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13,021 results for “Localization”
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN</strong>".</p> <p>Preprint: <a href="https://arxiv.org/abs/1908.05085">https://arxiv.org/abs/1908.05085</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8970177">https://ieeexplore.ieee.org/document/8970177</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen and Weyn, Maarten.</p>
Blood Exposome Database: MetFrag Local CSV
<p>This is a local CSV file of the Blood Exposome Database (<a href="http://bloodexposome.org/">http://bloodexposome.org/</a>) for MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>).</p> <p>Data was trimmed to necessary columns from parent-mapped TSV provided by Dinesh Barupal. Approx. 15 entries containing elements not processed by MetFrag were removed. Entries with singly charged formulas had the charge removed from the formula to produce results consistent with other MetFrag files (where neutral formula is required; no adjustment for +/-H was performed so these remained consistent with the mass entries with minimum manipulation - see xlsx file for traceback). Subsequent versions could be adjusted for different behaviour if desired. </p> <p>This file is for users wanting to integrate the latest Blood Exposome Database into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Please credit the data source in any use of this file as the licence is CC-BY: <a href="http://bloodexposome.org/">http://bloodexposome.org/</a></p>
Evaluating Changes in the Local Protein Physicochemical En-vironment induced by Molecular Dynamics Simulation
<p><span>Mutation of a single amino acid residue may significantly affect the structure and function of an entire protein. The effect of single amino acid substitutions can be assessed by examining the physicochemical environment surrounding the amino acid of interest, an emerging form of quantification of which is multidimensional tensors. However, the effect with respect to a protein variant’s inherent dynamics in tensor space is rarely assessed despite the potential importance of this form of analysis in revealing local physicochemical properties of the protein and response to mutation. Using the wild-type and 936 mutant structures of the protein domain 1pga, the present research evaluated the effects of local protein context and single amino acid substitutions on molecular dynamics simulation-derived structural distributions via the use of tensors capturing a range of biochemical properties. It was observed that the extent of simulated </span><span>physicochemical</span><span> variation local to a substituted amino acid is positively associated with local mechanical stiffness, loss of protein thermostability and decreased local hydrophobicity. In addition, it was observed that the largest tensor variation occurs in densely-packed, hydrophobic core-associated regions of protein structures. In summary, the pattern of tensor change aligns with prior knowledge about protein stability and physicochemical properties.</span></p>
Measurement Dataset of Thermal Fault Emulation of a 46Ah High-Power Kokam Nano Pouch Cell via Uniform and Local Heating
<h1>Preface</h1> <p>This dataset contains experimental data that supplement the article <em>Thermal fault detection by changes in electrical behaviour in lithium-ion cells </em>(<a href="https://doi.org/10.1016/j.jpowsour.2021.229572" target="_blank" rel="noopener">10.1016/j.jpowsour.2021.229572</a>) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see <a href="https://doi.org/10.17632/g443f7cn7p.2" target="_blank" rel="noopener">10.17632/g443f7cn7p.2</a>) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations. <em><br></em></p> <p>The published data belongs to the master thesis <em>Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink</em> <em>(2020), TU Clausthal</em> that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).</p> <h1>Structure</h1> <p>The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:</p> <table> <tbody> <tr> <td><strong>File type</strong></td> <td><strong>Content</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>*.png</td> <td>Simple graph of the provided data.</td> <td>Missing values are interpolated.</td> </tr> <tr> <td>*.csv</td> <td>Tabular data of the dataset.</td> <td>Columns are separated by ";", the decimal point is ".".</td> </tr> <tr> <td>*.pickle</td> <td>Pickled object of a <a href="https://pandas.pydata.org/docs/index.html" target="_blank" rel="noopener">pandas</a> dataframe (Python) of the data. Preserve index and data types.