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1,445 results for “Distances”
Simple distance estimates for Gaia DR2 stars with radial velocities
<p>Bayesian distance estimates for stars with radial velocities and parallaxes published in <em>Gaia</em> DR2. Our method and prior is designed to apply to this specific subset of stars in <em>Gaia</em> DR2.</p> <p>The method is published in "Simple distance estimates for Gaia DR2 stars with radial velocities", McMillan 2018, arXiv:1806.00426</p> <p>The code used to produce the estimates is here: https://doi.org/10.5281/zenodo.1270548</p>
dataset for: A Distance-Based Boolean Applicability Domain for category Quantitative Structure-Activity Relationship
<p>Dataset for category QSAR benchmarking</p> <p> </p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm
<p>In Ref. <a href="http://doi.org/10.5281/zenodo.1476721">http://doi.org/10.5281/zenodo.1476721 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm
<p>In Ref. <a href="http://dx.doi.org/10.5281/zenodo.1476719">http://doi.org/10.5281/zenodo.1476719 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm
<p>In Ref. we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm
<p>In http://dx.doi.org/10.5281/zenodo.1482739 we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Impact of binary interaction on the evolution of blue supergiants. The flux-weighted gravity luminosity relationship and extragalactic distance determinations
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2019A&A...621A..22F">Impact of binary interaction on the evolution of blue supergiants. The flux-weighted gravity luminosity relationship and extragalactic distance determinations</a></p>
Validity and reliability of the Kinovea program in obtaining angles and distances using coordinates in 4 perspectives
<p>Dataset used to perform the statistical analysis for the article "Validity and reliability of the Kinovea program in obtaining angles and distances using coordinates in 4 perspectives"</p>
Distance matrices of an aquatic invertebrate dataset on the Rhône river basin
<p>Environmental distance matrices, community dissimilarity matrices and spatial distances computed on an aquatic invertebrate dataset. More information in the metadata file.</p>
biodiego88/Publicacion_densidad_Aspurrelli_PNNUtria: Distance Sampling Atelopus spurrelli
<p>Código para el análisis de datos de distancias perpendiculares con el modelo Multinomial-Poisson mixture para estimar la densidad de Atelopus spurrelli en el Parque Nacional Natural Utría</p>
FIGURE 3 in Radiotagging a long-distance migratory characid fish: reproduction after surgery, tag losses, and effects in weight
FIGURE 3 | Fecundity (oocytes by gram of body weight) compared among treatments.
Source population and time spent in captivity affect survival and reproduction of long-distance translocated northern bobwhites
<p>Northern bobwhites (<em>Colinus virginianus</em>) have become a species of great conservation priority because of widespread and ongoing population declines. Long-distance translocations are becoming increasingly used to access a source population with densities high enough to support translocation. Two key uncertainties exist regarding the efficacy of long-distance translocations: choosing a source population with adaptations that will be successful in a novel environment and mitigating the stress response common during the translocation process. We translocated bobwhites from the South Texas Plains and the Floridian Coastal Plain to a recipient site in the Floridian Coastal Plain in 2021 and 2022 to compare the survival and productivity of bobwhites translocated from two different source populations. We also evaluated how varying holding times during the translocation process influenced the success of the translocated individuals. Breeding season survival, nest propensity, and fecundity were greater for Florida resident and Florida translocated bobwhites relative to Texas translocated bobwhites. We observed high rates of mortality during the transport and holding processes, but holding time did not affect breeding season survival of Texas translocated bobwhites. Both nest success and fecundity of Texas translocated bobwhites were negatively affected by holding time. Bobwhites translocated long distances may have the adaptive capacity to be successful in novel environments, but the consequences of translocation stress can be detrimental. Future translocation planning should consider choosing source populations from similar ecoregions to simultaneously decrease translocation distances and potential stress from translocation.</p>
