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16,497 results for “Phasing”
Phase coding of spatial representations in the human entorhinal cortex
<p>Supporting Preprocessed Electrophysiology Data for the article titled "Phase coding of spatial representations in the human entorhinal cortex".</p> <p>Data were preprocessed and analyzed using Matlab.</p> <p>Data, after decompression, are organized hierarchically in folders and subfolders (two levels).</p> <p>Main folder names are composed as subjectID_date_EC_DATA_taskNo</p> <p>Sub-folders named as: CHn_single-unit ID </p> <p>[subjectID, date, task num] + [Electrode channel, and single-unit ID] </p> <p>subjectID: (subject1, subject2)</p> <p>date: mmm-dd</p> <p>task: (1,2,3,4) Virtual environments (1: backyard, 2: Louvre, 3: Luxor, 4: desert)</p> <p>electrode Channel: CH1,CH2, CH3, CH4, CH5</p> <p>single-unit ID: 0, 1, 2, 3, 4, 5, 6</p> <p>For each channel and single-unit, the following 9 datasets were computed and saved. For instance, for the first electrode and first single-unit class (CH=1, cell ID= 0):</p> <p>CH1_Clu0.mat -- Summary of firing features of this single-unit including firing rate, and grid score of the cell. (In Matlab mat format)<br> CH1_Clu0_MeanPhaseMap.csv -- Mean spike phase map relative to gamma-band LFP<br> CH1_Clu0_Phase.csv -- Spike phase CH1_Clu0_spikeData.csv<br> CH1_Clu0_spkT.csv -- Spike times in increental order<br> CH1_Clu0_VarPhaseMap.csv -- Map of the variance of spike times in increental order<br> CH1_Clu0_xval.mat -- Summary of firing features of 50% of single-unit spikes (every other spike) for cross-validation purposes. (In Matlab mat format)<br> CH1_Clu0_xyPos.csv -- X,Y coordinates of the avatar's position in 1 ms resolution. In other words, the path taken by the avatar sampled at 1 kHz.<br> CH1_Clu0_XYspkT.csv -- X,Y coordinates of the avatar at moments of spikes. In other words, the location in space where a spike was fired by the putative neuron.</p> <p> </p>
Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
First-order electroweak phase transitions: a nonperturbative update, dataset
<p>This deposit contains data from Monte-Carlo and Langevin simulations of the SU(2) Higgs model, related to the paper "First-order electroweak phase transitions: a nonperturbative update". Everything is contained within the archive file <em>su2higgs_data.tar.gz</em>, a tarball compressed with Gzip.</p> <p>The data covers four equilibrium quantities: the critical mass, the jump in the quadratic Higgs condensate, the jump in the quartic Higgs condensate, and the surface tension. It also includes data for the bubble nucleation rate. Further details on the contents of the dataset are explained in README.md.</p>
First-order electroweak phase transitions: a nonperturbative update, videos
<p>This deposit contains videos created from Monte-Carlo and Langevin simulations of the SU(2) Higgs model, related to the paper "First-order electroweak phase transitions: a nonperturbative update". There are two videos, both in MPEG-4 format. Both files show data at the parameter point <span class="math-tex">\(x=0.0152473\)</span>, <span class="math-tex">\(y=0.0303620\)</span>, <span class="math-tex">\(ag_3^2= 4/7.332605\)</span>, <span class="math-tex">\(L=60a\)</span>, <span class="math-tex">\(\eta=10\)</span>, using the notation of the paper.</p> <p>The file <em>smoothing.mp4</em> shows the process of smoothing, or coarse-graining, the Higgs field, according to the method described in the section "Visualising bubble nucleation" of the paper. The particular configuration chosen is from the separatrix between phases, i.e. it is a critical bubble.</p> <p>The file <em>trajectory.mp4</em> shows a trajectory of the Langevin time evolution of a configuration starting in the symmetric phase and nucleating into the broken phase. The configurations in this trajectory have been smoothed, or coarse-grained, 10 times.</p>