</td> <td>Pickled with pandas version 2.2.2 using the pickle protocol 5</td> </tr> </tbody> </table> <p>The index and column names of the tabular time series have the following name scheme: X_Y_Z </p> <table> <tbody> <tr> <td><strong>Placeholder</strong></td> <td><strong>Description</strong></td> <td><strong>Example</strong></td> </tr> <tr> <td>X</td> <td>Quantity symbol</td> <td>U for voltage, I for current</td> </tr> <tr> <td>Y</td> <td>[optional] Additional index</td> <td><em>meas </em>for measured quantities</td> </tr> <tr> <td>Z</td> <td>Unit</td> <td>s for seconds, V for volt</td> </tr> </tbody> </table> <h1>Content</h1> <p>The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in <em>SensorPositions.zip</em> for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used. </p> <p>For details on the experimental setup, please refer to the method section of the linked article. </p> <h2>1. Validation</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current for WLTC emulation</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_meas_C</td> <td>Cell surface temperature</td> </tr> </tbody> </table> <h2>2. ThermalCalibration</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted. </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>3. UniformThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>4. LocalThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td> <p>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</p> </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table>
Local Earthquake Tomography of the Alpine Region from 24 Years of Data - DELIVERABLES
<h1><strong>Local Earthquake Tomography of the Alpine Region from 24 Years of Data</strong></h1> <p>M. Bagagli(1), I. Molinari(2), T. Diehl(3), E. Kissling(4)</p> <p><em>(1) Dipartimento Scienze della Terra, Università di Pisa, 56126 Pisa, Italy</em><br><em>(2) Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, 40127 Bologna, Italy</em><br><em>(3) Swiss Seismological Service, ETH Zurich, 8006 Zürich, Switzerland</em><br><em>(4) Institute of Geophysics, Department of Earth Sciences, ETH Zürich, 8006 Zürich, Switzerland</em></p> <p>mail-to: matteo.bagagli@dst.unipi.it<br>date: 08.11.2024<br>version: 1.0</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>This repository contains the all the deliverables of the aforementioned manuscript.<br>The folder is organized into subfolders for the relative tasks.</p> <p>- Min1D_StatDelays<br>- 3Dtomo<br>- EMSC_Catalog_May2007_Dec2015<br>- tomo2plt_scripts<br>- inventories</p> <p>For additional details, we refer the reader to the main manuscript and its supplementary materials.</p>
Sharpening emitter localization in front of a tuned mirror - NPC dataset
<p>This is a depository for two single molecule localisation microscopy datasets of nuclear pore complex (NPC) structures for single particle averaging. The data was published in: </p> <p>Heil, H.S., Schreiber, B., Götz, R. <em>et al.</em> Sharpening emitter localisation in front of a tuned mirror. <em>Light Sci Appl</em> <strong>7, </strong>99 (2018). https://doi.org/10.1038/s41377-018-0104-z</p> <p>Both datasets have two different levels of localisation precision as one is a conventional STORM experiment and the second a mirror-enhanced STORM experiment. A detailed description of the sample preparation and imaging conditions can be found in the related publication. In short the NPC structures are placed on the surface of a glas coverslip or nano-mirror coated coverslip by manual isolation and spreading of nuclear envelopes from xenopus laevis oocytes, fixed and stained by indirect immunolabeling. The primary antibody targets GP210, the secondary F(ab')<sub>2</sub> fragment is conjugated with Alexa Fluor 647. </p> <p>In this depository I'm providing the raw images data, localisation data and super-resolved reconstruction for the two experiments, as well as the localisation data and super-resolved reconstruction of single NPC rings. </p> <p>I'm also providing a MatLab script that allows to select single NPC positions in the super-resolved image and export the localization data of the single NPC ROI: <strong>P01_ImageAlignment_PickElements.m</strong></p> <p>Information about the dataset is also available here: <strong>NPC Image Alignment Dataset_Info.pdf.