cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21502575
title: Quantitative Analysis

description: The file dataset_wetseb2022_main.xlsx contains the main dataset on the first sheet. Each additional sheet focus on one of language contructs we analyzed (call, transfer, send, require, assert, revert). Every sheet has formulas calculating metrics. <br> In the file dataset_wetseb_contracts_by_version.xlsx, we separated the sheets by the major Solidity version.
DOI: 10.6084/m9.figshare.21502575.v1
title: Quantitative Analysis

description: The file dataset_wetseb2022_main.xlsx contains the main dataset on the first sheet. Each additional sheet focus on one of language contructs we analyzed (call, transfer, send, require, assert, revert). Every sheet has formulas calculating metrics. <br> In the file dataset_wetseb_contracts_by_version.xlsx, we separated the sheets by the major Solidity version.
DOI: 10.48670/moi-00231
title: Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis
DOI: 10.48670/moi-00232
title: Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis
DOI: 10.6084/m9.figshare.21376068
title: In-situ Sr isotope disequilibrium in plagioclases from Late Cenozoic basalts in Leiqiong Area: Evidence for the role of the Hainan Plume and mantle metasomatism due to a paleo-subduction event

description: Analytical Method (Supplementary I), Geochemical Data (Tables S1-S6), and Supplementary Figures (Figures S1-S7) <br>
DOI: 10.6084/m9.figshare.21376068.v2
title: In-situ Sr isotope disequilibrium in plagioclases from Late Cenozoic basalts in Leiqiong Area: Evidence for the role of the Hainan Plume and mantle metasomatism due to a paleo-subduction event

description: Analytical Method (Supplementary I), Geochemical Data (Tables S1-S6), and Supplementary Figures (Figures S1-S7) <br>
DOI: 10.6084/m9.figshare.21376068.v1
title: In-situ Sr isotope disequilibrium in plagioclases from Late Cenozoic basalts in Leiqiong Area: Evidence for the role of the Hainan Plume and mantle metasomatism due to a paleo-subduction event

description: <strong>Table S1</strong> Major-element (wt.%) and trace-element (ppm) concentrations of olivines of Middle Pleistocene (ZJ-19, ZJ-51, ZJ-56, and ZJ-62) and Late Pleistocene (ZJ-2, ZJ-29, ZJ-33, and ZJ-66) Leiqiong volcanic rocks. <strong>Table S2</strong> Major-element (wt.%) and trace-element (ppm) concentrations of clinopyroxenes of Middle Pleistocene (ZJ-40 and ZJ-43) and Late Pleistocene (ZJ-23) Leiqiong volcanic rocks. <strong>Table S3</strong> Major-element (wt.%) and trace-element (ppm) concentrations of plagioclases of Middle Pleistocene (ZJ-15, ZJ-43, and ZJ-44) and Late Pleistocene (ZJ-29, ZJ-33, and ZJ-66) Leiqiong volcanic rocks. <strong>Table S4</strong> The trace element values of standard samples during the mineral analyzing process. <strong>Table S5</strong> <em>In-situ</em> Sr isotopic ratios of plagioclases of Middle Pleistocene (ZJ-15, ZJ-43, and ZJ-44) and Late Pleistocene (ZJ-29) Leiqiong volcanic rocks.
DOI: 10.6084/m9.figshare.21502416
title: Data from the publication: "Age and socio-economic status affect dengue and COVID-19 incidence: Spatio-temporal analysis of the 2020 syndemic in Buenos Aires City"

description: Dataset for the joint analysis of dengue and COVID-19 syndemic in Buenos Aires Authonomous City (Argentina), from epidemiological week 1 to 22, 2020. Each row represents a census radius (the minimal census unit by INDEC).
DOI: 10.6084/m9.figshare.21502416.v1
title: Data from the publication: "Age and socio-economic status affect dengue and COVID-19 incidence: Spatio-temporal analysis of the 2020 syndemic in Buenos Aires City"

description: Dataset for the joint analysis of dengue and COVID-19 syndemic in Buenos Aires Authonomous City (Argentina), from epidemiological week 1 to 22, 2020. Each row represents a census radius (the minimal census unit by INDEC).
DOI: 10.48670/moi-00242
title: Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing
DOI: 10.48670/moi-00243
title: Global Ocean Sea Surface Temperature trend map from Observations Reprocessing
DOI: 10.57745/rize9g
title: LBCGC_ESDAC_N.tab

