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1,609 results for “Polar”
HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor
<p><strong>ABSTRACT</strong></p> <p>The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.</p> <p><strong>BACKGROUND</strong></p> <p>Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.</p> <p><strong>EXPERIMENTS</strong></p> <p>In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.</p> <p>The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].</p> <p>Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.</p> <p><strong>DATA DESCRIPTION</strong></p> <p>The structure of the dataset is as follows. One folder, called <em>HRV_anonymized_data</em> contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name <em>treatment_X.csv</em>, where <em>X</em> is the number of the person, while files related to the healthy controls are named <em>control_Y.csv</em>, where <em>Y</em> is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is <em>raw_RR_{control,treatment}_N.png</em>, where <em>N</em> is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>RR-interval [ms]</strong></td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds. <br> The second folder, called <em>accelerometer_anonymized_data</em> contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: <em>treatment_X.csv </em>and <em>control_X.csv</em> represent the data coming from the persons from the treatment and control group, respectively, while <em>X </em>is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>X [mg]</strong></td> <td><strong>Y [mg]</strong></td> <td><strong>Z [mg]</strong></td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.</p> <p>We also attached a file with the PANSS test scores (<em>PANSS.csv</em>) for all patients participating in the measurement. The structure of this file is as follows:</p> <table> <tbody> <tr> <td><strong>no_of_person</strong></td> <td><strong>PANSS_P</strong></td> <td><strong>PANSS_N</strong></td> <td><strong>PANSS_G</strong></td> <td><strong>PANSS_total</strong></td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> <p><br> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.</p> <p><strong>USAGE NOTES</strong></p> <p>All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the <em>main.py</em> file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the <em>utils_advanced_plots.py</em> file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting <em>sensitivity_analysis = False</em>), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting <em>exclude_quetiapine = True</em> because this medicine can have a strong impact on HRV [Hattori et al].</p> <p>The accelerometer data processing may be performed using the <em>utils_accelerometer.py</em> file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the <em>utils_loading.py </em>file.</p> <p>The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the <em>utils_basic_plots.py</em> file.</p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Polarization Dataset
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Data from: The genetic legacy of extreme exploitation in a polar vertebrate
<p>Microsatellite data (39 loci) from Antarctic fur seals and Subantarctic fur seals, used in the paper: "The genetic legacy of extreme exploitation in a polar vertebrate"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Understanding the effects of human exploitation on the genetic composition of wild populations is important for predicting species persistence and adaptive potential. We therefore investigated the genetic legacy of large-scale commercial harvesting by reconstructing on a global scale the recent demographic history of the Antarctic fur seal (<em>Arctocephalus gazella</em>), a species that was hunted to the brink of extinction by 18<sup>th</sup> and 19<sup>th</sup> century sealers. Molecular genetic data from over 2,000 individuals, sampled from all eight major breeding colonies across the species᾿ circumpolar geographic distribution, show that at least four relict populations around Antarctica survived commercial hunting. Coalescent simulations suggest that all of these populations experienced severe bottlenecks down to effective population sizes of around 150–200. Nevertheless, comparably high levels of neutral genetic variability were retained as these declines are unlikely to have been strong enough to deplete allelic richness by more than around 15%. These findings suggest that even dramatic short-term declines need not necessarily result in major losses of diversity, and explain the apparent contradiction between the high genetic diversity of this species and its extreme exploitation history.</p> <p> </p> <p><strong>Funding</strong></p> <p>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in<br> the framework of a Sonderforschungsbereich (project numbers 316099922 and 396774617–TRR 212) and the<br> priority programme "Antarctic Research with Comparative Investigations in Arctic Ice Areas" SPP 1158 (project<br> number 424119118). It was also funded by Norwegian Antarctic Research Expeditions (NARE) programme.