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13,021 results for “Localization”
Environmental and Mineralogical Characteristics of Tourmaline-bearing localities in the United States
<p><strong>Background</strong></p> <p>This dataset contains the compiled mineralogical records of 458 tourmaline-bearing sites in the United States. Environmental parameters associated with each locality such as surface temperature, precipitation, and geothermal gradient have also been recorded. Geological processes were identified for each locality using site and mineral descriptions from mineral databases and they were also interpreted using machine learning algorithms. Market basket analysis algorithms were utilized to identify patterns within the mineralogical data and these patterns were attributed to the geological processes listed in the dataset.</p> <p><strong>Data Sources</strong></p> <p>Mineral occurrence data were retrieved from Mindat.org for the tourmaline minerals schorl, dravite, elbaite, uvite, and foitite. Mineral descriptions listed in the Handbook of Mineralogy (Anthony et al., 2004) web database aided in the identification of different geological processes. Geothermal gradient data are interpolated from values listed in geothermal temperature studies conducted by Batir et al. (2013), Kron and Stix (1982), and Nathenson and Guffanti (1987). Elevation data were retrieved from the USGS Elevation Point Query Service and the climatic data from the WorldClim dataset. Köppen-Geiger climatic zones were assigned to each locality using data published by Kottek et al. (2006) and made available at http://koeppen-geiger.vu-wien.ac.at/</p> <p><strong>Version Two</strong></p> <p>The second version of this dataset (TourmalineLocalitiesV2.csv) contains the initial tourmaline-bearing sites retrieved from the Mindat mineral database plus 389 randomly generated test localities. The randomly generated locations have all of the same parameters as the initial dataset with the mineralogy being extracted from mineral surface models. Natural neighbor interpolation was used to generate surface models for each individual mineral in the dataset and the mineralogy for each test locality was extracted from the models. The first version of the dataset also did not have coordinates for each site and those have been added in this version. </p>
Data Set htwddKogRob-TSDChangesSim for Localization and Lifelong Mapping
<p>This dataset provides log files recorded in a changed indoor environment with 18 dynamic obstacles. The changes from the original map to the simulated world are highlighted in the figure htwddKogRob-TSDChangesSim_changesHighlighted.png. The total distance travelled in this data set is 179.8 km. The prior knowledge map the robot got to localize and update is shown in htwddKogRob-TSDChangesSim_prior.png and the simulated now changed map is shown in htwddKogRob-TSDChangesSim_groundTruth.png (for both maps: 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m).<br> </p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archives contain ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a simulated environment, the logfiles include both a changed starting position and a ground-truth pose. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Data Set htwddKogRob-TSDReal for Localization and Lifelong Mapping
<p>This dataset represents a 4.7 km long tour (odometry path shown in htwddKogRob-TSDReal_path.png) in an environment whose representation (see map htwddKogRob-TSDReal.png | 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m) is now obsolete. Several static objects have been moved or removed, and there are varying numbers of dynamic obstacles (people walking around).</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archive contains ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfiles hold only the odometry based robot poses. For further information, please refer to the header of the logfiles. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Data Set htwddKogRob-InfReal for Localization and Lifelong Mapping
<p>This small dataset contains real world log files from a 2.2 km long patrol between two points of a previously known map (see htwddKogRob-InfReal.png | 1px <span class="math-tex">\(\widehat{=} \)</span> 0.1m). The environment changes slightly and there are some dynamic obstacles. The figure (see htwddKogRob-InfReal_path.jpg) shows the path driven by the robots according to the real kilometers driven and the patrol points.</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archive contains ascii files, which contain the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfile provides only the odometry based robot poses. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p> <p> </p>
Mechanical data of rotary shear experiments and temperature measurements for the manuscript: "Fast and localized temperature measurements during simulated earthquakes in carbonate rocks"
