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13,021 results for “Localization”
Datasets for "Flavour-selective localization in interacting lattice fermions"
<p>This submission includes the datasets shown in the figures of journal article</p> <p>"Flavour-selective localization in interacting lattice fermions" by D. Tusi et al.<br> DOI: 10.1038/s41567-022-01726-5</p> <p>The naming of the files corresponds to the figure numbering in the original article.</p>
Raw Data - Photoelectrolysis of TiO2 is highly localized and the selectivity is affected by the light
<p>The dataset contains raw data that complements the article:</p> <p>Photoelectrolysis of TiO<sub>2</sub> is highly localized and the selectivity is affected by the light, <em>Chemical Engineering Journal</em>, 2022, 136995.</p> <p>C. Iffelsberger, S. Ng, and M. Pumera*</p> <p>https://doi.org/10.1016/j.cej.2022.136995</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Historical Local Climate Zones (LCZ) maps for Hong Kong (1845-2000)
<p>This dataset contains GIS maps of the historical urbanization of Hong Kong at 12 timeslices from 1845-2000 (1845, 1888, 1895, 1913, 1938, 1945, 1952, 1961, 1975, 1984, 1995, and 2000). These maps were compiled through classification of historical map material, photographs, paintings, and contemporary textual accounts. The maps describe the land cover of the historical urban core of the territory of Hong Kong: Victoria City on Hong Kong Island, and the Kowloon Peninsula on the mainland. Urban form is described in terms of the Local Climate Zones (LCZ) scheme presented by Stewart & Oke (2012).</p> <p>The dataset is currently in the format of a QGIS archive, but other file formats will be made available in the future and can be requested from the authors at any time.</p> <p>For further details on the process of generating the maps and their application, please see Yee and Kaplan (2022).</p> <p> </p> <p>Yee, M., & Kaplan, J. O. (2022). Drivers of urban heat in Hong Kong over the past 116 years. <em>Urban Climate</em>. doi:10.1016/j.uclim.2022/101308</p> <p>Stewart, I. D., & Oke, T. R. (2012). Local Climate Zones for Urban Temperature Studies. <em>Bulletin of The American Meteorological Society, 93</em>(12), 1879-1900. doi:10.1175/bams-d-11-00019.1</p>
Violation of Bell's Inequality under Strict Einstein Locality Conditions
<p><strong>Data reuse</strong></p> <p>Please cite G. Weihs et al., Phys. Rev. Lett. <strong>81,</strong> 5039 (1998) in publications that reuse this data and if possible inform the lead author, who is always keen to learn what else one can do with the data.</p> <p><strong>Data description</strong></p> <p>The data contained in the two archives <a href="Alice.zip">Alice.zip</a> and <a href="Bob.zip">Bob.zip</a> and were collected in the years 1997 through 1999 as a long-distance test of Bell's inequality and quantum key distribution. Publications that are based on this data include G. Weihs et al., Phys. Rev. Lett. <strong>81,</strong> 5039 (1998) and T. Jennewein et al. Phys. Rev. Lett. <strong>85,</strong> 4729 (2000). The former is cited in the 2022 Nobel Prize announcement.</p> <p><strong>Data format</strong></p> <p>All files come in triplets for each observer (Alice, blue, west end of Innsbruck campus; Bob, red, east end of campus). Everything has been packed into an archive for each observer (alice.zip, bob.zip).</p> <table> <tbody> <tr> <td> <p>*_H.txt</p> </td> <td> <p>Cryptic laboratory comments containing information about some unimportant(?) experimental parameters.</p> </td> </tr> <tr> <td> <p>*_V.dat</p> </td> <td> <p>Photon arrival times where the "arm" time has already been subtracted. IEEE-8bit double precision numbers in "Big Endian"-form naturally sorted ascendingly. Actual time resolution is 10<sup>-10</sup>seconds, but accuracy is only 0.5 ns.</p> </td> </tr> <tr> <td> <p>*_C.dat</p> </td> <td> <p>Apparatus setting and "outcome" for each photon detection encoded in the following way: 16-bit integers in "Big Endian"-form, each number showing the detector (0=vertical, 1=horizontal with repect to the polarizer) that fired in its LSB and the position of the switch (0=no rotation, 1=45° rotation) in the next to LSB. (Not very efficient, but quicker when sampling directly to the PCs RAM during data acquisition!)</p> <p>Somebody said that the bits must be reversed. I don't think so but there is always a chance that I made double, compensating mistakes in my LabView and C programming.