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34 results for “precession”

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zenodo44/100

Idealized precession GCM simulations with EC-Earth-2-2

<p>This repository contains output from the EC-Earth-2-2 extreme precession simulations (Prec-min / Pmin and Prec-max / Pmax). The experiment details are described in the papers given as references. In this repository, monthly output of the last 50 years of simulation is given for precipitation (in m/s), zonal wind (east-west, in m/s, parameter 165), meridional wind (north-south, in m/s, parameter 166), mixed layer depth (MLD, in m, based on a &sigma;0 difference of 0.01 with the surface), sea surface salinity (in PSU) and thermocline depth (TCD, in m).</p> <p>Furthermore, climatological mean monthly output (averaged over the last 50 years of simulation) is given for surface air temperature (parameter 167), surface sensible heat flux (146), surface latent heat flux (147), surface solar radiation downwards (169), surface thermal radiation downward (175), net surface solar radiation (176), net surface thermal radiation (177), net top solar radiation (at top of atmosphere, 178), and net top thermal radiation (at top of atmosphere, 179). Furthermore the following 3D variables are given on pressure levels: geopotential height (129), specific humidity q (133), zonal wind (east-west, 131), meridional wind (north-south, 132), vertical velocity (135). Please see the ECMWF GRIB parameter database for units and more details on these parameters: https://apps.ecmwf.int/codes/grib/param-db.</p> <p>More output from these simulations can be found through the repository:<br> Bosmans, Joyce. (2019). Idealized orbital extreme GCM simulations with EC-Earth-2-2 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3268528</p> <p>References:</p> <p>Bosmans, J. H. C. (2014). A model perspective on orbital forcing of monsoons and Mediterranean climate using EC-Earth. UU Depts. of Physical Geography and Earth Sciences.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., &amp; Lourens, L. J. (2015). Response of the North African summer monsoon to precession and obliquity forcings in the EC-Earth GCM. Climate dynamics, 44(1-2), 279-297.<br> Bosmans, J. H. C., Hilgen, F. J., Tuenter, E., &amp; Lourens, L. J. (2015). Obliquity forcing of low-latitude climate. Climate of the Past, 11(10), 1335-1346.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Lourens, L. J., &amp; Rohling, E. J. (2015). Precession and obliquity forcing of the freshwater budget over the Mediterranean. Quaternary Science Reviews, 123, 16-30.<br> Bosmans, J. H. C., Erb, M. P., Dolan, A. M., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Edge, D., Pope, J.O. &amp; Lourens, L. J. (2018). Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. Quaternary Science Reviews, 188, 121-135.<br> Chetankumar, J., Bosmans, J.H.C., Srinivasan, J. &amp; Chakraborty, A. (2019): The response of tropical precipitation to Earth&#39;s precesion: the role of energy fluxes and vertical stability. Climate of the Past, 15, 449-462.</p> <p>&nbsp;</p> <p>Contact:<br> Joyce.Bosmans@ru.nl</p> <p>Acknowledgements:<br> These model simulations were performed during Joyce Bosmans&#39; PhD research, funded by an Utrecht University `Focus en Massa&#39; grant and partly performed at the Royal Netherlands Meteorological Institute (KNMI), with support from ECMWF.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

Precessing binary-black-hole numerical relativity catalogue (minimal data release)

<p>This page contains the minimal data release associated with the catalogue presented in&nbsp;<a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations.&nbsp;</p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>

opencc-by-4.0Feb 2023View details →
zenodo40/100

Routine to reproduce the figures from "Experimental study of the flows in a non-axisymmetric ellipsoid under precession"" JFM, 2022

<p>The Zip file&nbsp;contains the python notebook and all necessary datasets to reproduce the figures from the publication:</p> <ol> <li>Burmann, F. and <strong>Noir, J</strong>., 2022. Experimental study of the flows in a non-axisymmetric ellipsoid under precession.&nbsp;<em>Journal of Fluid</em> <em>Mechanics</em>,&nbsp;<em>932</em>,<a href="https://doi.org/10.1017/jfm.2021.932">https://doi.org/10.1017/jfm.2021.932</a></li> </ol> <p>The data are saved in .npz format, the structure of the data is explained in the header of the python jupyter notebook.&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo40/100