description: :unav
DOI: 10.57745/jibkvv
title: Meta_LBCGC_ESDAC.csv

description: :unav
DOI: 10.21957/ts4y-fn59
title: IFS nextGEMS cycle 2 9km 1-year simulation, deep off
DOI: 10.6084/m9.figshare.21494100.v1
title: Data_Tb_CIMR.nc

description: Brightness Temperature (TB) for CIMR-like frequencies. <br> TB at 1.4 come from SMAP TB at 6, 10, 18, 36 GHz come from AMSR2. <br> All data have been projected on the EASEGrid 2.0 at 12.5 km resolution and averaged on a trimonthly basis. <br> Temporal window is 2018-11 to 2019-04. <br>
DOI: 10.6084/m9.figshare.21494100
title: Data_Tb_CIMR.nc

description: Brightness Temperature (TB) for CIMR-like frequencies. <br> TB at 1.4 come from SMAP TB at 6, 10, 18, 36 GHz come from AMSR2. <br> All data have been projected on the EASEGrid 2.0 at 12.5 km resolution and averaged on a trimonthly basis. <br> Temporal window is 2018-11 to 2019-04. <br>
DOI: 10.4121/19168442
title: Data supplement to: A tsunami generated by a strike-slip event: constraints from GPS and SAR data on the 2018 Palu earthquake

description: This data set contains input data and modelling results for the paper: A tsunami generated by a strike-slip event: constraints from GPS and SAR data on the 2018 Palu earthquake, published in Journal of Geophysical Research: Solid Earth. <br> This set contains the following data and model results: <br> - <em><strong>GPSrepo.zip</strong></em>: co-seismic displacement estimates of the 28 September 2018 Palu earthquake; kinematic GPS positions before and after the earthquake; and raw GPS data in RINEX format. <br> - <em><strong>SAR_displacements.zip</strong></em>: co-seismic displacement fields for the 28 September 2018 Palu earthquake, using SAR offsets, Multiple Aperture Interferometry (MAI) and InSAR. Includes incidence and azimuth angles, and additional information on the orbits. <br> - <em><strong>bathymetry.zip</strong></em>: contains grid of bathymetry and topography as used in the study. It encompasses an area with longitude range [118.5 121.5] and longitude range [-2 1]. <br> - <em><strong>3d_displacements.zip</strong></em>: co-seismic 3D displacement estimates of the 28 September 2018 Palu earthquake on a triangular grid based on GPS and SAR data. Also includes gridded input data. <br> - <em><strong>optical_data_polygons_inundation.zip</strong></em>: shapefiles of the outlines of inundated coastal areas of Palu Bay after the 28 September 2018 Palu earthquake and tsunami, based on optical data. <br> - <em><strong>finite_fault_models.zip</strong></em>: Finite fault solutions for scenarios I-VI as described in the paper, including the preferred scenario IV, which best describes the tsunami observations. <br> - <em><strong>Vertical_Displacements_Bay_scenario_IV.zip</strong></em>: Vertical displacements from preferred scenario IV as described in the paper for the Palu Bay region. Includes vertical displacements, and separately the effect of horizontal displacements on vertical displacements, as well as the full vertical displacement combining both. <br> <br> <br> Each separate archive contains its own README file that describes the contents of the archive. More detailed information on the derivation of (model) results can be found in the accompanying paper and its supporting information.<br> <br> <br>
DOI: 10.4121/12923897.v1
title: Underlying data, figures and scripts of the publication: Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling

description: This online resource contains raw dataset, MATLAB scripts for some kinematic wake models, and underlying data for the rticle under revision, "Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling"
DOI: 10.4121/12923897.v2
title: Underlying data, figures and scripts of the publication: Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling

description: This online resource contains raw dataset, MATLAB scripts for some kinematic wake models, and underlying data for the rticle under revision, "Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling"
DOI: 10.4121/12923897.v3
title: Underlying data, figures and scripts of the publication: Wake Scaling of Actuator Discs in Different Aspect Ratios

description: This online resource contains raw dataset, MATLAB scripts for some kinematic wake models, and underlying data for the rticle under revision, "Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling"
DOI: 10.4121/12923897.v4
title: Underlying data, figures and scripts of the publication: Wake Scaling of Actuator Discs in Different Aspect Ratios

description: This online resource contains raw dataset, MATLAB scripts for some kinematic wake models, and underlying data for the rticle under revision, "Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling"
DOI: 10.4121/12923897
title: Underlying data, figures and scripts of the publication: Wake Scaling of Actuator Discs in Different Aspect Ratios