Flight initiation distance differs among eumelanin-based color morphs in feral pigeons
<p><strong><span>The table lists the variable and factors used in the article:</span></strong></p> <p><span>“Flight initiation distance differs among eumelanin-based color morphs in feral pigeons”</span></p> <p><span>- “Individual” is an individual number to differentiate individiual feral pigeons</span></p> <p><span>- “Location” is the name of the location in Paris where the FID of the individual was measured</span></p> <p><span>- “Date” is the date when the FID of the individual was measured</span></p> <p><span>- “ColorMorph” is the eumelanin-based color morph of the individual</span></p> <p><span>- “Latitude” and “Longitude” are the spatial coordinates of the site in Paris where the FID of the individual was measured</span></p> <p><span>- "FID" is the Flight Initiation Distance measured</span></p> <p><span>- columns from “Urbanisation300” to “Urbanisation1000” are the Urbanization rates determined within increasing radius from 300 to 1000m around each individual</span></p> <p><span>- columns from “PedestrianTraffic300PropNiv1” to “PedestrianTraffic900PropNiv1” are the proportion of streets of level 1 intensity, measured within increasing radius from 300 to 900m</span></p> <p><span>- columns from “PedestrianTraffic300PropNiv2” to “PedestrianTraffic900PropNiv2” are the proportion of streets of level 2 intensity, measured within increasing radius from 300 to 900m<span><br></span></span></p> <p><span>- columns from “PedestrianTraffic300PropNiv3” to “PedestrianTraffic900PropNiv2” are the proportion of streets of level 3 intensity, measured within increasing radius from 300 to 900m</span></p>
Magnetic Distance Estimation Data from Gait Experiments with Magnetoelectric Sensors
<h2>Overview</h2> <p><br>This is the "Magnetic Distance Estimation Data from Gait Experiemtns with Magnetoelectric Sensors" dataset. <br>It represents a pilot study on magnetic motion tracking with novel magnetoelectric sensors during treadmill walking.<br>Therefore, it contains both technical (calibration) data and clinical (gait) data of five healthy participants.</p> <p>Example scripts for loading and processing data are available in the linked respository.</p> <p>The dataset is formatted according to the Brain Imaging Data Structure. See the `dataset_description.json` file for the specific version used.</p> <p>The work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the Collaborative Research Center CRC 1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics. The data was recorded in the project B9 on "Magnetoelectric Sensors for Movement Detection and Analysis".</p> <p>All measurements were approved by the ethics committee of Kiel University (File number: A122/20) and conducted in accordance with the Declaration of Helsinki.</p> <h2><br>Details about the experiment</h2> <p><br>Magnetic motion tracking enables a relative tracking, in which the distance between each sensor and actuator node can be estimated. <br>The full setup contains two actuator nodes (a0, a1) and four sensor nodes (s0, s1, s2, s3).<br>Each actuator-sensor pair produces nine magnetic signals (x,y,z by x,y,z) as well as three magnetic dipole moment signals that represent the currents through the coils (actuators).<br>Additionally, each node was tracked with an optical motion capture (OMC) system. The resulting position and orientation data of the attached rigid body act as a reference (ground truth) to evaluate the magnetic estimation. <br>Subfolders sub-01 to sub-08 each contain up to three calibration tasks which each contain between 60 and 120s of arbitrary movement of one coil (wand-mounted) around one stationary sensor (base).<br>Subfolders sub-09 to sub-13 each contain two 120s walking tasks (0.5 and 1 m/s) of five subjects in total with the full sensor and actuator setup. Two actuators were mounted to the shanks, two sensors to the thighs and two sensors were placed stationary next to the threadmill. <br>The dataset also contains a folder with derived data, which contains calibration parameters for each actuator-sensor pair. See the provided matlab script for details on how to load, visualize, and compare the results.</p>