<p>Mechanical data of rotary shear experiments and temperature measurements</p> <p>Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber).</p> <p>Mechanical data are presented in a tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul> <p> In a separate file, temperature data are presented as tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Temperature from optical fiber in the channel at 1.5 µm : Temperature_1,5 (°C) </li> </ul>
CO excitation, molecular gas density and interstellar radiation field in local and high-redshift galaxies
<p>This dataset includes the SED fitting figures and full sample table produced in the study of Liu et al. (2020, ApJ). Two example figures are shown in the Figure 1 of the paper. And selected columns of the full sample table is shown in the Table 1 of the paper. </p> <p> </p>
Localizing Spherical Fiducials in C‐arm Based Cone‐Beam CT
<p>This dataset was acquired as part of the work described in: "Localizing spherical fiducials in C-arm based cone-beam CT", Z. Yaniv, Med. Phys., Vol. 36(11), pp. 4957-4966, 2009, <a href="https://doi.org/10.1118/1.3233684">doi.org/10.1118/1.3233684</a>.</p> <p>The data includes two phantom imaging studies acquired with a Cone-Beam CT (CBCT) system. Each of the two datasets includes projection images (cine loops) and 3D reconstructions. Additionally, the data includes the CBCT system's projection matrices and MATLAB code for reading the projection images and overlaying epipolar lines onto them. All images are stored in the DICOM format.</p> <p>The full MATLAB code for fiducial localization described in the manuscript is available <a href="https://www.yanivresearch.info/software/cbctSphericalFiducialLocalization.zip">here</a>.</p> <p> </p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Destination Choice Model including panel datausing WiFi localization in a pedestrian facility
<p>A minimal example of a destination choice model including panel data on EPFL campus. It is based on the output of Danalet<em> et al. </em>(2014)<em>.</em> </p> <p>It runs on Pythonbiogeme. Some variables are removed from the dataset due to privacy issues. Thus, some parameters may not be significant.</p>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Spatially-localized X-ray scattering and X-ray microtomography measurements on Moso bamboo
<p><strong>Spatially-localized X-ray scattering and X-ray microtomography measurements on Moso bamboo</strong></p> <p> </p> <p>This data set is originally used in:</p> <p>Ahvenainen, P., Dixon, P. G., Kallonen, A., Suhonen, H., Gibson, L. J., & Svedström, K. (2017). Spatially-localized bench-top X-ray scattering reveals tissue-specific microfibril orientation in Moso bamboo. <em>Plant Methods</em>. <strong>13</strong>:5 DOI: 10.1186/s13007-016-0155-1</p> <p>This data set includes measurements on Moso bamboo (<em>Phyllostachys edulis</em>) performed with two separate set-ups at the Department of Physics, University of Helsinki as described in the above open-access publication. The X-ray microtomography (XMT) measurements, X-ray diffraction tomography (XDT) and localized X-ray scattering (LXS) are done with set-up 1. In LXS, the region-of-interest is selected from a tomographic reconstruction slice based on the XMT measurement using a small X-ray beam (diameter: 200 µm). Additional wide-angle X-ray scattering (WAXS) measurements are conducted with set-up 2 using a larger X-ray beam (diameter approx. 1 mm). </p> <p>The two-dimensional scattering patterns (Pilatus 1M hybrid pixel array detector) and tomographic reconstruction slices obtained with set-up 2 are stored as TIFF-images (.tif). The two-dimensional scattering patterns (MAR345 image plate detector) obtained with set-up 2 are stored as 32-bit RAW files (unsigned integers, 2300 columns, 2300 rows). </p> <p>The novel combined WAXS/XMT set up (set-up 1) is first presented in: Suuronen, J.-P., Kallonen, A., Hänninen, V., Blomberg, M., Hämäläinen, K., & Serimaa, R. (2014). Bench-top X-ray microtomography complemented with spatially localized X-ray scattering experiments. <em>Journal of Applied Crystallography</em>, <strong>47</strong>(1), 471–475. doi:10.1107/S1600576713031105</p> <p>Any queries related to the data set or the related Plant Methods article may be directed to the first author by email:</p> <p>Patrik Ahvenainen, PhD; patrik.ahvenainen@alumni.helsinki.fi</p>
Data and code for "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations"