description: This online resource contains raw dataset, MATLAB scripts for some kinematic wake models, and underlying data for the rticle under revision, "Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling"
DOI: 10.4121/12923897.v5
title: Underlying data, figures and scripts of the publication: Wake Scaling of Actuator Discs in Different Aspect Ratios

description: This online resource contains raw dataset, MATLAB scripts for some kinematic wake models, and underlying data for the rticle under revision, "Actuator disc diameter formulation for vertical-axis wind turbine kinematic wake modelling"
DOI: 10.4121/21215924.v1
title: Dataset belonging to the paper: Ensemble based flow field estimation using the dynamic wind farm model FLORIDyn

description: Dataset belonging to the paper: Ensemble based flow field estimation using the dynamic wind farm model FLORIDyn The dataset contains the some of the raw data and additional data used in the paper. The data is meant to allow others to compare their results more easily to the presented ones. Most files are given as .csv files, some are dependent on Matlab (.fig files). The dataset also conains animations, which could not be conveyed in a paper-format and information about the FLORIDyn-as-a-reference simulation case. <br> More detailed information can be found in the README.md file. <br>
DOI: 10.4121/20026406
title: SOWFA simulation setup belonging to the paper: The revised FLORIDyn model: Implementation of heterogeneous flow and the Gaussian wake

description: SOWFA setup for a nine turbine wind farm in turbulent wind during a wind direction change. This simulation is used as a preliminary indicator on how wakes might behave during changing environmental conditions. The turbines are set up in a 3x3 grid with the initial wind direction coming from 255 deg and then uniformly shifting by -60 deg to 195 deg in a 300 s time frame (from 20600 s to 20900 s). All turbines are 10 MW DTU reference turbines, simulated with an actuator disc. <br> For more details see the relevant files. OpenFOAM 2.4 was used to run the simulation. Content The data set contains the relevant in- and outputs from the simulation and its precursor. This should be sufficient to validate the plots from the related publications and to understand how the simulation was set up. Naturally, you will need to adapt local paths to your installation if you want to rerun the simulation. Note Due to a known bug in SOWFA, the power generated is multiplied twice with the air density. Therefore, to get the actual power generated, divide by 1.225.
DOI: 10.4121/21215924
title: Dataset belonging to the paper: Ensemble based flow field estimation using the dynamic wind farm model FLORIDyn

description: Dataset belonging to the paper: Ensemble based flow field estimation using the dynamic wind farm model FLORIDyn The dataset contains the some of the raw data and additional data used in the paper. The data is meant to allow others to compare their results more easily to the presented ones. Most files are given as .csv files, some are dependent on Matlab (.fig files). The dataset also conains animations, which could not be conveyed in a paper-format and information about the FLORIDyn-as-a-reference simulation case. <br> More detailed information can be found in the README.md file. <br>
DOI: 10.4121/21285939.v1
title: Experimental data of The effect of building geometry on the size of aeolian deposition patterns: scale model experiments at the beach

description: Experimental data of scale experiments performed at the beach. Two series of experiments were performed with cuboid scale models of various sizes, to determine how the size of aeolian depositon and erosion patterns around beach buildings depends on building geometry. Series A consisted of cuboid scale models of buildings, placed at the beach for approximately 1 day. These scale models ranged in size from 0.3x0.5x0.3 m (WxLxH) to 1x2x0.6 m. Serie B consisted of a shipping container (2.5x12x2.5 m) and a small scale model (0.5x2x0.5 m), placed simultaneously at the beach for more than 2 months. Deposition and erosion around buildings are measured using structure-from motion (SfM) photogrammetry. Data contains: Raw photos used for SfM photogrammetry Photogrammetry files Digital elavation models (DEMs) and orthophotos of the deposition and erosion around scale models. Processed data from the automatic detection of deposition edges: binarized orthophotos (deposition/rest) and the edges of the deposition areas Time lapses of the the experiments Wind speed and direction measurements Measured depositiion sizes
DOI: 10.4121/21285939.v2
title: Experimental data of The effect of building geometry on the size of aeolian deposition patterns: scale model experiments at the beach