TRADE4SD Deliverable 2.3: Database and infographics on standards rapprochement: STCs and bilateral measure of distance on pesticides and antibiotics
<p><span>One of the objectives of the Horizon2020 project “TRADE4SD” is to offer policy recommendations for improving trade policies at the national, European, and global levels, including reforms to the WTO, and to enhance policy alignment. To achieve this goal, it is essential to address the impact of NTMs, which, despite their increasing use, remain largely underexplored in terms of the effects on international trade. This limited understanding is due to the complexity of NTMs and their effects are difficult to generalise. Several critical areas related to NTMs and their implications for international trade require further investigation, which “TRADE4SD” seeks to address. This work explores how NTMs affect market access for developing countries, as these nations may face various challenges, such as limited capabilities, technological gaps, weaker infrastructures and institutions, and asymmetric information. More specifically, “TRADE4SD” aims to identify best practices for improving the management of SPS, which are essential for enhancing the competitiveness of agricultural and food exports. Strengthening SPS capacity is also vital for boosting productivity in the agricultural and food processing industries, contributing to agricultural and rural development, and helping to alleviate poverty. </span></p> <p><span>This deliverable consists of two main sections: one addressing Maximum Residue Levels (MRLs) for pesticides and antibiotics and the other focusing on Specific Trade Concerns (STCs). </span></p> <p><span>In the first section, we analysed the regulations and then acquired and processed the data to create the databases, which we later used to develop the indices and infographics (for both pesticides and antibiotics).</span></p> <p><span>In the STCs section, we analysed WTO documentation on all open disputes and verified their status. During our analysis, we identified the relationships between STCs and the Sustainable Development Goals (SDGs). Finally, we developed the infographics.</span></p>
Table 1. Uncorrected distances among 31 in On the distribution and taxonomy of bats of the Myotis mystacinus morphogroup from the Caucasus region (Chiroptera: Vespertilionidae)
<p><b>Table 1</b>. Uncorrected distances among 31 haplotypes of the cytochrome <i>b</i> gene found in the bats of the <i>Myotis mystacinus</i> morphogroup from the Caucasus region.</p><table><tbody><tr><th>hap1</th><th>hap2</th><th>hap3</th><th>hap4</th><th>hap5</th><th>hap6</th><th>hap7</th><th>hap8</th><th>hap9</th><th>hap10 hap11 hap12 hap13 hap14 hap15 hap16 hap17 hap18 hap19 hap20 hap21 hap22 hap23 hap24 hap25</th><th>hap26 hap27 hap28</th><th>hap29 hap30</th></tr></tbody><tbody><tr><th>hap2 0.001</th><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap3 0.007</th><td>0.006</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap4 0.003</th><td>0.002</td><td>0.004</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap5 0.015</th><td>0.014</td><td>0.017</td><td>0.012</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap6 0.039</th><td>0.038</td><td>0.039</td><td>0.036</td><td>0.039</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap7 0.041</th><td>0.040</td><td>0.041</td><td>0.039</td><td>0.042</td><td>0.004</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap8 0.039</th><td>0.038</td><td>0.039</td><td>0.036</td><td>0.039</td><td>0.002</td><td>0.004</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap9 0.127</th><td>0.126</td><td>0.131</td><td>0.128</td><td>0.132</td><td>0.133</td><td>0.136</td><td>0.135</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap10 0.127</th><td>0.126</td><td>0.131</td><td>0.128</td><td>0.132</td><td>0.133</td><td>0.136</td><td>0.135</td><td>0.003</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap11 