<p>This record contains data, scripts, and models associated with the publication "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations".</p> <h3>Databases</h3> <p>The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <h3>Models</h3> <p>The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <h3>Primitive structures</h3> <p>Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p> <h3>Tools for analyzing tilt angles</h3> <p><strong><code>standardize-cell-orientation.py</code></strong><br>Converts each frame in `movie.xyz` to the standardized cell setting and writes the resulting trajectory to `movie-standardized.xyz`.<br>This is useful when working with triclinic cells.</p> <p><code><strong>analyze-tilt-angles-for-orthorhombic-cells.py</strong></code><br>Analyzes the distribution of tilt angles for each frame in a trajectory.<br>The analysis assumes that the B-B bonds are oriented along the Cartesian coordinate system either without further rotation or after a "simple" rotation of the entire cell.</p> <p><code><strong>angle-analysis.ovito</strong></code><br>Ovito script that approximately implements the procedure used in <code>analyze-tilt-angles-for-orthorhombic-cells.py</code>.</p>
ForensicVM Windows 11 Virtualization Dataset: Cloud and Local Application Data Retrieval with Virtualization and Password Bypass Challenge
<p>A Windows 11 Pro dataset designed in VirtualBox, complete with local and cloud apps, is to be virtually analyzed for crucial evidence. Bypassing the straightforward 'Bart' password is essential for access, yet original passwords should remain unchanged for others to attempt the same challenge. The task involves determining the password's nature, Bart's motives, identifying involved cloud applications, and extracting data both offline and online. The viability of dead box forensics for complete data retrieval is questioned, alongside what additional information network access could unveil. The challenge includes identifying two financial applications, extracting their data, and gathering cryptocurrency-related information, presented as an engaging forensicVM showcase by Nuno Mourinho, Mario Candeias, and Rogerio Bravo (Escola Superior de Tecnologia e Gestão de Beja, Instituto Politécnico de Beja).</p>
Data for 'Actis: A Strictly Local Union–Find Decoder'
<p>The logical error (failure) and runtime data for the plots in the paper 'Actis: A Strictly Local Union–Find Decoder'.</p>
Awareness campaign video for local communities: Understanding and Preventing Rabies in the Menabe Region, Madagascar
<p>Cette courte vidéo est un outil utilisé pour la sensibilisation contre la rage. </p><p>Elle a été produite dans le cadre d'une recherche opération dans le sud ouest de Madagascar dans la région Menabe.</p><p>Elle présente en langue Malagasy, sous-titrée en Français:</p><p>- Ce qu'est la rage: la rage est une maladie due par une morsure d'un animal enragé ou le léchage de la plaie d'une personne par un animal enragé. Le chien est responsable de la rage dans près de 9 cas sur 10.</p><p>- actions à entreprendre à si une personne a été mordue par un chien</p><p>- les gestes à effectuer si une personne a été mordue par un chien</p><p>- elle insiste sur le fait que chez l'homme, la rage est traitable à 100% avant l'apparition du premier symptôme.</p><p>La vidéo est aussi disponible sur Youtube : </p><p><i>https://www.youtube.com/watch?v=RBK3Uywxszo&ab_channel=DaoudaKassi%C3%A9</i></p>
Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania
<h3><strong>Reference</strong></h3><p>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> </p><h3><strong>Abstract</strong></h3><p>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (<i>P</i> = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.</p><h3><strong>Replication Data</strong></h3><p>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the <i>hh_data</i> folder and includes:</p><ul><li><i>hh_imputed10_2018:</i> anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li><i>hh_compensation_2018:</i> anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li><i>hh_migration_2018:</i> anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li><i>hh_rsdata_2018:</i> extracted remote sensing data at the household geo-location for 2018</li><li><i>hh_land_<strong>2019</strong>:</i><strong> </strong> anonymized household extract for <strong>2019 </strong>of land variables</li></ul><p>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <i>lsms_data</i> folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). </p><p>Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the <i>gis_data</i> folder.</p><p>Finally, our data replication includes several model outputs (found in <i>mod_outputs)</i>, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <i>main_analysis.Rmd</i> script. </p><h3><strong>Replication Code</strong></h3><p>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:</p><ul><li><i>main_analysis.Rmd:</i> main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li><i>compensation.R:</i> analysis of benefits and compensation received directly by households from LSLAs</li><li><i>landvalue.R:</i> analysis of household land values as a function of distance from LSLAs</li><li><i>migration.R:</i> analysis of migration behavior following LSLAs</li><li><i>selection_bias.R:</i> analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>