</p> </td> </tr> </tbody> </table> <p><strong>Files and folders</strong></p> <table> <tbody> <tr> <td><strong>Folder Name</strong></td> <td><strong>Description</strong></td> <td><strong>Mode</strong></td> <td><strong>Start Date</strong></td> </tr> <tr> <td>bellstat</td> <td>Static BI test (no fast switching)</td> <td>local, static</td> <td>30-Nov-1997</td> </tr> <tr> <td>bluesine</td> <td>Static correlation scan varying "blue" modulator bias</td> <td>local</td> <td>30-Nov-1997</td> </tr> <tr> <td>first</td> <td> </td> <td>local</td> <td>28-Nov-1997</td> </tr> <tr> <td>firstlong</td> <td>First run of long-distance (360m separation) measurements</td> <td>remote</td> <td>28-Apr-1998</td> </tr> <tr> <td>korrel</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>localswitch</td> <td> </td> <td>local, switched (45°)</td> <td>11-Jan-1998</td> </tr> <tr> <td>locbell</td> <td>BI Test</td> <td>local, swiched (45°)</td> <td>26-Mar-1998</td> </tr> <tr> <td>loccorr</td> <td>Measurement of correlations in parallel bases</td> <td>local , switched (45°)</td> <td>30-Mar-1998</td> </tr> <tr> <td>longdist</td> <td>Main run of long-distance BI tests</td> <td>remote, switched (45°)</td> <td>15-Apr-1998</td> </tr> <tr> <td>longtime</td> <td>Maximum continous measurement time (about 5 minutes!) BI test</td> <td>remote, switched (45°)</td> <td>06-Jun-1998</td> </tr> <tr> <td>newlongtime</td> <td> </td> <td>remote, switched (45°)</td> <td>22-Dec-1998</td> </tr> <tr> <td>scanblue</td> <td>Scan varying "blue" side modulator bias</td> <td>remote, switched (45°)</td> <td>01-May-1998</td> </tr> <tr> <td>scanred</td> <td>Scan varying "red" side modulator bias</td> <td>remote, switched (45°)</td> <td>31-May-1998</td> </tr> <tr> <td>sineblue</td> <td>Scan varying "blue" side modulator bias</td> <td>local, switched (45°)</td> <td>01-Apr-1998</td> </tr> <tr> <td>sinered</td> <td>Scan varying "red" side modulator bias</td> <td>local, switched (45°)</td> <td>30-Mar-1998</td> </tr> <tr> <td>switchtest</td> <td> </td> <td>local, switched (45°)</td> <td>03-Jan-1998</td> </tr> <tr> <td>wignerscanalice</td> <td>Scan varying "blue"side modulator bias, basis choice for Wigner's inequality</td> <td>remote, switched (30°)</td> <td>23-Dec-1998</td> </tr> </tbody> </table>
Replication material for 'The impact of local identities on voting behaviour: A Scouse case study'
<p>This holds the replication material for the paper 'The impact of local identities on voting behaviour: A Scouse case study'</p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Dataset for Local Communication in Small-Scale PV Systems: Study on Inverter - Smart Meter PLC Communication
<p>This study investigates communication technologies and protocols for small-scale photovoltaic (PV) systems, focusing on the interaction between inverters and smart meters. The research evaluates the performance of Power Line Communication (PLC) technologies, comparing both narrowband (NB-PLC) and broadband (BB-PLC) options. The analysis identifies MODBUS protocol limitations and highlights the benefits of advanced protocols like DLMS/COSEM and DNP3 for enhanced efficiency and reliability. Field tests demonstrate the viability of PLC for residential PV systems, with narrowband PLC showing better performance over longer distances. Future work aims to optimize PLC communication, digitize ripple control signals, and develop a Multi-Radio and Cable Access Technology (Multi-RCAT) module. This module will integrate various communication technologies, enabling flexible and redundant local communication behind utility sub-meters. These advancements will support real-time production and consumption control, contributing to the efficient and sustainable operation of decentralized energy systems.</p>
Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungary, Switzerland
<p><strong>Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungay, Switzerland</strong></p> <p>In the UNTWIST project, we have carried out a total of 18 focus groups in Denmark, Germany, Hungary, Spain, Switzerland and the United Kingdom. They explore RWPP voters’ subjective perceptions of their needs and demands, their horizon of expectations, and their level of ‘gender fatigue’. Groups’ design followed two minimum criteria: same-sex composition (with a minimum of two same sex -male and female- groups per country) and voting behaviour (current voters of RWPP who have previously voted for mainstream parties or abstained or have doubts about RWPP and mainstream or abstain in case of voting for the first time).</p> <p> The composition of the groups varied between 6 and 10 participants per group in all but one partner’s country. In Denmark, all focus groups experienced dropouts. These unforeseen issues led to conducting the focus groups with fewer participants than was initially designed.</p> <p> In 83% of countries, the empirical composition of focus groups was considered and controlled for participants’ age, social class position and level of education.</p> <p>Finally, groups were same-sex moderated.