0.125</th><td>0.125</td><td>0.129</td><td>0.126</td><td>0.130</td><td>0.132</td><td>0.135</td><td>0.134</td><td>0.002</td><td>0.004</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap12 0.126</th><td>0.125</td><td>0.130</td><td>0.127</td><td>0.131</td><td>0.133</td><td>0.136</td><td>0.135</td><td>0.003</td><td>0.005</td><td>0.001</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap13 0.129</th><td>0.128</td><td>0.132</td><td>0.130</td><td>0.133</td><td>0.136</td><td>0.139</td><td>0.138</td><td>0.006</td><td>0.009</td><td>0.006</td><td>0.007</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap14 0.127</th><td>0.126</td><td>0.131</td><td>0.128</td><td>0.130</td><td>0.131</td><td>0.133</td><td>0.132</td><td>0.004</td><td>0.006</td><td>0.004</td><td>0.004</td><td>0.006</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap15 0.128</th><td>0.127</td><td>0.132</td><td>0.129</td><td>0.132</td><td>0.135</td><td>0.138</td><td>0.137</td><td>0.010</td><td>0.012</td><td>0.010</td><td>0.011</td><td>0.012</td><td>0.010</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap16 0.122</th><td>0.121</td><td>0.125</td><td>0.123</td><td>0.125</td><td>0.127</td><td>0.130</td><td>0.129</td><td>0.019</td><td>0.022</td><td>0.019</td><td>0.020</td><td>0.020</td><td>0.019</td><td>0.018</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap17 0.145</th><td>0.144</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.143</td><td>0.146</td><td>0.158</td><td>0.157</td><td>0.156</td><td>0.157</td><td>0.160</td><td>0.156</td><td>0.158</td><td>0.159</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap18 0.145</th><td>0.144</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.143</td><td>0.146</td><td>0.158</td><td>0.157</td><td>0.156</td><td>0.157</td><td>0.160</td><td>0.156</td><td>0.158</td><td>0.161</td><td>0.002</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap19 0.146</th><td>0.145</td><td>0.147</td><td>0.146</td><td>0.147</td><td>0.146</td><td>0.144</td><td>0.146</td><td>0.159</td><td>0.158</td><td>0.157</td><td>0.158</td><td>0.161</td><td>0.157</td><td>0.159</td><td>0.161</td><td>0.003</td><td>0.001</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap20 0.143</th><td>0.142</td><td>0.145</td><td>0.144</td><td>0.145</td><td>0.144</td><td>0.141</td><td>0.144</td><td>0.156</td><td>0.155</td><td>0.154</td><td>0.155</td><td>0.158</td><td>0.154</td><td>0.156</td><td>0.159</td><td>0.004</td><td>0.002</td><td>0.003</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap21 0.145</th><td>0.144</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.143</td><td>0.146</td><td>0.158</td><td>0.157</td><td>0.156</td><td>0.157</td><td>0.160</td><td>0.156</td><td>0.158</td><td>0.161</td><td>0.004</td><td>0.002</td><td>0.003</td><td>0.002</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap22 0.144</th><td>0.143</td><td>0.146</td><td>0.145</td><td>0.146</td><td>0.145</td><td>0.142</td><td>0.145</td><td>0.157</td><td>0.156</td><td>0.155</td><td>0.156</td><td>0.159</td><td>0.155</td><td>0.157</td><td>0.160</td><td>0.003</td><td>0.001</td><td>0.002</td><td>0.001</td><td>0.001</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap23 0.151</th><td>0.150</td><td>0.153</td><td>0.152</td><td>0.151</td><td>0.145</td><td>0.142</td><td>0.145</td><td>0.161</td><td>0.160</td><td>0.159</td><td>0.160</td><td>0.161</td><td>0.159</td><td>0.161</td><td>0.160</td><td>0.027</td><td>0.025</td><td>0.026</td><td>0.025</td><td>0.025</td><td>0.025</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap24 0.153</th><td>0.152</td><td>0.154</td><td>0.154</td><td>0.153</td><td>0.146</td><td>0.144</td><td>0.146</td><td>0.162</td><td>0.161</td><td>0.161</td><td>0.161</td><td>0.162</td><td>0.161</td><td>0.162</td><td>0.161</td><td>0.029</td><td>0.027</td><td>0.026</td><td>0.027</td><td>0.027</td><td>0.026</td><td>0.002</td><td>–</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap25 0.153</th><td>0.152</td><td>0.154</td><td>0.154</td><td>0.153</td><td>0.146</td><td>0.144</td><td>0.146</td><td>0.163</td><td>0.162</td><td>0.161</td><td>0.162</td><td>0.163</td><td>0.161</td><td>0.163</td><td>0.162</td><td>0.029</td><td>0.027</td><td>0.028</td><td>0.027</td><td>0.027</td><td>0.026</td><td>0.004</td><td>0.005</td><td>–</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>hap26 0.153</th><td>0.152</td><td>0.154</td><td>0.154</td><td>0.153</td><td>0.146</td><td>0.144</td><td>0.146</td><td>0.162</td><td>0.161</td><td>0.161 