<br> This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research<br> Council, and is part of the Polar Science for Planet Earth Programme. The Department of Environmental Affairs<br> provided logistical support for research at Marion Island and the Department of Science and Technology of<br> South Africa provided funding through the National Research Foundation (NRF). We are grateful to Caroline<br> Bonin, Debbie Baird-Bower and Iain Staniland together with the seal biologists working within the Marion<br> Island Marine Mammal Programme for sample collection and logistics. We acknowledge support for the Article<br> Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld<br> University.</p>
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Spectral Induced Polarization (SIP) field data sets for comparison between time and frequency domain (Martin et al. 2020)
<p>Data and scripts for the scientific journal article:</p> <p>Martin, T., Günther, T., Flores Orozco, A. & Dahlin, T. (2020): <br> Evaluation of spectral induced polarization field measurements in time and frequency domain, <br> J. Appl. Geophys., 180, 104141, doi:10.1016/j.jappgeo.2020.104141.</p> <p>The paper deals with the comparison of instruments for measuring induced polarization (IP)<br> data, both in frequency domain (FD) as well in time-domain. Four instruments have been used<br> to measure two profiles (IP1: a=1m, IP5: a=5m) over a known blackshale near the town Schleiz<br> in Thuringia, Germany.</p> <p>Preface: Our vision is not only to publish scientific results, but also openly demonstrate<br> how the results have been produced. This includes the raw data, processed data and inversion<br> results, but also all scripts that have been used for it in a reproducible way.<br> The scripts use the pyBERT (Python Boundless Electrical Resistivity Tomography) based on the<br> pyGIMLi (Python Geophysical Inversion and Modelling Library) computational framework.<br> Here, we used the pyBERT version 2.3.1 based on pygimli 1.1.1, but it should be reachable by<br> similar versions as well. All is based on the classes FDIPdata and TDIPdata.<br> For installing, we refer to the webpages www.pygimli.org and gitlab.com/resistivity-net/bert</p> <p>Folders:<br> figures figures used for production process, generated by the scripts in the folder<br> scripts Python scripts for generating the pdf files<br> IP1 data for the shallow profile using an electrode spacing of a=1m<br> IP5 data for the deeper profile using an electrode spacing of a=5m</p> <p>Each of the two data folders contains data and inversion results in separated folder named<br> according to the instrument names: <br> * SIP256C (Radic Research) - FDIP instrument with remote units (intelligent electrodes)<br> * DAS-1 (Multi-Phase Technology) - FDIP and TDIP instrument using multi-core cables<br> * Terrameter LS2 (GuidelineGeo ABEM) - TDIP instrument using multi-core cables<br> * Syscal Pro Switch72 (Iris instruments) - TDIP instrument using multi-core cables</p> <p>Please see readme files in the individual folders for specific information.</p> <p>Notice: In additon to the published results, we also added more data from the field <br> measurements which completes our results, e.g. <br> - TDIP data with 8s aquisition time<br> - all four instruments for the long profile IP5</p> <p>Complete full-waveform data for the Terrameter LS2 are available on request.</p>
Microwave brightness temperature of lunar south polar region
<p>These are Brightness temperature data of lunar south polar region (≤-80°) obtained by CE-2 MRM</p>
L'Aquila 2009 seismic sequence: integrated dataset of automatic first motion polarities focal mechanisms and RMT with HypoDD high quality relative earthquake locations
<p>This dataset is related to the L'Aquila 2009 seismic sequence that happened in Central Apennines (Italy).</p> <p>It contains:</p> <ul> <li>2782 quality selected focal mechanisms produced with the standard software FPFIT based on automatically determined first motion polarities of automatically detected and analyzed foreshocks and aftershocks recorded from January 2009 to December 2009 (flag <strong>fty</strong> in the header is MP)</li> <li>475 (out of 627) quality selected focal mechanisms produced with the standard software FPFIT also based on automatically determined first motion polarities but for only 3204 M<sub>L</sub> >= 1.9 earthquakes and by using take-off angles calculated within a local 3d tomographic velocity model (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL047365">Di Stefano et al., 2011</a>) , published and released in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JB008352">Chiaraluce et al., 2011</a> (flag <strong>fty</strong> in the header is JG)</li> <li>165 (out of 181) Regional Moment Tensors determined for earthquakes M<sub>L</sub> >= 3.0 based on broadband waveform inversion of ground velocities and published by <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a> (flag <strong>fty</strong> in the header is HM)</li> <li>The hypocenters of the total 3422 earthquakes reported in the present focal solutions dataset have been taken from the very high quality double difference locations of the about 64000 aftershocks reported in <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> and published, as part of the full dataset, <a href="https://doi.org/10.5281/zenodo.4036248">on Zenodo</a>. </li> </ul> <p>The association between the focal solutions and the HypoDD hypocenters has been performed through the direct use of the HypoDD event identifier where possible (the whole MP dataset) and through spatial and temporal earthquakes coordinates matching in all the other case by using the capability of a MySQL database. </p> <p>Two files are uploaded, one in plain text with blank separator, the second in plain text with ";" separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>OT_Date:</strong> date of the origin time in the format YYYY-MM-DD</p> <p><strong>OT_Time:</strong> time of the origin time in the format HH:mm:ss.dcm</p> <p><strong>lat:</strong> hypocenter latitude expressed in degrees </p> <p><strong>lon:</strong> hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep:</strong> hypocenter depth expressed in km </p> <p><strong>ML:</strong> local magnitude (pure number) from <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> (see last column notes also)</p> <p> </p> <p><strong>id_dd:</strong> the hypoDD event identifier, allowing to directly connect to the <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> full dataset</p> <p><strong>IMPORTANT NOTE about st1 and st2 (below): </strong>the focal solutions are presented here based on the convention they where produced or published, so there are two different (but compatible) conventions for the fault plains orientation in the 3d space</p> <p><strong>st1:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 1 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 1 (FPFIT convention)</li> </ul> <p><strong>dip1: </strong>dip of plane 1</p> <p><strong>rk1: </strong>rake of plane 1</p> <p><strong>st2:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 2 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 2 (FPFIT convention)</li> </ul> <p><strong>dip2: </strong>dip of plane 2</p> <p><strong>rk2: </strong>rake of plane 2</p> <p><strong>fty:</strong> flag to distinguish the type of solution, CMT=HM or JG, FPFIT=MP</p> <p><strong>MW:</strong> only for HM, this columns reports also MW from <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a></p>
Ship logs from ARCTOS Barents Sea Polar Front 2021-05 cruise
<p>PolarFront 2021-05 ship logs. Original (ISO 8859-1 encoded) text files from the ship logger on Helmer Hanssen.</p>
Polarized, color-selective and semi-transparent organic photodiode of aligned merocyanine H-aggregates
<p>Data to report <a href="https://doi.org/10.1039/D4TC00678J">https://doi.org/10.1039/D4TC00678J</a>:</p> <p><span><span>Highly anisotropic thin films of H-type coupled dipolar merocyanines </span></span><span><span>with large dichroic ratios of over 50 </span></span><span><span>were </span></span><span><span>deposited by solution shearing. These layers were incorporated into simultaneously color- and polarization-selective organic photodiodes. Using a transparent non-fullerene acceptor, polarization-sensitive planar-heterojunction devices with an average visible transmittance of 93% were obtained.</span></span></p> <p> </p>
Online Knowledge Production in Polarized Political Memes: The Case of Critical Race Theory (Dataset)
<p>This is the supplementary dataset to the article entitled "Online Knowledge Production in Polarized Political Memes: The Case of Critical Race Theory." This study, completed by Alyvia Walters, Tawfiq Ammari, Kiran Garimella, and Shagun Jhaver, was accepted for publication in <em>New Media & Society </em>in 2024.</p> <p>Description of files:</p> <p>Memes Codebook.dox - Codebook used for qualitative coding of memes.</p> <p>Memes Project.qdpx - NVivo coding project, exported.</p> <p>all_posts.jsonl, clusters.zip, images.zip, & image_data.csv - the complete collection of data and images used for this project. Please see publication in <em>New Media & Society </em>for more information on the use and collection of these items.</p>
Dataset for Figure 2 in 'Tales on co-response-ability in times of environmental polarization'