</p> <p>Two comprised folders are provided. One contains the anonymised transcriptions of 18 Focus Groups carried out for WP2 of the UNTWIST project in their local languages. The other contains the IA-translated (Deepl) version of the same focus groups. Please note that the translations have not been human-supervised. </p> <p>FG_CHE_1 Female <br>Female Group, Switzerland</p> <p>FG_CHE_2 Male<br>Male Group, Switzerland </p> <p>FG_DEN_1 Female <br>Female Groups, Denmakr</p> <p>FG_DEN_2 Male<br>Male Group, Denmark </p> <p>FG_DEN_3 Male <br>Male Group, Denmark</p> <p>FG_DEN_4 Mixed <br>Mix Male and Female Group, Switzerland</p> <p>FG_ESP_1 Male<br>Male Group, Spain</p> <p>FG_ESP_2 Male<br>Male Group, Spain </p> <p>FG_ESP_3 Female <br>Female Group, Spain</p> <p>FG_ESP_4 Female<br>Female Group, Spain</p> <p>FG_GBR_1 Female <br>Female Group, UK</p> <p>FG_GBR_2 Male <br>Male Group, UK</p> <p>FG_GER_1 Female <br>Female Group, Germany</p> <p>FG_GER_2 Male<br>Male Group, Germany</p> <p>FG_HUN_1 Female <br>Female Group, Hungary</p> <p>FG_HUN_2 Female <br>Female Group, Hungary</p> <p>FG_HUN_3 Male<br>Male Group, Hungary </p> <p>FG_HUN_4 Male<br>Male Group, Hungary </p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Raw data (RF) provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Article</strong> : <a href="https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext">https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext</a></p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>Beamformed dataset</strong> : <a href="../record/6811910#.ZA9dV3bMLid">https://zenodo.org/record/6811910#.ZA9dV3bMLid</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Supplementary Material to "Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)"
<p><span>Here we briefly describe the supplementary materials for the publication “Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)” in the European Journal of Mineralogy, 35(5), 715-735 Schorn, S., Rogowitz, A., & Hauzenberger, C. A. (2023).</span></p>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
Método de análisis de la visibilidad de los investigadores de la Universidad de Antioquia en medios de comunicación internacionales, nacionales y regionales-locales
<p>Principalmente, el proceso se dio a partir de cuatro momentos: el primero, la búsqueda de información en Google News; el segundo, la organización de la información en una matriz general, de acuerdo a campos y categorías específicas; el tercero, la normalización y limpieza de los datos, y el cuarto, el análisis y construcción de resultados. Cada momento se representó gráficamente a partir del modelado BPM (Business Process Management) con el fin de poder generar un flujo de trabajo replicable.</p>
Indoor Localization Dataset
<p>The dataset contains information concerning the older people’s movement inside their homes regarding their indoor location in the home setting.</p> <p>The dataset is recorded daily with the use of smart beacon devices installed in each older person's home and monitored through the system.</p> <p>Each record of the dataset has the following fields:</p> <p>- <strong>part_id</strong>: The user ID, which should be a 4-digit number</p> <p>- <strong>ts_date</strong>: The recording date, which follows the “YYYYMMDD” format, e.g. 14 September 2017, is formatted as 20170914</p> <p>- <strong>ts_time</strong>: The recording time, which follows the “hh:mm:ss” format</p> <p>- <strong>room</strong>: The room which the person entered on the specific date and time (It is assumed that the person remained in the room till the next recording of the same day)</p>
A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning</strong>".</p> <p>Preprint:<a href="https://arxiv.org/abs/1908.06851"> https://arxiv.org/abs/1908.06851</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8911792">https://ieeexplore.ieee.org/document/8911792</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> <p> </p>
Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
<p>This dataset supplements the article “<a href="https://doi.org/10.1162/COLI_a_00354">Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</a>” published in the Computational Linguistics journal:</p> <ul> <li> <p><code>watset-coli-lcc-performance.tsv</code>: runtime analysis</p> </li> <li> <p><code>watset-coli-synsets.zip</code>: synset induction experiment (note that <code>pairwise-{en-babelnet,ru-rwn}.pkl</code> files are excluded due to the licensing issues)</p> </li> <li> <p><code>watset-coli-triframes.zip</code>: semantic frame induction experiment</p> </li> <li> <p><code>watset-coli-classes.zip</code>: semantic class induction experiment</p> </li> </ul>
Evaluating data-flow coverage in spectrum-based fault localization
<p>This release contains files with the results of the experiment comparing the use of data- and control-flow spectra for Spectrum-based Fault Localization. It also has instructions to run Jaguar to perform experiments. The subject programs used in the experiment are public available in our GitHub repository.</p>
HMDB4.0 MetFrag Local CSV
<p>This is a local CSV file of HMDB4.0 (http://www.hmdb.ca/) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>Data was extracted from the XML, metals and entries with no monoisotopic mass were removed, one naming error for http://www.hmdb.ca/metabolites/HMDB0037436 was fixed and the XML fields adjusted to headers for MetFrag import. </p> <p>This file is for users wanting to integrate the latest HMDB 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>The two versions are identical, the two names fit various formatting conventions used behind the scenes in MetFragWeb.</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.