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21 results for “source tracking”
Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times
<p>A hallmark of bacterial populations cultured <em>in vitro</em> is their homogeneity of growth, where the majority of cells display identical growth rate, cell size and content. Recent insights, however, have revealed that even cells growing in exponential growth phase can be heterogeneous with respect to variables typically used to measure cell growth. Bacterial heterogeneity has important implications for how bacteria respond to environmental stresses, such as antibiotics. The phenomenon of antimicrobial persistence, for example, has been linked to a small subpopulation of cells that have entered into a state of dormancy where antibiotics are no longer effective. While methods have been developed for identifying individual non-growing cells in bacterial cultures, there has been less attention paid to how these cells may influence growth in colonies on a solid surface. In response, we have developed a low-cost, open-source platform to perform automated image capture and image analysis of bacterial colony growth on multiple nutrient agar plates simultaneously. The descriptions of the hardware and software are included, along with details about the temperature-controlled growth chamber, high-resolution scanner, and graphical interface to extract and plot the colony lag time and growth kinetics. Experiments were conducted using a wild type strain of <em>Escherichia coli </em>K12 to demonstrate the feasibility and operation of our setup. By automated tracking of bacterial growth kinetics in colonies, the system holds the potential to reveal new insights into understanding the impact of microbial heterogeneity on antibiotic resistance and persistence. </p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
Figure 3 in Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil
Figure 3. Locations and copy numbers for human- and ruminant-indicative Bacteroides spp. DNA extracted from the material retained from filtration of 500 ml was used for qPCR determination of rDNA copy number. The size of the indicated shapes in the figure is proportional to the copy number/ml at that point as indicated in the legend. Inset shows points 12 and 13 at the same scale as the main figure.
Figure 2 in Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil
Figure 2. Locations and concentrations of coliforms and E. coli at water collection points. A volume of water (100 µl – 1 ml) collected mid-stream was plated using the Coliscan culture system. Colonies were identified and counted at 48h. The size of the indicated shapes in the figure is proportional to the number of colonies/ml cultured as indicated in the legend. Inset shows points 12 and 13 at the same scale as the main figure.
Figure 1 in Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil
Figure 1. Study area, rivers and water collection sites. The collection points on the Jiquiriçá River are P1-5; collection points on the Brejões P6-8. P3 is at the junction of the 2 rivers, and P9 and P10 are from the water treatment plant and an outside faucet, respectively. Left inset – Location of Bahia state, Salvador and Jenipapo within Brazil based on Wikimedia Commons (2011). Right inset – relationship of P12 and P13 to Jenipapo. These 2 points represent the source of piped water for the community and the furthest point upstream for collection on the Brejões River, respectively. Inset modified from Wikimedia Commons (2011).
Evaluating Human Health Risks with Microbial Source Tracking and Quantitative Microbial Risk Assessment in Little Bay, Texas
<p>These results are the median human health risks associated with a range of recreational activities (i.e., swimming, kayaking, fishing, boating, jet skiing) in Little Bay, Texas. The health risks were estimated based on a quantiative microbial risk assessment (QMRA) that was performed using enterococci concentrations as well as host-associated molecular marker (i.e., human, canine, gull) concentrations measured previously in Little Bay. These human health risks represent the probability of a gastrointestinal illness for recreators.</p>
Table 1 in Fission track dating of obsidian source samples from the Willaumez Peninsula, Papua New Guinea and eastern Australia