Deep and complex vascular anatomy in the rat brain described with Ultrasound Localization Microscopy in 3D
<p><strong>Abstract:</strong></p><p>Ultrasound Localization Microscopy (<strong>ULM</strong>) enables imaging microvessels in the brain with a resolution of a few tens of micrometers <i>in vivo</i>. The planar architecture of arterioles and venules was revealed with a 2D ultrasound scanner in the cortex of the rat brain. However, deeper in the brain, where the vascularization becomes tri-dimensional, 2D imaging remains limited by the elevation projection. In this study, volumetric ultrasound imaging was performed in the craniotomized rat brain to yield 3D ULM<i> in vivo</i> within 7.5 min of acquisition with a commercial system. For instance, it highlighted the thalamus or the circle of Willis with small vessels down to 21 µm. Microbubbles tracking also gave access to the 3D velocity vector of blood flow allowing to distinguish flow directions. Volumetric ULM resolved deep complex tri-dimensional vascular structures and was compared to 2D ULM. It is a safe, simple and repeatable system to image wide field of view in the brain.</p><p><strong>Data Description:</strong></p><p>Microbubbles have been detected, localized, and tracking with 3D ultrasound imaging <i>in vivo</i> in a rat brain with skull removal.</p><p>Individual microbubble trajectories are described in 4 columns vectores: <strong>[z, x, y, time]</strong> for each position of the path. Space positions are given in [mm], and times are given in [ms]. Trajectories data are stored in .mat files (<strong>tracks_0xx.mat </strong>and zipped inside <strong>tracks.zip</strong>) as cell arrays.</p><p>Tracks can be binned inside a volumetric grid with the sample code (<strong>ULM_rendering.m</strong>).</p><p><strong>Reference to be cited: </strong>Chavignon, Heiles, Hingot, Orset, Vivien and Couture.</p><p><i>Deep and complex vascular anatomy in the rat brain described with Ultrasound Localization Microscopy in 3D.</i><br> </p>
Law Indexes: New York (Local Laws)
<p>Under <a href="https://www.nysenate.gov/legislation/laws/CNS/A9">Article IX (Local Governments) of the Constitution of the State of New York</a>, adopted in 1963, local governments in the State of New York have the power to adopt local laws. To effectuate the article, the Legislature enacted the <a href="https://www.nysenate.gov/legislation/laws/MHR/">Municipal Home Rule Law</a> (<a href="https://hdl.handle.net/2027/uc1.a0001834712?urlappend=%3Bseq=893%3Bownerid=113368669-899">1963 N.Y. Laws 2697, Ch. 843</a>), which outlines the process that local governments must follow to adopt local laws. <a href="https://www.nysenate.gov/legislation/laws/MHR/27">Section 27 of the Law</a> requires local governments to file local laws with the Secretary of State before they can become effective.</p> <p>This data set contains index records for over 130,000 local laws filed with the Secretary of State, mostly between 1969 and 2003. Pursuant to Freedom of Information Law (FOIL) Request No. DOS-22-02-052, a copy of the index database, created using DataPerfect, was released by the State on March 17, 2022. These records were requested in an effort to expand the geographic scope and detail of information in the Local Geohistory Project, which aims to educate users and disseminate information concerning the geographic history and structure of political subdivisions and local government.</p> <p>This data set complements local law volumes published with session laws through 1973, along with printed indexes covering the years 1974 through 1982. Original local law filings for this time period have been accessioned by the New York State Archives as part of <a href="https://iarchives.nysed.gov/xtf/view?docId=ead/findingaids/13241.xml;query=">Series Number 13241</a>.</p>
Simulated Local Electrical Impedance in Atrial Tissue With Varying Contact Force
<p>In this dataset we can find geometrical setups that served as an input to carry forward electrical impedance simulations with EIDORS. <br>A 3D geometrical models of one ablation catheters combining measurements of local impedance (LI) and contact force (CF) commercially available is included. The objective of these in silico experiments laid on understanding how CF and tissue deformation affect LI measurements.<br>To achieve it, using the catheter against the tissue, several grams of force are applying.<br>The dataset consists of the original geometrical models before deformation and a couple of examples of the deformed one.</p> <h2>Data structure</h2> <ul> <li>geos: original geometries of the catheter and the tissue in stl <ul> <li>catheter.stl</li> <li>tissue.stl</li> </ul> </li> <li>geos_deformed: deformed geometries at 5 and 10 grams, respectively. Includes the catheter, the mesh, and the tissue <ul> <li>5 g <ul> <li>catheter.stl</li> <li>tissue_5g.stl</li> <li>mesh_5g.stl</li> </ul> </li> <li>10 g <ul> <li>catheter.stl</li> <li>tissue_10g.stl</li> <li>mesh_10g.stl</li> </ul> </li> </ul> </li> </ul>