</strong></p> <p>Image parameters: 102 nm pixel size, EM Gain 100, Photoelectrons per A/D count: 15.01</p> <p>Column structure of the localisation text files: </p> <p>Id,Frame, x [nm], y [nm], sigma [nm], intensity [photon], offset [photon], bkgstd [photon], chi2, Uncertainty [nm], detections</p> <p>Files: </p> <ul> <li><strong>NPCData_glass_EPI.tif</strong></li> </ul> <p>-> NPC on glass coverslip, low power EPI illumination, widefield image, 20 ms exposure</p> <ul> <li><strong>NPCData_glass_STORM.tif</strong></li> </ul> <p>-> NPC on glass coverslip, high power EPI illumination, 5 ms exposure, 20000 frames</p> <ul> <li><strong>NPCData_glass_STORM_loc.csv</strong></li> </ul> <p>-> ThunderSTORM Localisation data of NPCData_glass_STORM.tif, parameters specified NPCData_glass_STORM_loc-protocol.txt</p> <ul> <li><strong>NPCData_glass_STORM_20xNormalizedGaussian.tif</strong></li> </ul> <p>-> 20x Nomalized Gaussian reconstruction of localization data from NPCData_glass_STORM.tif (ThunderSTORM), pixelsize 5.1 nm</p> <ul> <li><strong>NPCData_glass_STORM_singleRings.zip</strong></li> </ul> <p>-> Localisation data and 20x 20x Nomalized Gaussian reconstruction of single NPC ROIs picked out of the NPCData_glass_STORM dataset, ROI size is 240*240 nm<sup>2</sup></p> <ul> <li><strong>NPCData_nanomirror_EPI.tif</strong></li> </ul> <p>-> NPC on nanomirror coated coverslip, low power EPI illumination, widefield image, 20 ms exposure</p> <ul> <li><strong>NPCData_nanomirror_STORM.tif</strong></li> </ul> <p>-> NPC on nanomirror coated coverslip, high power EPI illumination, 5 ms exposure, 20000 frames</p> <ul> <li><strong>NPCData_nanomirror_STORM_loc.csv</strong></li> </ul> <p>-> ThunderSTORM Localisation data of NPCData_nanomirror_STORM.tif, parameters specified NPCData_nanomirror_STORM_loc-protocol.txt</p> <ul> <li><strong>NPCData_nanomirror_STORM_20xNormalizedGaussian.tif</strong></li> </ul> <p>-> 20x Nomalized Gaussian reconstruction of localisation data from NPCData_nanomirror_STORM.tif (ThunderSTORM), pixelsize 5.1 nm</p> <ul> <li><strong>NPCData_nanomirror_STORM_singleRings.zip</strong></li> </ul> <p>-> Localisation data and 20x 20x Nomalized Gaussian reconstruction of single NPC ROIs picked out of the NPCData_nanomirror_STORM dataset, ROI size is 240*240 nm<sup>2</sup></p>
Robust Acoustic Reflector Localization for Robots
<p>In this repository, we share our MATLAB code and dataset used to perform the experiments listed within our paper "<strong>Robust Acoustic Reflector Localization for Robots.</strong>"</p>
Data supplement for "Localized states in coupled Cahn-Hilliard equations"
<p>Data set and plotting codes for all figures of "Localized states in coupled Cahn-Hilliard equations". Additionally, it includes Matlab codes for numerical path continuation and python codes for time simulation which can be used to reproduce the data.</p> <p>For more information, please see the included README.md</p>
RAW SEM images mosaics dataset for the HRDIC strain localization study in shot peened Ni superalloy
<p>We used a FEI Magellan HR 400L FE-SEM with a theoretical resolution of ≤ 0.9 nm at <1kV and ≤ 0.8 nm at ≥ 5kV to take backscattered electron images of the fine, homogeneous distributed gold speckle pattern obtained by remodelling of a thin gold layer previously deposited on the polished sample surface. The images were obtained at a working distance of 3.5 mm, 5 kV and 0.8 nA beam current. Mosaics of 30x15 images were used to cover 950x420 µm<sup>2</sup>. Each image contains 2048 x 1768 pixels and has a horizontal field of view of 43 µm. The images were overlapped by 20% to enable easy stitching prior to the digital image correlation. We obtained 7 mosaics, one before tensile testing and 6 after each deformation step.</p> <p>This set of images at different strain steps is coupled with the EBSD data set in https://doi.org/10.5281/zenodo.4730184, the HRDIC strain maps in http://doi.org/10.5281/zenodo.4728016 and data visualisation scripts in http://doi.org/10.5281/zenodo.4727939</p> <p>0_def corresponds to the undeformed sample, while 1_def to 6_def were obtained after each deformation step.</p>
Prioritizing forestation based on biogeochemical and local biogeophysical impacts - data
<p>Output data produced in "Prioritizing forestation based on biogeochemical and local biogeophysical impacts"</p> <p>Contact: michael.gregory.windisch@alumni.ethz.ch; edouard.davin@wyssacademy.org</p> <p>Content: Global output data of BGC, BGP, and combined effect of forestation and forest conservation in NetCDF4 files of 0.083° resolution</p> <p>Naming Key:<br> {effect}_{LUaction}_{season}_TCR_{TCR}_{stat}.nc</p> <p>Naming List:<br> effect_list = ["dC","dT","full"] # BGC only, BGP only, Combined effect<br> LUaction_list = ["defor", "refor"] # Forest conservation action, Forest establishment action<br> season_list = ["Annual", "JJA", "DJF"] # Yearly values, Boreal summer values (June, July, August), Boreal winter values (December, January, February)<br> TCR_list = ["local", "global"] # Local climate response as translator to CO2 equivalent, Global climate response as translator to CO2 equivalent<br> stat_list = ["median", "STD"] # Median values between all input datasets, Standard deviation values between all input datasets </p>