<p>Table 1. Measurement of thermal neutron fluence.1</p><table><tbody><tr><th>monitor</th><th>external detector</th><th>B (109ntl)</th><th>trackdensity onext.detector (105tcm2)</th><th>thermal neutronfluence (1015ncm2)</th></tr></tbody><tbody><tr><th>Auwire</th><td>TODO</td><td>TODO</td><td>TODO</td><td>2.399±0.034</td></tr><tr><th>Cowire</th><td>TODO</td><td>TODO</td><td>TODO</td><td>2.392±0.025</td></tr><tr><th>CNlglass</th><td>mIca</td><td>1.873±0.025</td><td>13.43±0.26</td><td>2.515±0.036</td></tr><tr><td>makrofol</td><td>1.863±0.023</td><td>13.24±0.26</td><td>2.468±0.058</td></tr><tr><th>CN2glass</th><td>mIca</td><td>1.985±0.007</td><td>12.34±0.25</td><td>2.449±0.051</td></tr><tr><td>makrofol</td><td>2.016±0.041</td><td>12.31±0.25</td><td>2.482±0.072</td></tr></tbody></table><p>1 Calibration of glass dosimeters (B value measurement) was performed by using cobalt and gold monitors (Bonetti <i>et al.,</i> 1994). The thermal neutron fluence value used in the age measurements has been obtained as a weighted mean of the values listed in the last column.</p>
FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking
<p>This is the software described in our article 'FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking' and the used datasets for image analysis. It is intended as a easy to use software for tracking of cytoskeletal fibers offering both source code and GUI-based executable. Datasets are sorted according to figures in the article with folders containing raw images, analysis results and resulting figure files. The source folder also includes a tutorial and installation notes.</p> <p>For a system running Ubuntu 21.04 there is a slightly modified command line needed: java --module-path /usr/share/openjfx/lib –add-modules=javafx.base,javafx.controls,javafx.fxml,javafx.graphics,javafx.media,javafx.swing,javafx.web -jar GUIFocalAdhesionOnly.jar</p>
Advancing source tracking: systematic review and source-specific genome database curation of fecally shed prokaryotes
<p>This repository contains several files describing the data discussed in Lindner et al's "Advancing source tracking: systematic review and source-specific genome database curation of fecally shed prokaryotes". </p> <ol> <li>"database.fna" = concatenation of the (draft or complete) genome sequences described in the paper (n=12,730 source associated prokaryotic genomes) which passed quality checks and are species-level representatives (i.e., dereplicated at 95% ANI).</li> <li>"gdef.txt" = a manifest describing which sequences belong to which genomes.</li> <li>"sources.txt" = a manifest describing which genomes belong to which sources.</li> </ol> <p>The sources this database covers:</p> <table> <tbody> <tr> <td>Source Category</td> <td>Species-level <br>genome count</td> <td>Source-specific<br>species-level <br>genome count</td> </tr> <tr> <td>Bird</td> <td>56</td> <td>40</td> </tr> <tr> <td>Cat</td> <td>86</td> <td>6</td> </tr> <tr> <td>Chicken</td> <td>1314</td> <td>887</td> </tr> <tr> <td>Cow</td> <td>39</td> <td>15</td> </tr> <tr> <td>Dog</td> <td>139</td> <td>56</td> </tr> <tr> <td>Pig</td> <td>2764</td> <td>2035</td> </tr> <tr> <td>Ruminant</td> <td>740</td> <td>714</td> </tr> <tr> <td>Human</td> <td>4484</td> <td>3350</td> </tr> <tr> <td>Wastewater</td> <td>3108</td> <td>3097</td> </tr> </tbody> </table> <p> </p> <p>See publication for further details. </p>
Table 2 in Fission track dating of obsidian source samples from the Willaumez Peninsula, Papua New Guinea and eastern Australia
<p>Table 2. Results for fission track dating of Papua New Guinean obsidians.1</p><table><tbody><tr><th></th><th></th><th>spontaneous</th><th></th><th>induced</th><th>neutron</th><th></th></tr></tbody><tbody><tr><th>sample</th><td>ns</td><td>trackdensity</td><td>nj</td><td>trackdensity</td><td>fluence</td><td>age±</td></tr><tr><th></th><td>(t)</td><td>(tcm2)</td><td>(t)</td><td>(x104tcm2)</td><td>(X1015 ncm-2)</td><td>(a)</td></tr><tr><th>Baki</th><td>11</td><td>17</td><td>2933</td><td>5.54</td><td>1.644±0.014</td><td>30198±8906</td></tr><tr><th>Kutau</th><td>15</td><td>14</td><td>7094</td><td>5.16</td><td>1.644±0.014</td><td>26707±7688</td></tr></tbody></table><p>1 ns and nj represent the number of spontaneous and induced tracks counted, respectively. The induced track density has been evaluated by means of the population subtraction method. Ages are calculated by using the following constants (Ao; = 1.551 x 1O-lOa·1, <i>238U/235U</i> = 137.88, O"f = 580.2 b, Af = 7.03 x 1O.17a-1). The thermal neutron fluence is determined from both two calibrated mica and Makrofol detectors in contact with glass dosimeters (Coming CNl, CN2) and two metallic monitors (Co, Au). All corrected ages are weighted means of values in the plateau region.</p>