Dataset of the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals"
<p>This dataset provides the raw data associated with the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals".It contains:</p> <ul> <li>A readme file meant to help the user navigate the database</li> <li>The raw data associated with all the plots and charts found in the Main Text and in the Supplementary information.</li> <li>The raw data collected during the 3D electron diffraction experiments on Pb<sub>3</sub>S<sub>2</sub>Cl<sub>2</sub> Nanocrystals. </li> <li>The CIF files of all the crystal structures refined in the work</li> <li>An atomistic model of the Pb<sub>4</sub>S<sub>3</sub>Cl<sub>2</sub>/CsPbCl<sub>3</sub> interface, which can be visualized with the freeware software Vesta. </li> </ul>
Dataset for simulations of a beamline that controls longitudinal phase space whilst transporting LWFA electrons to an undulator
<p>This dataset relates to a design for a particle accelerator beamline. The beamline transports particles (electrons) from a laser wakefield accelerator (LWFA) source to an undulator. The unique design allows the 'chirp' or 'longitudinal phase space' of the electron distribution to be sheared during transport.<br> The dataset contains: 1) Initial bunch distributions created by the ASTRA generator program and conversion to MAD8 program format; 2) a working MAD8 batch file; 3) Three set-ups of the beamline to provide positive, negative or no shear; 4) Tracking simulation input files to track the initial distributions through each beamline set-up; 5) Output electron distributions that result from each tracking simulation.</p>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Example Data)
<p><strong>Overview </strong></p> <p>This repository contains 2 example 4D-STEM datasets format from the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The data was written to hdf5 for compatibility with the python programming language. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly.</p> <p> </p> <p><strong>Parameters</strong></p> <p>The twisted bilayer graphene dataset is simulated. The smaller file is an experimental SrTiO<sub>3</sub> dataset.</p> <table> <thead> <tr> <th scope="row"> </th> <th scope="col">Graphene</th> <th scope="col">STO</th> </tr> </thead> <tbody> <tr> <th scope="row">E0</th> <td>200kV</td> <td>300kV</td> </tr> <tr> <th scope="row">Apeture</th> <td>25 mrad</td> <td>20 mrad</td> </tr> <tr> <th scope="row">Detector Size</th> <td>2.5 Å<sup>-1</sup></td> <td>1.6671 Å<sup>-1</sup></td> </tr> <tr> <th scope="row">Dimensions</th> <td>101x101x128x128</td> <td>60x60x64x64</td> </tr> <tr> <th scope="row">Step Size</th> <td>0.2</td> <td>0.1818</td> </tr> </tbody> </table> <p><br> </p>
MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"
<p>These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below.</p> <p>The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps.</p> <p>The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where:</p> <p><malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting).</p> <p><age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr).</p> <p><prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population).</p> <p>The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies.</p> <p>The files can be read into python using:</p> <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> <p>which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters <span class="math-tex">\(F, \gamma, \overline{\log(\rho)}\)</span>, and <span class="math-tex">\(\sigma_{\log(\rho)}\)</span>, respectively.</p>
LimeSurvey-Befragung: Authentizitätszuschreibungen in der VR-Anwendung Blackbox (Phase I von II, ID824987)