Remote and Local Processes Controlling Decadal Sea Ice Variability in the Weddell Sea
<p>These datasets are based on the 270-yr simulation results of CTR and SAOWED experiments, which include annual average of atmospheric and ocean variables used to make figures in a paper by Morioka and Behera (2021).</p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</p>
The Quest for the Missing Dust: New Herschel Maps of Local Group Galaixes (LMC, SMC, M31, M33) that Restore Previously-Missed Extended Emission, Along With SED-Fitting Results, Hydrogen Gas Maps, and Swift UV Observations
<p>Here we provide the data products from publications:</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies</em>, ApJ 921 35</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies</em>, ApJ 946 42</p> <p>This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.</p> <p> </p> <p>For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.</p> <p>For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 <span>\(\mu\)</span>m bands, and the SPIRE 250, 350, and 500 <span>\(\mu\)</span>m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean).</p> <p> </p> <p>We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (SED_Sigma_Mass.fits), dust temperature (SED_Temp.fits), beta 1 (SED_Beta1.fits), beta 2 (SED_Beta2.fits), break wavelength (SED_Break.fits), and 500 <span>\(\mu\)</span>m excess (SED_Excess500.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.</p> <p>Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 <span>\(\mu\)</span>m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.</p> <p>Furthermore, provide the Swift-UVOT maps used in Paper II. This data is provided for Swift-UVOT bands W1, W2, and M2. For each band, we provide a FITS file containing 3 extensions. Extension 1 (SURF_BRI) provides the map of surface brightness in MJy/sr (converted using the Swift-UVOT zero points given in Breeveld et al., 2011). Extension 2 (RATE) provides the map of count rate (in photons/sec). Extension 3 (EXP) provides the map of exposure time (in sec). The maps for the LMC and SMC are those presented in Hagen et al. (2017). The maps for M31 and M33 are were reduced following the same process as those in Hagen et al. (2017), and will be fully presented in Decleir et al. (in prep.), but are provided here for the purposes of reproducibility.</p> <p>Lastly, for each galaxy, we provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits). None of the maps presented have had deprojection corrections applied</p> <p> </p>
3D Charged Particles Dataset - Roto-translated Local Coordinate Frames for Interacting Dynamical Systems
<p>This repository contains the "<strong>3D charged particles</strong>" dataset from the paper</p> <blockquote> <p><strong>Roto-translated Local Coordinate Frames for Interacting Dynamical Systems</strong><br> <a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>, <a href="https://menaveenshankar.github.io/">Naveen Shankar Nagaraja</a>, <a href="https://egavves.com/">Efstratios Gavves</a><br> NeurIPS 2021<br> <a href="https://arxiv.org/abs/2110.14961">https://arxiv.org/abs/2110.14961</a><br> <a href="https://github.com/mkofinas/locs">https://github.com/mkofinas/locs</a></p> </blockquote> <p>It contains simulations of trajectories of 5 charged particles in 3 dimensions, interacting via Coulomb forces.</p> <p>There are 30,000 simulations for training, 5,000 for validation, and 5,000 for testing.</p> <p>Train and validation simulations last for 99 timesteps, while test simulations last for 99 timesteps.</p> <p>The features comprise positions and velocities of particles, while edges describe the product of pairwise charges.</p>
Spectral response of disorder-free localized lattice gauge theories
<p>Raw data for all figures in the manuscript "Spectral response of disorder-free localized lattice gauge theories"</p>
ARLCL: Anchor-free Ranging-Likelihood-based Cooperative Localization