Table 3 in Fission track dating of obsidian source samples from the Willaumez Peninsula, Papua New Guinea and eastern Australia
<p>Table 3. Results of fission track dating of Australian obsidian samples; ns' nj+s represent the number of spontaneous and induced+spontaneous tracks being counted, respectively. See Table 2 for constants. (*) In this case the corrected age is determined by using the correction curve of sample AU602.</p><table><tbody><tr><th></th><th>spontaneous</th><th></th><th>induced</th><th>neutron</th><th></th><th>apparentheatingcorrected</th></tr><tr><th>sample</th><th>ns trackdensity (t) (x104tcm-2)</th><th>lli+s (t)</th><th>trackdensity (x105tcm-2)</th><th>fluence (X1015 ncm-2)</th><th>Ds/Di</th><th>age±0" (Ma)</th><th>hours at 140°C</th><th>age±0" (Ma)</th></tr><tr><th>Far North Queensland (FNQ)</th></tr></tbody><tbody><tr><th>AU602</th><td>2533 8.17±0.16</td><td>3736</td><td>3.51 ±0.07</td><td>2.431 ±0.016</td><td>0.63 ±0.06</td><td>33.9± 1.1</td><td>60</td><td>86.9±3.8</td></tr><tr><th>AU603</th><td>1390 7.25 ± 0.19</td><td>3666</td><td>3.23 ±0.07</td><td>2.431 ± 0.016</td><td>0.54 ± 0.05</td><td>32.7 ± 1.3</td><td>(*)</td><td>88.5 ± 9.0</td></tr><tr><th>AU612</th><td>278 10.86±0.65</td><td>1002</td><td>4.13 ±0.15</td><td>2.431 ±0.016</td><td>0.68±0.07</td><td>38.2±3.2</td><td>(*)</td><td>92.3 ± 10.6</td></tr><tr><th>AU662</th><td>122 4.24±0.38</td><td>608</td><td>1.69 ±0.09</td><td>1.644 ±0.014</td><td>0.53 ±0.10</td><td>24.7 ± 3.1</td><td>70</td><td>85.5 ± 5.9</td></tr><tr><th>New South Wales (NSW</th></tr><tr><th>AUlOl</th><td>273 2.32±</td><td>0.14</td><td>8982.57</td><td>±0.092.431</td><td>±0.016</td><td>0.79±0.06</td><td>13.1</td><td>TODO</td></tr><tr><th>AUl02</th><td>95 1.48±</td><td>0.15</td><td>7602.23</td><td>±0.092.431</td><td>0.016</td><td>TODO</td><td>9.7</td><td>TODO</td></tr></tbody></table>
EMAC-MESSy source segregated CH4 mixing ratios simulated along CARIBIC flight tracks
<p>The *.zip archives contain netCDF data files with EMAC-model simulated CH4 mixing ratios (nmol/mol) along CARIBIC-flight tracks during the years 1997 through 2016 in the upper troposphere and lowermost stratosphere.</p> <p> </p> <p>CARIBIC2org.zip:</p> <p>Every .nc file contains time, lon, lat, and track pressure of the flight simulation and the vertical column of mixing ratios in hybride coordinates total CH4 and eleven source segregated (tagged) contributions. For a best fit of the observations the .nc files have to be multiplied with the scale factors in column 3.</p> <p> </p> <p>CARIBIC_tag_emi_org*CARIBIC2.nc:</p> <p>Tracer: variable name:</p> <p>CH4 total tracer_gp_CH4_fx</p> <p> </p> <p>CH4 tagged: variable name: scale:</p> <p>animals tracer_gp_CH4_fx_e01_a01 0.87</p> <p>bogs tracer_gp_CH4_fx_e02_a01 1.04</p> <p>coal tracer_gp_CH4_fx_e03_a01 0.87</p> <p>gas tracer_gp_CH4_fx_e04_a01 0.89</p> <p>landfills tracer_gp_CH4_fx_e05_a01 0.95</p> <p>oil tracer_gp_CH4_fx_e06_a01 0.91</p> <p>rice tracer_gp_CH4_fx_e07_a01 1.04</p> <p>swamps tracer_gp_CH4_fx_e08_a01 1.13</p> <p>termits tracer_gp_CH4_fx_e09_a01 1.13</p> <p>biomass burn tracer_gp_CH4_fx_e10_a01 1.13</p> <p>biofuel tracer_gp_CH4_fx_e11_a01 1.13</p> <p> </p> <p>CARIBICric.zip, CARIBICsha.zip: CARIBICtro.zip:</p> <p>In these arcives every .nc file contains time, lon, lat, and track pressure of the flight simulation and the simulated mixing ratios along the flight tracks for the post 2006 CH4 increments to be added to the above totals. The increments have to be scaled with the factors in colums 3 for an optimal fit of the observations.</p> <p>CARIBICric.zip: CARIBIC_tag_emi_ric*.nc</p> <p>Tracer: variable name: scale:</p> <p>CH4 rice tracer_gp_CH4_fx 0.48</p> <p> </p> <p>CARIBICsha.zip: CARIBIC_tag_emi_sha*.nc</p> <p>Tracer: variable name: scale:</p> <p>CH4 shale gas tracer_gp_CH4_fx 0.48</p> <p> </p> <p>CARIBICtro.zip: CARIBIC_tag_emi_tro*.nc</p> <p>Tracer: variable name: scale:</p> <p>CH4 tropics tracer_gp_CH4_fx 0.04</p> <p> </p> <p>The mixing ratios are based on emission amounts of 20.5 Tg/CH4/y for each, rice-, shale gas fracking-, and tropical wetlands. The best fit of the observations is a combination of them multiplied with the scale factors in column 3.