<p>Bei den hier vorliegenden Datensätzen handelt es sich um den Appendix meiner Dissertation "Virtual Reality in Gedenkstätten – Eine empirische Untersuchung zu Authentizitätskonstruktionen". Diese wurde am 30.04.2024 eingereicht, eine Veröffentlichung steht noch aus.</p> <p>Der Datensatz enthält alle derzeit möglichen Exportformate der LimeSurvey-Befragung: Authentizitätszuschreibungen in der VR-Anwendung Blackbox (Phase I von II, ID824987). (Limesurvey: Community Edition, Version 5.6.13+230327). Gesichert wurden sowohl die Umfragestruktur, als auch die Statistiken (statistic-survey824987.xls, statistic-survey824987.pdf).</p> <p> </p> <p>Die folgenden Beschreibungen wurden wörtlich von https://umfrage.uni-wuppertal.de/ übernommen:</p> <ul> <li><strong>Umfrage-Struktur (*.lss)</strong> <ul> <li>Bei diesem Export werden alle Gruppen, Fragen, Antworten und Bedingungen für Ihre Umfrage in einer .lss-Datei (die im Grunde eine XML-Datei ist) abgelegt. Diese Umfragestruktur kann beim Erstellen einer neuen Umfrage mit der Funktion 'Umfrage importieren' verwendet werden. Eine Umfrage, die ein benutzerdefinierte Designvorlage verwendet, wird zwar importiert, aber die Vorlage, auf die sie verweist,könnte dem neuen Server nicht vorhanden sein. In diesem Fall verwendet das System die globale Standarddesignvorlage.</li> </ul> </li> <li><strong>Umfragearchiv (.lsa) </strong> <ul> <li>Dieser Export soll eine vollständige Sicherung einer aktiven Umfrage zu Archivierungszwecken erstellen. Es enthält die folgenden Daten in einer ZIP-Datei, die mit '.lsa' endet. <ul> <li>Umfragestruktur Antwortdaten (Achtung: Enthält keine in eine Datei-Upload-Frage hochgeladenen Dateien. Diese müssen separat exportiert werden.)</li> <li>Umfrageteilnehmerdaten (wenn verfügbar)</li> <li>Timings (falls aktiviert)</li> </ul> </li> </ul> </li> <li><strong>queXML Format (*.xml)</strong> <ul> <li>queXML ist eine XML-Beschreibung eines Fragebogens. Es ist nicht für die Sicherung einer LimeSurvey-Umfrage geeignet, da es keine Bedingungen exportieren kann und es ist nicht möglich, alle Fragetypen zu exportieren. Durch das Exportieren eines Fragebogens nach queXML können Sie PDF-Dokumente erstellen, die gedruckt, ausgefüllt, gescannt und mit der queXF-Software verarbeitet werden können. Um mehr über queXML zu erfahren, sehen Sie sich diese Seite an: quexml.acspri.org.au.</li> </ul> </li> <li><strong>queXML PDF-Export</strong> <ul> <li>queXML ist eine XML-Beschreibung eines Fragebogens. Auf der folgenden Seite können Sie eine PDF-Datei erstellen, die ausgedruckt und erneut gescannt werden kann. Es ist nicht für die Sicherung einer LimeSurvey-Umfrage geeignet, da es keine Bedingungen exportieren kann und es ist nicht möglich, alle Fragetypen zu exportieren. Um mehr über queXML zu erfahren, klicken Sie auf diese Seite: quexml.acspri.org.au .</li> </ul> </li> <li><strong>Tab-getrennte-Werte Format (*.txt)</strong> <ul> <li>Diese Funktion erleichtert die Verwendung von Excel zum Verfassen und Bearbeiten von Umfragen. Es beseitigt vollständig die Abhängigkeit von SGQA-Codes. Darüber hinaus erleichtert es die Bulk-Bearbeitung Ihrer Umfrage, z. B. Suchen/Ersetzen, Bulk-Neuordnung, Schleifen (sich wiederholende Gruppen) und Testen (z. B. vorübergehende Deaktivierung von Pflicht- oder Validierungskriterien).</li> </ul> </li> <li><strong>Druckbare Umfrageversion (* .html)</strong> <ul> <li>Dadurch wird eine .zip-Datei mit der Umfrage in allen Sprachen heruntergeladen. Es enthält auch die notwendigen Stylesheets, um es auf jedem HTML-fähigen Gerät oder Browser zu anzeigen zu können. Dies wird keine Logik oder EM-Funktionalität enthalten, das müssen Sie selbst berücksichtigen.</li> </ul> </li> </ul>
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters
<p>Dataset for the article titled "Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters".</p> <p>This research was conducted at Antwerp Engineering, Photoelectrochemistry and Sensing (A-PECS) group, University of Antwerp, Belgium.</p>