In Silico Local Electrical Impedance Measurements in the Atria
<div>This document describes a dataset provided in the context of the manuscript “In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human” [1].</div> <div> </div> <div>Authors: Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dössel O, Luik A</div> <div> </div> <div>The dataset contains in silico simulation setups and results from forward electrical impedance simulations with EIDORS. Geometrical models include the commercially available ablation catheters IntellaNav MiFi and IntellaNav StPt catheter measuring local impedance (LI). </div> <div>Catheter geometries were embedded in different surrounding conditions of clinical importance. Catheter tissue interaction with and without scar, the insertion of the catheter into a pulmonary vein (PV), the withdrawal into a transeptal sheath, and catheter irrigation were modeled to quantify the respective effect on LI measurements. In vitro and clinical data used for validation purposes are included in the dataset as well.</div> <div> </div> <div>Abbreviations: </div> <div>LI: local impedance, all numbers are given in Ohms</div> <div>MiFi: IntellaNav MiFi catheter</div> <div>PV: pulmonary vein</div> <div>StPt: IntellaNav StPt catheter</div> <div> </div> <div> </div> <div>Simulation results, in vitro measurements and clinically measured traces are provided in the following MATLAB files in the subdirectory „results_LI“:</div> <div> </div> <div>• impConductivities.mat</div> <div>In vitro measurements and simulation results for MiFi and StPt in NaCl solutions of different concentrations as described in section III A of the related publication [1]. The struct "impedance" includes the following fields:</div> <div>⁃ conc: concentration of NaCl solutions from in vitro measurements in weight percentages</div> <div>⁃ cond: conductivities of the NaCl solutions from in vitro measurements in S/m</div> <div>⁃ temp: interpolated temperature curves from in vitro measurements in °C</div> <div>⁃ condSim: different conductivities of the NaCl solutions from in silicon experiments in S/m</div> <div>⁃ LI_MiFi_iV: 41x9 matrix with interpolated in vitro LI measurements with the MiFi catheter in 9 different NaCl solutions and at 41 interpolated temperature values</div> <div>⁃ LI_StPt_iV: 41x9 matrix with interpolated in vitro LI measurements with the StPt catheter in 9 different NaCl solutions and at 41 interpolated temperatures values</div> <div>⁃ LI_MiFi_iV_RT: LI values for different NaCl solutions at room temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_MiFi_iV_BT: LI values for different NaCl solutions at body temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_StPt_iV_RT: LI values for different NaCl solutions at room temperature interpolated from in vitro StPt measurements</div> <div>⁃ LI_StPt_iV_BT: LI values for different NaCl solutions at body temperature interpolated from in vitro StPt measurements</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different NaCl solutions</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different NaCl solutions</div> <div> </div> <div>• impSheath.mat</div> <div>Simulation results and clinical measurements of LI with MiFi and StPt for different overlaps with a transeptal sheath as described in section III B of the related publication [1]. The struct „impSheath“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and distal edge of the sheath in mm. Negative distances describe an insertion of the catheter into the sheath</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_MiFi_cd: 281x2 matrix containing clinical LI measurements with the MiFi catheter in the second column and corresponding time steps in the first column</div> <div>⁃ LI_StPt_cd: 301x2 matrix containing clinical LI measurements with the StPt catheter in the second column and corresponding time steps in the first column</div> <div> </div> <div>• impTissue.mat</div> <div>Simulation results for MiFi and StPt with variable distance and angle between catheter and tissue as described in section III C of the related publication [1]. The struct „impTissue“ contains the following fields:</div> <div>⁃ distance: 25 different distances between catheter tip and endocardial surface in mm</div> <div>⁃ distanceSel: 5 selected distances between catheter tip and endocardial surface in mm</div> <div>⁃ angle: 13 different angles between catheter and endocardial tissue surface in degrees</div> <div>⁃ LI_MiFi_d_alpha: 5x13 matrix with simulated LI values for the MiFi catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_MiFi_d_90: 25 simulated LI values for the MiFi catheter for different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div>⁃ LI_StPt_d_alpha: 5x13 matrix with simulated LI values for the StPt catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_StPt_d_90: 25 simulated LI values for the StPt catheter