0.161</td><td>0.162</td><td>0.161</td><td>0.162</td><td>0.161</td><td>0.027</td><td>0.025</td><td>0.026</td><td>0.025</td><td>0.025</td><td>0.025</td><td>0.002</td><td>0.004</td><td>0.002</td><td>–</td><td></td><td></td><td></td><td></td></tr><tr><th>hap27 0.151</th><td>0.150</td><td>0.153</td><td>0.150</td><td>0.149</td><td>0.146</td><td>0.147</td><td>0.146</td><td>0.161</td><td>0.161</td><td>0.159</td><td>0.160</td><td>0.161</td><td>0.159</td><td>0.161</td><td>0.158</td><td>0.049</td><td>0.049</td><td>0.050</td><td>0.049</td><td>0.049</td><td>0.048</td><td>0.050</td><td>0.052</td><td>0.052</td><td>0.050</td><td>–</td><td></td><td></td><td></td></tr><tr><th>hap28 0.149</th><td>0.148</td><td>0.151</td><td>0.148</td><td>0.147</td><td>0.145</td><td>0.146</td><td>0.145</td><td>0.159</td><td>0.160</td><td>0.157</td><td>0.158</td><td>0.159</td><td>0.157</td><td>0.161</td><td>0.156</td><td>0.047</td><td>0.047</td><td>0.048</td><td>0.047</td><td>0.047</td><td>0.046</td><td>0.048</td><td>0.050</td><td>0.050</td><td>0.048</td><td>0.002</td><td>–</td><td></td><td></td></tr><tr><th>hap29 0.150</th><td>0.149</td><td>0.152</td><td>0.149</td><td>0.148</td><td>0.146</td><td>0.146</td><td>0.146</td><td>0.160</td><td>0.161</td><td>0.158</td><td>0.159</td><td>0.160</td><td>0.158</td><td>0.161</td><td>0.157</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.047</td><td>0.049</td><td>0.050</td><td>0.051</td><td>0.049</td><td>0.003</td><td>0.001</td><td>–</td><td></td></tr><tr><th>hap30 0.152</th><td>0.153</td><td>0.157</td><td>0.154</td><td>0.154</td><td>0.144</td><td>0.143</td><td>0.144</td><td>0.158</td><td>0.159</td><td>0.158</td><td>0.157</td><td>0.158</td><td>0.156</td><td>0.157</td><td>0.154</td><td>0.104</td><td>0.103</td><td>0.104</td><td>0.103</td><td>0.101</td><td>0.102</td><td>0.099</td><td>0.101</td><td>0.101</td><td>0.101</td><td>0.097</td><td>0.096</td><td>0.097</td><td>–</td></tr><tr><th>hap31 0.167</th><td>0.166</td><td>0.165</td><td>0.166</td><td>0.164</td><td>0.159</td><td>0.161</td><td>0.157</td><td>0.168</td><td>0.169</td><td>0.168</td><td>0.168</td><td>0.166</td><td>0.168</td><td>0.171</td><td>0.164</td><td>0.169</td><td>0.169</td><td>0.170</td><td>0.169</td><td>0.168</td><td>0.168</td><td>0.161</td><td>0.163</td><td>0.163</td><td>0.163</td><td>0.161</td><td>0.159</td><td>0.160</td><td>0.147</td></tr></tbody></table>
Data for the paper "Improving Efficiency Through the Publication of Expected Distances for Standard Terminal Arrival Routes "
<p>Data in support of the paper "Improving Efficiency Through the Publication of Expected Distances for Standard Terminal Arrival Routes". The data is organised in subfolders containing the data for each of the three analysed airports (LSGG, EDDM, LIRF).</p> <p>Each folder includes a <em>landing_full.parquet</em> traffic file that contains all analyzed trajectories for the respective airport, along with random subsamples of sizes 10,000, 5,000, and 2,500.</p> <p>Additionally, each folder also contains samples of 1000 trajectories following each of the four specific STAR procedure that were analysed per airport:</p> <ul> <li><strong>LSGG:</strong> AKITO 3R, BELUS 3N, KINES 2N, LUSAR 2N</li> <li><strong>EDDM:</strong> BETOS 1A, LANDU 1B, NAPSA 1B, ROKIL 1A</li> <li><strong>LIRF: </strong>ELKAP 2A, LAT 2C, RITEB 2A, VALMA 2C</li> </ul> <p>Finally, each folder also contains a <em>landing_df.parquet</em> file that summarises the following key information about each of the trajectories contained in the <em>landing_full.parquet</em> file:</p> <table> <tbody> <tr> <td><strong>ID</strong></td> <td>A unique identifier linking the row to the corresponding trajectory data</td> </tr> <tr> <td><strong>Typecode</strong></td> <td>The ICAO typecode of the aircraft</td> </tr> <tr> <td><strong>Start</strong></td> <td>Timestamp when the aircraft passes the first waypoint of the STAR</td> </tr> <tr> <td><strong>Stop</strong></td> <td>Timestamp when the aircraft crosses the runway threshold</td> </tr> <tr> <td><strong>Runway</strong></td> <td>Designator of the landing runway</td> </tr> <tr> <td><strong>STAR</strong></td> <td>Name of the STAR procedure used by the aircraft</td> </tr> <tr> <td><strong>Distance</strong></td> <td>Total distance traveled by the aircraft from the initial waypoint of the STAR to the runway threshold</td> </tr> </tbody> </table>