Measurement of interturn short-circuits emulation on dual three-phase PMS motor
<p>The published data contains measurement records made on a special permanent magnet synchronous motor (PMSM). The motor has a stator with winding taps. The winding taps can be used to emulate various severities of interturn short-circuits.</p> <p>A detailed machine description and modeling can be found in the paper "Interturn short circuit modelling in dual three-phase PMSM" (https://dx.doi.org/10.1109/IECON49645.2022.9968364). </p> <p>The file name specifies the measurement conditions according to</p> <p>spd10-5000rpm_flt<strong>N</strong>z<strong>P</strong>_<strong>XX</strong>NM.mat</p> <p><strong>N</strong> - Number of shorted turns (0-healthy operation)</p> <p><strong>P</strong> - Fault phase ( U or V)</p> <p><strong>XX </strong>- Load torque generated by a dynamometer</p> <p> </p> <p>For example, the file spd10-5000rpm_flt1zu_25NM.mat contains data where one coil turn in phase U was shorted, and the machine was producing 25 Nm torque.</p> <p>Measurement was performed by the microcontroller and synchronised with the control algorithm. The sample rate is 10 kHz for this reason. Each data file contains measured currents in stator coordinates as well as currents and voltages (control value) in rotor (dq) coordinates. These variables are measured for each three-phase sub-system. Measured motor position, speed and required motor speed are also included. </p>
BBS phase 1 & phase 2 high quality E. coli bin assembled genomes
<p>1,402 <em>Escherichia coli</em> bin assembled genomes derived from the metagenome data collected as part of the <a href="https://www.ucl.ac.uk/global-health/research/a-z/baby-biome-study">BabyBiome study (BBS)</a> phase 1 & phase 2.</p> <p>The data in this upload was first published as part of "<em>Group 2 and 3 ABC-transporter dependant K-antigen loci contribute significantly to variation in the invasive potential of Escherichia coli"</em> (Gladstone et al. 2024, to be released).</p> <h2>Files</h2> <p>Assembly data:</p> <ul> <li>BBS_E_coli_BAGs.tar: Archive containing sequences of the 1,402 bin assembled genomes.</li> <li>BBS_E_coli_metadata.tsv: Table linking the sequence assemblies to the subject data.</li> </ul> <p>Capsule predictions:</p> <ul> <li>BBS_E_coli_Kaptive_output.csv: Capsule predictions for all sequence data.</li> <li>BBS_E_coli_deduplicated_sequences_IDs.txt: Filenames for assemblies that constitute the 873 deduplicated sequences analysed in Gladstone et al. 2024.</li> </ul> <p>Quality control data:</p> <ul> <li>BBS_E_coli_demix_check_scores.tsv: Output from demix_check for the sequence assemblies.</li> <li>BBS_E_coli_checkm_results.tsv: Output from checkm.</li> <li>BBS_E_coli_gunc_results.tsv: Output from gunc.</li> </ul> <h2>Methods</h2> <h3>Bin assembled genomes</h3> <p>Source data:</p> <ul> <li>BBS phase 1: <a href="https://doi.org/10.1038/s41586-019-1560-1">Shao et al. 2019</a></li> <li>BBS phase 2: <a href="https://doi.org/10.1038/s41564-024-01804-9">Shao et al. 2024</a></li> </ul> <p>The data was produced using the mSWEEP and mGEMS pipeline (<a href="https://doi.org/10.12688/wellcomeopenres.15639.2">Mäklin et al. 2020</a> & <a href="https://doi.org/10.1099/mgen.0.000691">Mäklin et al. 2021</a>) following the steps described in <a href="https://doi.org/10.1038/s41467-024-49591-5">Khawaja, Mäklin, Kallonen, et al. 2024</a>.</p> <h3>Quality control</h3> <p>The BAGs in this upload were filtered with demix_check (<a href="https://github.com/harry-thorpe/demix_check">https://github.com/harry-thorpe/demix_check</a>) and only those with a quality score 1 or 2 are included. For the capsule type annotations, contigs shorter than 5,000bp were removed but the short contigs are still present in the uploaded files). Further QC data is available from checkm (<a href="https://genome.cshlp.org/content/25/7/1043.short">Parks et al. 2015</a>) and gunc (<a href="https://link.springer.com/article/10.1186/s13059-021-02393-0">Orakov et al. 2022</a>) results.