different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div> </div> <div>• impTissueScar.mat</div> <div>Simulation results for MiFi and StPt interacting with tissue in the presence of scar as described in section III C of the related publication [1]. The struct „impTissueScar“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and endocardial surface for all simulation setups in mm</div> <div>⁃ centerX: horizontal distance between the catheter tip and the center of the line of scar for all simulation setups in mm</div> <div>⁃ LI_MiFi3mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_StPt3mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_MiFi6mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div>⁃ LI_StPt6mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div> </div> <div>• impPV.mat</div> <div>Simulation results for MiFi and StPt insertion into a pulmonary vein (PV) as described in section III D of the related publication [1]. The struct „impPV“ includes the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and tissue surface in mm. Negative distances describe an insertion of the catheter into the vein.</div> <div>⁃ radius: inner radius of the PV in mm</div> <div>⁃ thickness: thickness of the PV tissue in mm</div> <div>⁃ LI_MiFi_d_r_th: 31x4x4 matrix containing the LI simulation results for the MiFi catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div>⁃ LI_StPt_d_r_th: 31x4x4 matrix containing the LI simulation results for the StPt catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div> </div> <div>• impFlush.mat</div> <div>Simulation results for MiFi and StPt flush with NaCl at different flow rates as described in section III E of the related publication [1]. The struct „impFlush“ includes the following fields:</div> <div>⁃ radius: radius of the NaCl spheres at the irrigation holes in mm</div> <div>⁃ LI_MiFi_NaCl: LI extracted from simulations with MiFi catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div>⁃ LI_StPt_NaCl: LI extracted from simulations with StPt catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div> </div> <div>Additionally, exemplary geometrical setups and results are provided as VTK files in the subdirectory „selectedGeometriesAndSimResults“:</div> <div> </div> <div>Each VTK file contains the following data fields:</div> <div>⁃ Ids (point data): integer specifying the Id of the respective vertex</div> <div>⁃ Voltage (point data): electric potential of the respective vertex with respect to a reference potential in mV</div> <div>⁃ Conductivity (cell data): conductivity of the material of the respective cell in S/mm</div> <div>⁃ Current (cell data): current density of the respective cell in nA/mm^2</div> <div>⁃ Ids (cell data): integer specifying the Id of the respective cell</div> <div>⁃ Material (cell data): integer specifying the material of the respective cell (for MiFi setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: mini electrode 1, 8: insulator mini electrode 1, 9: mini electrode 2, 10: insulator mini electrode 2, 11: mini electrode 3, 12: insulator mini electrode 3, 13: tissue, 14: blood, 15: sheath, 16:NaCl, 17: scar tissue; for StPt setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: tissue, 8: blood, 9: NaCl, 10: scar tissue)</div> <div> </div> <div>• mifi.vtk: MiFi catheter in blood </div> <div>• stpt.vtk: StPt catheter in blood</div> <div>• mifiTissue_dist000_angle0000.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 0°</div> <div>• mifiTissue_dist000_angle0450.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 45°</div> <div>• mifiTissue_dist000_angle0900.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 90°</div> <div>• mifiTissue_dist000_angle1350.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 135°</div> <div>• mifiTissue_dist000_angle1800.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 180°</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line3mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 3mm width</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line6mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 6mm width</div> <div>• mifi_PV_d0060_r030_th20.vtk: MiFi catheter 6mm above the endocardial surface with a PV of 3 mm radius and 2mm PV tissue thickness</div> <div>• mifi_PV_d-070_r030_th20.vtk: MiFi catheter inserted into a PV of 3 mm radius and 2mm PV tissue thickness; insertion depth = 7mm</div> <div>• mifi_flush_050-0.50.vtk: MiFi catheter within blood with NaCl spheres of 0.5mm radius at irrigation holes</div> <div>• mifi_sheath_0100.vtk: MiFi catheter within transeptal sheath extracted by 10mm</div> <div> </div> <div>[1] Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dossel O, Luik A. In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human. IEEE Trans Biomed Eng. 2023 Feb;70(2):533-543. doi: 10.1109/TBME.2022.3196545. Epub 2023 Jan 19. PMID: 35925848.</div> <p> </p>
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