description: Experimental data of scale experiments performed at the beach. Two series of experiments were performed with cuboid scale models of various sizes, to determine how the size of aeolian depositon and erosion patterns around beach buildings depends on building geometry. Series A consisted of cuboid scale models of buildings, placed at the beach for approximately 1 day. These scale models ranged in size from 0.3x0.5x0.3 m (WxLxH) to 1x2x0.6 m. Measurements were taken at the end of each day (each experiment). Serie B consisted of a shipping container (2.5x12x2.5 m) and a small scale model (0.5x2x0.5 m), placed simultaneously at the beach for more than two months. Measurements were taken at six different days during this period. Deposition and erosion around buildings are measured using structure-from motion (SfM) photogrammetry. Data contains: Raw photos used for SfM photogrammetry Photogrammetry files Digital elavation models (DEMs) and orthophotos of the deposition and erosion around scale models. Processed data from the automatic detection of deposition edges: binarized orthophotos (deposition/rest) and the edges of the deposition areas Time lapses of the the experiments Wind speed and direction measurements Measured deposition sizes
DOI: 10.4121/21285939
title: Experimental data of The effect of building geometry on the size of aeolian deposition patterns: scale model experiments at the beach

description: Experimental data of scale experiments performed at the beach. Two series of experiments were performed with cuboid scale models of various sizes, to determine how the size of aeolian depositon and erosion patterns around beach buildings depends on building geometry. Series A consisted of cuboid scale models of buildings, placed at the beach for approximately 1 day. These scale models ranged in size from 0.3x0.5x0.3 m (WxLxH) to 1x2x0.6 m. Measurements were taken at the end of each day (each experiment). Serie B consisted of a shipping container (2.5x12x2.5 m) and a small scale model (0.5x2x0.5 m), placed simultaneously at the beach for more than two months. Measurements were taken at six different days during this period. Deposition and erosion around buildings are measured using structure-from motion (SfM) photogrammetry. Data contains: Raw photos used for SfM photogrammetry Photogrammetry files Digital elavation models (DEMs) and orthophotos of the deposition and erosion around scale models. Processed data from the automatic detection of deposition edges: binarized orthophotos (deposition/rest) and the edges of the deposition areas Time lapses of the the experiments Wind speed and direction measurements Measured deposition sizes
DOI: 10.4121/19635243
title: Data underlying article 'Towards effectively restoring agricultural landscapes in East African drylands: Linking plant functional traits with soil hydrology'

description: We measured field-saturated soil hydraulic conductivity (Kfs) and soil organic carbon (SOC) in 38 plots across agricultural landscapes in Muminji, Kenya. Woody vegetation and land use inventories took place and species functional traits were measured on the 63 most important species. We systematically tested the effects of vegetation quantity (aboveground woody biomass and vegetation cover) and quality (functional properties and diversity) on soil health (Kfs as a proxy for soil infiltrability and SOC for soil fertility). This dataset contains the data that underlies the analysis and outcomes of this study, described for each dataset below.
DOI: 10.4121/19635243.v1
title: Data underlying article 'Towards effectively restoring agricultural landscapes in East African drylands: Linking plant functional traits with soil hydrology'

description: We measured field-saturated soil hydraulic conductivity (Kfs) and soil organic carbon (SOC) in 38 plots across agricultural landscapes in Muminji, Kenya. Woody vegetation and land use inventories took place and species functional traits were measured on the 63 most important species. We systematically tested the effects of vegetation quantity (aboveground woody biomass and vegetation cover) and quality (functional properties and diversity) on soil health (Kfs as a proxy for soil infiltrability and SOC for soil fertility). This dataset contains the data that underlies the analysis and outcomes of this study, described for each dataset below.
DOI: 10.4121/21375657
title: Data figure 4,6, and 7 of "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments", Renewable Energy 116, Pages 470-478 (2018)

description: TXT files with data of the experimental and simulation results + the corresponding Python scripts of Figures 4, 6, and 7 of the paper. <br> R.J.A.M. Stevens, L.A. Martínez Tossas, C. Meneveau, Comparison of large eddy simulations using actuator disk or actuator line models with wind tunnel experiments, Renewable Energy, 116 (A), 470-478 (2018). https://doi.org/10.1016/j.renene.2017.08.072
DOI: 10.4121/21375657.v1
title: Data figure 4,6, and 7 of "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments", Renewable Energy 116, Pages 470-478 (2018)

description: TXT files with data of the experimental and simulation results + the corresponding Python scripts of Figures 4, 6, and 7 of the paper. <br> R.J.A.M. Stevens, L.A. Martínez Tossas, C. Meneveau, Comparison of large eddy simulations using actuator disk or actuator line models with wind tunnel experiments, Renewable Energy, 116 (A), 470-478 (2018). https://doi.org/10.1016/j.renene.2017.08.072
DOI: 10.5519/qd.1ys0tv0b
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (21584 records)
DOI: 10.5281/zenodo.6302240
title: JetNet150