</p> <h3>Multilocus sequence typing</h3> <p>Sequence type (ST) was determined using fastmlst (<a href="https://journals.sagepub.com/doi/10.1177/11779322211059238">Guerrero-Araya et al. 2021</a>) with the `ecoli#1` database.</p> <h3>PopPUNK clustering</h3> <p>Sequence clusters (SC) correspond to the database available from <a href="https://zenodo.org/records/12528310">https://zenodo.org/records/12528310</a> and were created using PopPUNK (<a href="https://genome.cshlp.org/content/29/2/304.short">Lees et al. 2019</a>). Construction is described in <a href="https://doi.org/10.1038/s41467-024-49591-5">Khawaja, Mäklin, Kallonen, et al. 2024</a>.</p> <h3>Capsule type annotations</h3> <p>The capsule type annotations were created using Kaptive (<a href="https://doi.org/10.1099/mgen.0.000800">Lam et al. 2022</a>) with an <em>E. coli</em> specific database available from <a href="https://github.com/rgladstone/EC-K-typing">https://github.com/rgladstone/EC-K-typing</a> and described in Gladstone et al. 2024.</p>
5D-NP-FABTECH_ALD - Open Dataset for: "ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic-inorganic structures"
<p>This is the open dataset for the paper: "L. Demelius, L. Zhang, A. M. Coclite and M. D. Losego, ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic–inorganic structures, <em>Mater. Adv.</em>, 2024, <strong>5</strong>, 8464–8474."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Masks for ISIMIP3 Agriculture (GGCMI phase 3) model runs
<p>This dataset consists of a netCDF file with a number of layers at half-degree global resolution. Each layer is a binary map representing whether each gridcell is included (1 if yes, 0 if no) in one or more input datasets used in the ISIMIP3 Agriculture (GGCMI phase 3) model runs. Individual-dataset masks:</p> <ul> <li>has_soil indicates inclusion in the <a href="https://data.isimip.org/10.48364/ISIMIP.942125">ISIMIP3 soil input dataset (Volkholz & Müller, 2020)</a> (ignoring "gravel," which has some missing cells).</li> <li>has_cropcals indicates inclusion in the <a href="https://zenodo.org/record/5062513">Jägermeyr et al. (publication in prep.)</a> crop calendar dataset (ignoring second-season rice, which is not grown in all gridcells).</li> <li>has_lu indicates inclusion in all 15 area maps in the historical land use area dataset <a href="https://protocol.isimip.org/protocol/ISIMIP3b/index.html#socioeconomic-forcing">prepared for ISIMIP3</a> (landuse-totals_histsoc_annual_1850_2014.nc).</li> <li>has_crops indicates inclusion in all 15 area maps in the historical 15-crop dataset <a href="https://protocol.isimip.org/protocol/ISIMIP3b/index.html#socioeconomic-forcing">prepared for ISIMIP3</a> (landuse-15crops_histsoc_annual_1850_2014.nc). Note that there are two gridcells that are missing from this </li> <li>has_fertilizer indicates inclusion in every fertilizer_application_histsoc*.nc file in the <a href="http://doi.org/10.5281/zenodo.4954582">fertilizer and manure dataset prepared by Heinke et al. (2021) for GGCMI3</a>.</li> <li>has_all is a composite mask indicating inclusion in all of the above.</li> </ul> <p>Also included are a figure showing the masks and the MATLAB script used to generate the data and figure.</p> <ul> </ul>
Animation to visualize the electron beam damage induced in calcium silicate hydrate phases