<p>This dataset contains part of the asnwers to surveys collecting feedback from participants in four participatory workshops within the project SharedDialogues (shareddialogues.org), focussed on exploring the environmnetal crisis of the Mar Menor lagoon (Murcia, Spain). It has been used for generating Figure 2 in the paper entitled 'Tales on co-response-ability in times of environmental polarization' published in the journal Ecology&Society. Participants were asked to rate in a likert scale 1-5 to what extent they personally felt 'safe', 'listened by others', 'understood by others', 'confronted', and to what extent they felt the overall affective tonality of the workshop as 'respectful', 'collaborative', 'tense'. Answers were anonymous and number of participants participating and answering the survey varied from workshop to workshop. This evaluation method has been adapted from: <span><span>Brugnach, M. de Waard, S. Dubois, D. Farolfi, S. 2021. Relational quality and uncertainty in common pool water management: an exploratory lab experiment. Sci Rep 11, 15188. https://doi.org/10.1038/s41598-021-94517-6</span></span></p> <div> <div> <div> <div> <div> <div> <div> <p><span>This work has been founded by UKRI Research England under the Participatory Research funding stream; the Ministry of Science and Innovation through the Juan de la Cierva (MCIN/AEI/10.13039/501100011033) and Ramón y Cajal (RYC2021- 031626-I); the María de Maeztu program for accreditation of excellence 2023-2027 (CEX2021- 001201-M); the Basque Government through the BERC 2022-2025 program; and the European Commission through the Horizon 2020 research and innovation program, Marie Skłodowska-Curie Training and Innovation Network ‘NEWAVE – Next Water Governance’ under funding agreement No. 861509.</span></p> </div> </div> </div> </div> </div> </div> </div>
Extent of past PSRs in the north and south polar regions of the Moon
<p>Extent of perennially shadowed regions (PSRs) obtained by applying ray-tracing to LOLA shape models with various values of the maximum solar declination.</p> <p>See README.TXT for a technical description of the data files.</p> <p> </p>
Polar mesospheric clouds from the Balloon Lidar Experiment (BOLIDE) during the PMC Turbo balloon mission
<p>This dataset contains BOLIDE lidar data obtained during the PMC Turbo balloon mission that was launched on 7 July 2018 from Esrange, Sweden and landed in Nunavut, Canada on 14 July 2018. The mission was designed to study small-scale atmospheric dynamics induced by breaking atmospheric gravity waves within the polar mesospheric cloud layer at ~82 km altitude. PMC Turbo floated at around 40 km altitude and carried seven digital cameras to image the polar mesospheric cloud layer and the first Rayleigh lidar to successfully operate from a balloon.</p> <p>The lidar data consists of volume backscatter coefficients of polar mesospheric clouds, available at 20 m vertical and 10 s temporal resolution, contained in a compressed netcdf file. The magnitude of volume backscatter coefficients scales with<br> the brightness of clouds imaged by the PMC Turbo cameras. The netcdf file further includes floating altitude, rotator angle (azimuth) as well as latitude and longitude of the lidar beam at 82 km altitude.</p> <p>Users are encouraged to contact us for discussion when using BOLIDE data.</p> <p>Data contact: natalie.kaifler@dlr.de</p> <p>References:</p> <p>PMC Turbo camera videos: https://svs.gsfc.nasa.gov/13073</p> <p>NASA Space Physics Data Facility: https://cdaweb.gsfc.nasa.gov/index.html/, select PMC Turbo</p> <p>DLR Institute mission database: https://halo-db.pa.op.dlr.de/mission/112</p> <p>Kaifler, N., Kaifler, B., Rapp, M., Fritts, D.C. The polar mesospheric cloud dataset of the Balloon Lidar Experiment BOLIDE. Earth System Science Data. In preparation.</p> <p>Kaifler, B., Rempel, D., Roßi, P., Büdenbender, C., Kaifler, N., and Baturkin, V.: A technical<br> description of the Balloon Lidar Experiment (BOLIDE), Atmos. Meas. Tech., 13, 5681–5695,<br> https://doi.org/10.5194/amt-13-5681-2020, 2020.</p> <p>Fritts, D. C., Miller, A. D., Kjellstrand, C. B., Geach, C., Williams, B. P., Kaifler, B.,<br> et al. (2019). PMC Turbo: Studying gravity wave and instability dynamics in the summer mesosphere<br> using polar mesospheric cloud imaging and profiling from a stratospheric balloon. Journal of<br> Geophysical Research: Atmospheres, 124, 6423– 6443. https://doi.org/10.1029/2019JD030298</p>
Coupling charge and topological reconstructions at polar oxide interfaces
<p>Dataset corresponding to the publication 'Coupling charge and topological reconstructions at polar oxide interfaces' (<a href="https://arxiv.org/abs/2107.03359">arXiv:2107.03359</a>) (Phys. Rev. Lett. <strong>127</strong>, 127202) </p>
Resistance, utilization factor curve and polarization curve of test campaign
<p>The datesets refer to the article "Experimental Procedures & First Results of an Innovative Solid Oxide Fuel Cell Test Rig: Parametric Analysis and Stability Test" (https://zenodo.org/badge/DOI/10.5281/zenodo.5763507.svg).