<p>This *dataset (68440 files of approx. 30GB unzipped) includes all Bluetooth Low Energy (BLE) Received Signal Strength (RSS) samples used for the evaluation of the Anchor-free Ranging-Likelihood-based Cooperative Localization (ARLCL) method. Each DB file corresponds to an evaluated scenario of a unique combination of nodes/samples (i.e. reflecting different swarm deployments and measurement qualities). The maximal setting is 21 Raspberry Pis and 20 RSS measurement samples. Each DB file contains the true positions of the participating nodes, 100 resampling cases, and one exceptional case (#RSS_0#) where all samples have been used (this case has not been considered in the paper).<br> <br> The specific structure of the DB files is required by our open-sourced Cooperative Localization Optimizer (<a href="https://github.com/CDS-Bern/ARLCL-Optimizer"><em><strong>ARLCL-Optimizer</strong></em></a>), and also the <strong>Mass Spring</strong> and <strong>Maximum Likelihood - Particle Swarm Optimization</strong> implementations that we used in the paper. These are also provided openly in our repo (<a href="https://github.com/CDS-Bern/ARLCL-Optimizer">https://github.com/CDS-Bern/ARLCL-Optimizer</a>).<br> <br> We encourage future cooperative localization solutions to use our provided dataset/software for comparisons or results reproduction.</p> <p>*The files have been compressed using <a href="https://www.7-zip.org/">7-zip</a>.</p>
Data Archive: Local and Global Order in Dense Packings of Semiflexible Polymers of Hard Spheres
<p>Data archive corresponding to the publication "Local and Global Order in Dense Packings of Semi-flexible 2 Polymers of Hard Spheres" by D. Martinez-Fernandez et al., Polymers 15, 551 (2023); DOI: https://doi.org/10.3390/polym15030551.</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All snapshots have been generated and successively analyzed by the Simu-D software.</p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix -- Demo datasets for the HEELS tutorials
<p>We introduced a new estimator for local heritability, "HEELS", which attains comparable statistical efficiency as the REML estimator (such as those produced by GCTA and BOLT-REML) but only requires summary-level statistics – Z-scores from marginal association tests and the empirical LD. Our method has been implemented into an open-source Python-based command line tool. </p> <p>The datasets released here can be downloaded to test the two main functions of our software package: 1) estimating local heritability; 2) computing the low-dimensional representation of the LD matrix. They are meant to accompany the HEELS tutorials we have posted onto the wiki pages of our github repository: https://github.com/huilisabrina/HEELS/wiki.</p> <p> </p>
A hybrid 100-m global land cover dataset with Local Climate Zones for WRF
<p>This hybrid 100-m CGLC-MODIS-LCZ global land cover dataset is produced for the Weather Research and Forecasting (WRF) model starting from version 4.5. It is based on 1) the Copernicus Global Land Service Land Cover (CGLC, Buchhorn et al., 2021) product resampled to MODIS IGBP classes (CGLC-MODIS), and 2) the global map of Local Climate Zones (LCZ, Demuzere et al., 2022a, b) that describes the urban and built-up land surface. Both the CGLC and LCZ products are available at a 100-m spatial resolution, are representative for the year 2018, and cover -180°W to 180°E and -60°S to 78°N. Remaining areas are filled with the MODIS land cover classes. This dataset has been implemented into the WRF Preprocessing System (WPS) as <a href="https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html">tiled binary data files</a> with <a href="https://github.com/wrf-model/WPS/blob/develop/geogrid/GEOGRID.TBL.ARW_LCZ">a new GEOGRID table entry</a> to allow WRF/WPS users to flexibly use this dataset in their studies particularly for urban modeling applications.</p> <p>To display the dataset in QGIS, <em>cmap_Qgis_CGLC_MOD_LCZ.txt </em>can be used as a color scheme.</p> <p>For more details, please read the technical documentation: <a href="https://doi.org/10.5281/zenodo.7670792">https://doi.org/10.5281/zenodo.7670792</a>.<br> <br> References:</p> <p><em>Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.-E., Li, L., Tarko, A. Copernicus Global Land Service: Land Cover 100m: version 3 Globe 2015-2019: Product User Manual (Dataset v3.0, doc issue 3.4). Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963<br> <br> Demuzere M, Kittner J, Martilli A, et al. A global map of local climate zones to support earth system modelling and urban-scale environmental science. Earth Syst Sci Data. 2022a;14(8):3835-3873. doi:10.5194/essd-14-3835-2022</em></p> <p><em>Demuzere M, Kittner J, Martilli A, et al. (2022). Global map of Local Climate Zones (2.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6364593</em></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.