</p> <p> </p> <p> </p> <p>The corresponding CH4 total mixing ratios (together with other species) are available on demand via <a href="http://www.caribic-atmospheric.com/">http://www.caribic-atmospheric.com</a> "Data access".</p> <p> </p> <p>References:</p> <p>This study:</p> <p>Zimmermann, P. H., Brenninkmeijer, C. A. M., Pozzer, A., Jöckel, P., Winterstein, F., Zahn, A., Houweling, S., and Lelieveld, J.: Model simulations of atmospheric methane (1997–2016) and their evaluation using NOAA and AGAGE surface and IAGOS-CARIBIC aircraft observations, Atmos. Chem. Phys., 20, 1–23, <a href="https://doi.org/10.5194/acp-20-1-2020">https://doi.org/10.5194/acp-20-1-2020</a> ,2020</p> <p>The CARIBIC project:</p> <p>Brenninkmeijer, C. A. M., Crutzen, P., Boumard, F., Dauer, T., Dix, B., Ebinghaus, R., Filippi, D., Fischer, H., Franke, H., Fries, U., Heintzenberg, J., Helleis, F., Hermann, M., Kock, H. H., Koeppel, C., Lelieveld, J., Leuenberger, M., Martinsson, B. G., Miemczyk, S., Moret, H. P., Nguyen, H. N., Nyfeler, P., Oram, D., O'Sullivan, D., Penkett, S., Platt, U., Pupek, M., Ramonet, M., Randa, B., Reichelt, M., Rhee, T. S., Rohwer, J., Rosenfeld, K., Scharffe, D., Schlager, H., Schumann, U., Slemr, F., Sprung, D., Stock, P., Thaler, R., Valentino, F., van Velthoven, P., Waibel, A., Wandel, A., Waschitschek, K., Wiedensohler, A., Xueref-Remy, I., Zahn, A., Zech, U., and Ziereis, H.: Civil Aircraft for the regular investigation of the atmosphere based on an instrumented container: The new CARIBIC system, Atmos. Chem. Phys., 7, 4953-4976, doi:10.5194/acp-7-4953-2007, 2007.</p> <p>EMAC atmospheric chemistry model:</p> <p>Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717-752, doi:10.5194/gmd-3-717-2010, 2010.</p> <p> </p> <p> </p> <p> </p>
Typhoon track tracking and forecasting algorithm based on multi-source information
<p>Typhoon track tracking and forecasting algorithm based on multi-source information</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Tracking and Exploring the Source of Viral REbound
ClinicalTrials.gov study NCT03117985. IPD Sharing: NO. Countries: 1. Publications: 1.
Synthetic and real datasets for "Seismic source tracking with six degree-of-freedom ground motion observations"
<p>Synthetic datasets for the 2D and 3D rupture tracking and real datasets for the traffic noise tracking used in the manuscript "Seismic source tracking with six degree-of-freedom ground motion observations". The README file describes the data structures.</p>
Data from: Tracking the origins of fly invasions; using mitochondrial haplotype diversity to identify potential source populations in two genetically intertwined fruit fly species (Bactrocera carambolae and Bactrocera dorsalis [Diptera: Tephritidae])
Bactrocera carambolae Drew and Hancock and B. dorsalis (Hendel) (Diptera: Tephritidae) are important pests of many fruits. These flies have been spread across the world through global travel and trade, and new areas are are at risk of invasion. Whenever new invasive populations are discovered, quick and accurate identification is needed to mitigate the damage they can cause. Determining invasive pathways can prevent further spread of pests as well as subsequent reinvasions through the same pathway. Molecular markers can be used for both species identification and pathway analysis. We analyzed 1601 individuals from 18 populations using 765 base pairs of the mitochondrial cytochrome oxidase I (COI) gene to infer the haplotype diversity and population structure within these flies from across their native and invasive ranges. We analyzed these samples by either grouping by species or geographic populations due to the genetic similarity in the mitochondrial genome. We found no genetic structure between B. dorsalis and B. carambolae and our findings suggest recent and most likely ongoing, genetic exchange between these two species in the wild. Hyper-diverse mitochondrial genetic diversity in the native range suggests large population sizes and relatively high mutation rates. Only 52% of the haplotypes found in the trap captures from California are shared with haplotypes from flies found in our global survey, indicating significant genetic diversity in the native range that is missing from our samples. However, these results provide a foundation for the accurate determination of the provenance of invasive populations around the world.