</p> <p>Solid Oxide Fuel Cells are a promising technology for Solid Oxide Fuel Cells (SOFC) are a promising technology For high-efficiency electrochemical conversion of a vast range of fuel gas mixtures, thigh operating temperature conditions (650–900 ◦C) represent a challenge both at system level and at laboratory testing level, in terms of material properties and performance dynamics. In this work a detailed procedural analysis is presented for an innovative all-ceramic compact SOFC test rig and first experimental testing results are reported in terms of polarization curves obtained under parametric variation of operating conditions (H2 content, air ratio λ and temperature) and short-term voltage stability test under load (140 h at 0.3 A/cm2 ). The electrochemical characterization results confirm the validity of the used all-ceramic cell holder, showing excellent cell performances in terms of polarization. H2 content has the most impact on SOFC performance, followed by temperature and finally air ratio, whose impact in the analyzed range is hardly seen. From the short-term stability test, the test bench setup reliability is demonstrated, showing no significant performance degradation after 140 continuous hours under load, which confirms the high quality and reproducibility of the results.</p>
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers
<p>These datasets are part of the submitted paper for the LREC2022 conference entitled: "The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers"</p> <p>The various data sources, as well as the methodology, are explained in detail in the research paper which will be available soon.</p> <p>ALPIN stands for Austrian Language Polarity in Newspapers. The dictionary consists of three different parts which were merged together:</p> <ul> <li>Austrian Media Corpus: AMC (AMC_v1.0.csv)</li> <li>STANDARD posts: STP (STP_v1.0.csv)</li> <li>Austriacisms: AUT (AUT_v1.0.csv)</li> </ul> <p>Austrian Media Corpus (AMC) (Ransmayr et al., 2017) & STANDARD posts (STP) (Schabus et al., 2017) rely on the SPLM algorithm as used in SentiDraw (Sharma & Dutta 2021). Austriacisms (AUT) was generated by using the Best-Worst scaling (BWS) (Kiritchenko and Mohammad, 2017b). The AUT list was collected from the “Variantenwörterbuch des Deutschen” (Ammon et al., 2016) (thereby only selecting those words that only surface in Austrian German and in no other variety of German) and an austriacism list of Wikipedia (https://de.wikipedia.org/wiki/Liste_von_Austriazismen).</p> <p>The scores are scaled to the interval [-1, 1] using the min-max-abs scaling, ranging from negative to positive.</p> <p>References:<br> Sharma, S. S., & Dutta, G. (2021). SentiDraw: Using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination. Information Processing & Management, 58(1), 102412.<br> Kiritchenko, S. and Mohammad, S. M. (2017b). Capturing reliable fine-grained sentiment associations by crowdsourcing and best-worst scaling.<br> Schabus, D., Skowron, M., & Trapp, M. (2017). One Million Posts: A Data Set of German Online Discussions. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1241–1244. https://doi.org/10.1145/3077136.3080711<br> Ransmayr, J., Mörth, K., & Ďurčo, M. (2017). AMC (Austrian Media Corpus). In Korpusbasierte Forschungen zum österreichischen Deutsch. In Digitale Methoden der Korpusforschung in Österreich (= Veröffentlichungen zur Linguistik und Kommunikationsforschung Nr. 30) (pp. 27–38). Verlag der Österreichischen Akademie der Wissenschaften.<br> Ammon, U., Bickel, H., & Ebner, J. (2016). Variantenwörterbuch des Deutschen : die Standardsprache in Österreich, der Schweiz, Deutschland, Liechtenstein, Luxemburg, Ostbelgien und Südtirol sowie Rumänien, Namibia und Mennonitensiedlungen. Walter de Gruyter.</p>
Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces'
<p>Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces' by Kuppusamy Senthil Kumar et al. The two versions of Fig. 4d datasets correspond to the preprint version (https://zenodo.org/record/4905692#.Ymj9odpBxaQ) and publication version (https://www.nature.com/articles/s41467-021-22383-x), since a new set of data was taken during the review process. </p>
Spectral induced polarization of non-consolidated heterogeneous clay mixtures
<p>We present a spectral induced polarization dataset on heterogeneous mixtures of illite and red montmorillonite, with two longitudinal, and one transversal arrangement. Additionally, there is a 50-50% in volume content homogeneous mixture of illite and red montmorillonite.</p> <p>Each file has its header, describing each column. The ReadMe file also explains the content and format of each dataset.</p>
Utility of polarizations available from Sentinel-1 for tundra mapping
<p>This presentation reviews achievements with C-band SAR in general and specifically Sentinel-1 for Arctic land monitoring, the implications of the current acquisition strategy including the availability of certain polarizations and discusses differences to other wavelengths through the use of Kennaugh parameterization. Unfrozen as well as frozen season observations provide added value. C-VV has the best availability and can be used for a wide range of land cover and terrain change studies. C-HH has been proven applicable for soil characterization but can only be applied regionally. A combination with other wavelengths (demonstrated for X-band) is promising for specifically mapping of wetlands, which are of interest with respect to permafrost applications.</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.