due project contraints these data sets were not yet harmonized.</p>
Comparing zero-parameter theories for the WCA and harmonic-repulsive melting lines (data)
<p>This dataset contains data related to the paper titled "Comparing zero-parameter theories for the WCA and harmonic-repulsive melting lines" by Jeppe C. Dyre and Ulf R. Pedersen, with the abstract:</p> <p>The melting line of the Weeks-Chandler-Andersen (WCA) system was recently determined accurately and compared to the predictions of four analytical hard-sphere approximations [Attia \textit{et al.}, J. Chem. Phys. \textbf{157}, 034502 (2022)]. Here, we study an alternative zero-parameter prediction based on the isomorph theory, the input of which relate to properties at a single reference state point on the melting line. The two central assumptions made are that the harmonic-repulsive potential approximates the WCA potential and that pair collisions are uncorrelated. The new approach gives excellent predictions at high temperatures, while the hard-sphere-theory based predictions are better at lower temperatures. Supplementing the WCA investigation, the face-centered-crystal to fluid coexistence line is determined for a system of harmonic-repulsive particles and compared to the zero-parameter theories. The results indicate that the excellent isomorph-theory predictions for the WCA potential at higher temperatures may be partly due to a cancellation of errors between the two above-mentioned assumptions.</p>
Data set: A solid-state high harmonic generation spectrometer with cryogenic cooling
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Data from: Solid-state high harmonic generation in common large bandgap substrate materials
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