Precessing binary-black-hole numerical relativity catalogue (complete data release)

<p>This page contains the minimal data release associated with the catalogue presented in&nbsp;<a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations.&nbsp;</p> <p>The content of the data release is described&nbsp;<a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>

opencc-by-4.0Feb 2023View details →
zenodo40/100

Correcting for probe wandering by precession path segmentation

<p>Scanning precession electron diffraction datasets and processing scripts used in the journal publication &quot;<strong>Correcting for probe wandering by precession path segmentation</strong>&quot;.</p> <p>DOI link to publication:&nbsp;<a href="http://doi.org/10.1016/j.ultramic.2023.113715">https://doi.org/10.1016/j.ultramic.2023.113715</a></p> <p>&nbsp;</p> <p><strong>Prerequisites</strong></p> <p>To run the scripts necessary to process the data, the open source packages JupyterLab and&nbsp;HyperSpy&nbsp;need to be installed. These notebooks were created with these package versions:</p> <ul> <li>hyperspy 1.6.4</li> <li>jupyterlab 3.2.0</li> </ul> <p>&nbsp;</p> <p><strong>Data files</strong></p> <p>There are three data types:</p> <ul> <li>Scanning precession electron diffraction (SPED) datasets contain the .hspy extension. There are two SPED datasets acquired by precession path segmentation, and one regular dataset: <ul> <li><em>SPED_256x256_22x22_10186nm_NBD_a5_spot1nm_CL20cm_125msExp_3000msFB_subframing_x8_pivotoff_01.hspy</em> is a precession path segmentation dataset presented in figure 2 in the article.</li> <li><em>SPED_zoom1_256x256_12x12_5556nm_NBD_a5_spot1nm_CL20cm_10msExp_3000msFB_subframing_x8.hspy</em>&nbsp;is a precession path segmentation dataset presented in figures 1 and 3 in the article.</li> <li><em>SPED_zoom1_256x256_12x12_5556nm_NBD_a5_spot1nm_CL20cm_10msExp_300msFB.hspy</em> is a regular SPED dataset of the same region as the dataset just above (also containing &quot;zoom1&quot; in its name).</li> </ul> </li> <li>A series of diffraction patterns with de-rocking switched off can be found in the&nbsp;<em>eight_segments.npy</em>&nbsp;file. This dataset was recorded by Dr. Tina Bergh at Department of Chemical Engineering, Norwegian University of Science and Technology, and is meant to be used as an illustration of the precession segments used in figure 1 in the article.</li> <li>Virtual bright field images of the precession path segmented scans, before and after rigid correction in SmartAlign, can be found with the .png extension. The files starting with &quot;pivot01...&quot; are images from the precession path segmentation scan with an intentional pivot point misalignment, while the files starting with &quot;small...&quot; are from the well aligned scan. The files ending with &quot;_reg&quot; are regular, i.e., non-corrected compound VBF images, while the files ending with &quot;_cor&quot; are SmartAlign rigidly corrected VBF images.</li> </ul> <p>&nbsp;</p> <p><strong>Processing scripts</strong></p> <p>There are two processing scripts:</p> <ul> <li><em>p01_slicing_segments.ipynb</em>&nbsp;is used to process the precession path segmentation datasets, from slicing of the raw data to constructing virtual bright field images.</li> <li><em>p02_image_processing.ipynb</em> is used to create the images found in the article and for image analysis. The latter includes blur quantification and measuring edge sharpness.</li> </ul> <p>&nbsp;</p>

opencc-by-4.0Nov 2022View details →
zenodo40/100

General relativistic precession and the long-term stability of the solar system: SimulationArchive dataset