<p>This dataset visualizes the electron beam damage induced by a scanning electron microscope (SEM) in calcium silicate hydrates (C-S-H). The specimen used is 28 days hydrated alite (water/solid = 0.5). It was scanned using a thermofischer scientific Helios G4 UX microscope at 350 V/25 pA with a stage bias of 200 V.</p> <p>This animation was an afterthought. Therefore, the dataset provides multiple magnifications and resolutions and some of the images are not in focus. Nevertheless, It can be seen, that the C-S-H needle in the right half of the image significantly deformes within a timespan of 124 seconds of constant scanning of that region.</p> <p><strong>File content:</strong></p> <ul> <li>All images ending with "raw" are the raw images provided by the SEM software including all metadata.</li> <li>The file "C3S_CSH_e-beam-damage_aligned stack.tif" contains the aligned image set using the SIFT algorithm. It contains the correct scaling if opened with ImageJ.</li> <li>The file "C3S_CSH_e-beam-damage_animation.gif" provides the final animation including a overlayed scalebar.</li> </ul>
HYCOM mode 1 steric SSH M2 amplitude and phase
<p>This data set contains data from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a> (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). <strong>Please cite this article when using these data. </strong></p> <p>This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>The data set contains</p> <ol> <li>the M2 mode 1 complex amplitude (RE+i*IM) of steric SSH computed for a two-week time series; the phase is relative to GMT 01-Sep-2016 01:00:00; the mode-1 amplitude is computed from the mode-1 surface pressure value (see <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a>). </li> <li>Mode-1 eigenspeed</li> <li>Positive seafloor depth, and latitude and longitude coordinates</li> </ol> <p>To convert eigenspeed to wavelength, phase speed and group speed, see <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a></p> <p><a href="https://sites.google.com/site/maartenbuijsman/">https://sites.google.com/site/maartenbuijsman/</a></p>
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </p>
Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps
<p>This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022. </p> <p> </p> <p>We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records. </p> <p>#----------------------------------------------------------</p> <p> </p> <p>LGM in the file names referes to 21 ka BP</p> <p>PI refers to pre-industrial</p> <p>#----------------------------------------------------------</p> <p> </p> <p><strong>This dataset contains:</strong></p> <p><strong>NetCDF files ------------------------------------------------------------------------------------</strong></p> <p> </p> <ul> <li><strong>Monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Bias-corrected monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>ELA</strong> <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> <p><br> <strong>csv files ------------------------------------------------------------------------------------</strong></p> <p><strong>* dates refer to model dates, not real ones!!!</strong><br> tj_700_hpa_pr_lgm : Tagliamento glacier daily wind and precipitation at the 21 ka BP<br> tj_700_hpa_pr_pi : Tagliamento glacier daily wind and precipitation at the PI<br> db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP<br> db_700_hpa_pr_pi : Dora Baltea glacier daily wind and precipitation at the PI<br> r_700_hpa_pr_lgm : Rhine glacier daily wind and precipitation at 21 ka BP<br> r_700_hpa_pr_pi : Rhine glacier daily wind and precipitation at the PI<br> ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier daily wind and precipitation at 21 ka BP<br> ist_700_hpa_pr_pi : Inn-Salzach-Traun glacier daily wind and precipitation at the PI</p> <p> </p>
Dataset for the Physical Review A article: Waveguiding driven by the Pancharatnam-Berry phase
<p>The dataset for reproducing the figure files of the paper, "Waveguiding driven by the Pancharatnam-Berry phase," Chandroth P. Jisha, Stree Vithya Arumugam, Lorenzo Marrucci, Stefan Nolte, and Alessandro Alberucci in Phys. Rev. A <strong>107</strong>, 013523 </p>
ScienceDex guides
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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.