description: Gluon (g), Top Quark (t), Light Quark (q) jets/particle clouds saved in hdf5 format. The shape of particles features is [N, 150, 4], where N is the total number of jets, 150 is the number of particles per jet, and 4 is the number of particle features (in order): [eta_rel, phi_rel, pt_rel, mask]. N = 177252, 177945, 170679 for g, t, q jets respectively. These are the same jets as in JetNet (https://zenodo.org/record/5502543), but including up to 150 particles.
DOI: 10.5281/zenodo.6975117
title: JetNet150

description: Gluon (<code>g</code>), Top Quark (<code>t</code>), Light Quark (<code>q</code>), W boson (<code>w</code>), and Z boson (<code>z</code>) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1], but up to 150 particles. Each file has <code>particle_features</code>; and <code>jet_features</code>; arrays, containing the list of particles' features per jet and the corresponding jet's features, respectively. <code>particle_features</code> is of shape [N, 150, 4], where N is the total number of jets, 30 is the max number of particles per jet, and 4 is the number of particle features, in order: [\(\eta_{\mathrm{rel}}\), \(\varphi_{\mathrm{rel}}\), \(p_{T\mathrm{rel}}\), mask]. See [1, Sec. 2] for definitions of these. <code>jet_features</code> is of shape [N, 4], where 4 is the number of jet features, in order: [\(p_T\), \(\eta\), mass, # of particles]. N = 177252, 177945, 170679, 177172. 176952 for g, t, q, w, z jets respectively. Finally, the JetNet package is recommended for easy access and use of this dataset. [1] Kansal et. al., <em>Particle Cloud Generation with Message Passing Generative Adversarial Networks</em>, NeurIPS 2021 <code>arXiv:2106.11535</code>
DOI: 10.5281/zenodo.6302239
title: JetNet150

description: Gluon (<code>g</code>), Top Quark (<code>t</code>), Light Quark (<code>q</code>), W boson (<code>w</code>), and Z boson (<code>z</code>) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1], but up to 150 particles. Each file has <code>particle_features</code>; and <code>jet_features</code>; arrays, containing the list of particles' features per jet and the corresponding jet's features, respectively. <code>particle_features</code> is of shape [N, 150, 4], where N is the total number of jets, 30 is the max number of particles per jet, and 4 is the number of particle features, in order: [\(\eta_{\mathrm{rel}}\), \(\varphi_{\mathrm{rel}}\), \(p_{T\mathrm{rel}}\), mask]. See [1, Sec. 2] for definitions of these. <code>jet_features</code> is of shape [N, 4], where 4 is the number of jet features, in order: [\(p_T\), \(\eta\), mass, # of particles]. N = 177252, 177945, 170679, 177172. 176952 for g, t, q, w, z jets respectively. Finally, the JetNet package is recommended for easy access and use of this dataset. [1] Kansal et. al., <em>Particle Cloud Generation with Message Passing Generative Adversarial Networks</em>, NeurIPS 2021 <code>arXiv:2106.11535</code>
DOI: 10.5281/zenodo.4834875
title: JetNet

description: Gluon (<code>g</code>), Top Quark (<code>t</code>), Light Quark (<code>q</code>), W boson (<code>w</code>), and Z boson (<code>z</code>) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1]. Each file has <code>particle_features</code>; and <code>jet_features</code>; arrays, containing the list of particles' features per jet and the corresponding jet's features, respectively. <code>particle_features</code> is of shape [N, 30, 4], where N is the total number of jets, 30 is the max number of particles per jet, and 4 is the number of particle features, in order: [\(\eta_{\mathrm{rel}}\), \(\varphi_{\mathrm{rel}}\), \(p_{T\mathrm{rel}}\), mask]. See [1, Sec. 2] for definitions of these. <code>jet_features</code> is of shape [N, 4], where 4 is the number of jet features, in order: [\(p_T\), \(\eta\), mass, # of particles]. N = 177252, 177945, 170679, 177172, 176952 for g, t, q, w, z jets respectively. Finally, the JetNet package is recommended for easy access and use of this dataset. [1] Kansal et. al., <em>Particle Cloud Generation with Message Passing Generative Adversarial Networks</em>, NeurIPS 2021 <code>arXiv:2106.11535</code>