Table 2. Pairwise uncorrected p - distances for 16 S in Two new species of gymnophthalmid lizards of the genus Petracola (Squamata: Cercosaurinae) from the Andes of northeastern Peru, and their phylogenetic relationships
<p><b>Table 2.</b> Pairwise uncorrected <i>p</i> -distances for 16S rRNA between <i>Petracola</i> species. The asterisk (*) indicates type locality.</p><table><tbody><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th></tr></tbody><tbody><tr><th>(1) <i>P. ventrimaculatus</i> CORBIDI 9235</th><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>(2) <i>P. ventrimaculatus</i> KU 219838</th><td>0.024</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>(3) <i>P. waka</i> KU 212687</th><td>0.063</td><td>0.071</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>(4) <i>P. waka</i> MUBI 2603</th><td>0.073</td><td>0.091</td><td>0.063</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>(5) <i>P. waka</i> MUBI 2605</th><td>0.073</td><td>0.091</td><td>0.063</td><td>0.000</td><td>-</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>(6) <i>P. waka</i> MUBI 2609*</th><td>0.069</td><td>0.082</td><td>0.066</td><td>0.031</td><td>0.031</td><td>-</td><td></td><td></td><td></td><td></td></tr><tr><th>(7) <i>P. waka</i> MUBI 2611*</th><td>0.069</td><td>0.082</td><td>0.066</td><td>0.031</td><td>0.031</td><td>0.000</td><td>-</td><td></td><td></td><td></td></tr><tr><th>(8) <i>P. shurugojalcapi</i> MUBI 17727</th><td>0.058</td><td>0.074</td><td>0.080</td><td>0.079</td><td>0.079</td><td>0.079</td><td>0.079</td><td>-</td><td></td><td></td></tr><tr><th>(9) <i>P. shurugojalcapi</i> PFAUNA 430</th><td>0.058</td><td>0.074</td><td>0.080</td><td>0.079</td><td>0.079</td><td>0.079</td><td>0.079</td><td>0.000</td><td>-</td><td></td></tr><tr><th>(10) <i>P. amazonensis</i> MUBI 11473</th><td>0.057</td><td>0.072</td><td>0.085</td><td>0.078</td><td>0.078</td><td>0.072</td><td>0.072</td><td>0.037</td><td>0.037</td><td>-</td></tr></tbody></table>
Data and Code for "Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution"
<p>This repository contains the datasets and data sources, analysis code, and workflow associated with the manuscript "<em>Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution</em>". The following resources are provided:</p> <ul> <li> <p><strong>Data Files</strong>:</p> <ul> <li><code>time_series_data.csv</code>: A curated time series dataset with dates as rows and NUTS 2 regions as columns. Each column is labeled using a 4-letter abbreviation format "CC.RR", where "CC" represents the country code and "RR" represents the region code. This same abbreviation is also included in the accompanying GeoJSON file.</li> <li><code>geometry_data.geojson</code>: A GeoJSON file representing the spatial boundaries of the NUTS 2 regions, with the same 4-letter abbreviations used in the CSV file. EPSG:4326.</li> <li><code>COVID19_data_sources.xlsx</code>: This Excel file contains important metadata regarding the sources of COVID-19 data used in this study. It includes: <ul> <li>Source of the data for each country</li> <li>Official website(s)</li> <li>The agency responsible for the data</li> <li>Description of the processing steps used to curate the data into the final time series.</li> </ul> </li> </ul> </li> <li> <p><strong>Code</strong>:</p> <ul> <li><code>analysis.py</code>: A Python script used to process and analyze the data. This code can be run using Python 3.x. The libraries required to run this script are listed in the first lines of the code. The code is organized in different numbered sections (1), (2), ... and sub-sections (1a), (1b) ... Make sure to run the script one (sub-)section at a time, so that everything stays overviewable and you don't get all the output at once.</li> </ul> </li> <li> <p><strong>Workflow</strong>:</p> <ul> <li><code>workflow.png</code> : A detailed workflow according to the Knowledge Discovery in Databases (KDD) process, outlining the steps involved in processing and analyzing the data, including the methods used. This workflow provides a comprehensive guide to reproducing the analysis presented in the paper.</li> </ul> </li> </ul>
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