Data from: Using host-associated differentiation to track source population and dispersal distance among insect vectors of plant pathogens
Open the record for dataset details and reuse information.
Data from: Tracking the origins of fly invasions; using mitochondrial haplotype diversity to identify potential source populations in two genetically intertwined fruit fly species (Bactrocera carambolae and Bactrocera dorsalis [Diptera: Tephritidae])
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Data Corpus for the IEEE-AASP Challenge on Acoustic Source Localization and Tracking (LOCATA)
<p>This repository contains the final release of the development and evaluation datasets for the LOCATA Challenge.</p> <p>The challenge of sound source localization in realistic environments has attracted widespread attention in the Audio and Acoustic Signal Processing (AASP) community in recent years. Source localization approaches in the literature address the estimation of positional information about acoustic sources using a pair of microphones, microphone arrays, or networks with distributed acoustic sensors. The IEEE AASP Challenge on <strong>acoustic source LOCalization And TrAcking (LOCATA)</strong> aimed at providing researchers in source localization and tracking with a framework to objectively benchmark results against competing algorithms using a common, publicly released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment.</p> <p>Four different microphone arrays were used for the recordings, namely:</p> <ul> <li>Planar array with 15 channels (DICIT array) containing uniform linear sub-arrays</li> <li>Spherical array with 32 channels (Eigenmike)</li> <li>Pseudo-spherical array with 12-channels (robot head)</li> <li>Hearing aid dummies on a dummy head (2-channel per hearing aid).</li> </ul> <p>An optical tracking system (OptiTrack) was used to record the positions and orientations of talker, loudspeakers and microphone arrays. Moreover, the emitted source signals were recorded to determine voice activity periods in the recorded signals for each source separately. The ground truth values are compared to the estimated values submitted by the participants using several criteria to evaluate the accuracy of the estimated directions of arrival and track-to-source association. </p> <p>The datasets encompass the following six, increasingly challenging, scenarios:</p> <ul> <li><strong>Task 1:</strong> Localization of a single, static loudspeaker using static microphones arrays</li> <li><strong>Task 2:</strong> Multi-source localization of static loudspeakers using static microphone arrays</li> <li><strong>Task 3:</strong> Localization of a single, moving talker using static microphone arrays</li> <li><strong>Task 4:</strong> Localization of multiple, moving talkers using static microphone arrays</li> <li><strong>Task 5:</strong> Localization of a single, moving talker using moving microphone arrays</li> <li><strong>Task 6:</strong> Multi-source localization of moving talkers using moving microphone arrays.</li> </ul> <p>The development and evaluation datasets in this repository contain the following data:</p> <ul> <li>Close-talking speech signals for human talkers, recorded use DPA microphones</li> <li>Distant-talking recordings using four microphone arrays: <ul> <li>Spherical Eigenmike (32 channels)</li> <li>Pseudo-spherical prototype NAO robot (12 channels)</li> <li>Planar DICIT array (15 channels)</li> <li>Hearing aids installed in a head-torso simulator (4 channels)</li> </ul> </li> <li>Ground-truth annotations of all source and microphone positions, obtained using an OptiTrack system of infrared cameras. The ground-truth positions are provided at the frame rate of the optical tracking system</li> </ul> <p>The following software is provided with the data:</p> <ul> <li>Matlab code to read the datasets: <a href="https://github.com/cevers/sap_locata_io">github.com/cevers/sap_locata_io</a></li> <li>Matlab code for performance evaluation of localization and tracking algorithms: <a href="https://github.com/cevers/sap_locata_eval">github.com/cevers/sap_locata_eval</a></li> </ul> <p>For further information, see:</p> <ul> <li>C. Evers, H. W. Löllmann, H. Mellmann, A. Schmidt, H. Barfuss, P. A. Naylor, W. Kellermann<br> <em>"</em>The LOCATA Challenge: Acoustic Source Localization and Tracking<em>," </em>in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>, vol. 28, pp. 1620-1643, 2020, doi: <a href="https://doi.org/10.1109/TASLP.2020.2990485">10.1109/TASLP.2020.2990485</a></li> <li>Documentation: <a href="https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf">https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf</a></li> </ul>
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