<p>We share the data for 1280 long-term solar system simulations used in <a href="https://doi.org/10.1093/mnras/stad719">Brown &amp; Rein (2023)</a>. The simulations are zipped together consecutively in groups of 16 and saved in the <code>REBOUND</code>&nbsp;(3.18.1)&nbsp;SimulationArchive format. Please see the companion paper and <a href="https://doi.org/10.5281/zenodo.7753656">code</a> for details.</p> <p><strong>Abstract</strong></p> <p>The long-term evolution of the solar system is chaotic.&nbsp;In some cases, chaotic diffusion caused by an overlap of secular resonances can increase the eccentricity of planets when they enter into a linear secular resonance, driving the system to instability.&nbsp;Previous work has shown that including general relativistic contributions to the planets&#39; precession frequency is crucial when modelling the solar system.&nbsp;It reduces the probability that the solar system destabilizes within 5 Gyr by a factor of 60.&nbsp;We run 1280 additional <em>N</em>-body simulations of the solar system spanning 12.5 Gyr where we allow the general relativistic&nbsp;precession rate to vary with time.&nbsp;We develop a simple, unified, Fokker-Planck advection-diffusion model that can reproduce the instability time of Mercury with, without, and with time-varying general relativistic&nbsp;precession.&nbsp;We show that while ignoring general relativistic&nbsp;precession does move Mercury&#39;s precession frequency closer to a resonance with Jupiter, this alone does not explain the increased instability rate.&nbsp;It is necessary that there is also a significant increase in the rate of diffusion.&nbsp;We find that the system responds smoothly to a change in the precession frequency: There is no critical general relativistic&nbsp;precession frequency below which the solar system becomes significantly more unstable.&nbsp;Our results show that the long-term evolution of the solar system is well described with an advection-diffusion model.&nbsp;</p>

opencc-by-4.0Mar 2023View details →
zenodo40/100

Scanning Precession Electron Tomography (SPET) for Structural Analysis of Thin Films along Their Thickness

<p>&nbsp;_______________________________________________________________________________<br> | General information:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |_______________________________________________________________________________<br> | Article &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Scanning Precession Electron Tomography (SPET) for Structural Analysis&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp;of Thin Films along Their Thickness&nbsp; &nbsp;<br> |<br> | Authors &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Sara Passuti, Julien Varignon, Adrian David, Philippe Boullay&nbsp;<br> |<br> | Journal &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| Symmetry 2023, 15, 1459&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | DOI &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | 10.3390/sym15071459&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Funding &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| NanED (www.naned.eu)(ESR project 12)&nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Project Label &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; | PVO_STO&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Sample Label&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;| PVO_STO&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Dataset description&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| SPET (scanning precession electron tomography) acquisition on&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| the PVO thin film deposited on STO substrate, analyzed in&nbsp;section</p> <p>|&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| in the form of a TEM lamella. In the main folder the datasets</p> <p>|&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| corresponding to each one of the analyzed areas of the&nbsp;</p> <p>|&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| sample at&nbsp;different thicknesses (i.e. distances from the&nbsp;</p> <p>|&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| interface with the substrate) is found.<br> |______________________________________________________________________________<br> | Experimental&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |_______________________________________________________________________________<br> | Data Type&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; | Electron diffraction data - 3D ED&nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Data collection method &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| SPET (Scanning Precession Electron&nbsp;Tomography)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Number of experimental frames &nbsp; &nbsp; &nbsp; &nbsp; | 57&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | tilt range&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | -50.7&deg; to +43.5&deg;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Exposure time per frame&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | 500 ms&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Software used for the data collection | ASI Accos&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |______________________________________________________________________________<br> | Instrumental: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> |_______________________________________________________________________________|<br> | Instrument &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| Transmission electron microscope&nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; Jeol F200&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Radiation source &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| cold FEG&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Accelerating voltage&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;| 200 kV&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Wavelength &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| 0.0251 &Aring;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Probe Type &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| Microdiffraction&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Beam Diameter&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | 10 nm &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Beam Convergence&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; &nbsp;| Parallel beam, convergence &lt;0.1mrad&nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Detector&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; | Hybrid pixel detector ASI Cheetah M3&nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Number of pixels in the image &nbsp; &nbsp; &nbsp; &nbsp; | 512 x 512&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Pixel size&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | 55 &micro;m x 55 &micro;m&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Effective camera length&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | 200 mm&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Calibration constant&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| 0.00708 &Aring;-1/pixel&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |______________________________________________________________________________<br> | Sample description:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |_______________________________________________________________________________<br> | Name&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| PVO thin film on STO substrate&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | at thickness = 0.52 nm&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | film deposited by SPS and cut by FIB&nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Chemical composition &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| PrVO3, SrTiO3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Number of crystals contributing &nbsp; &nbsp;| 1&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | to the data set &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |______________________________________________________________________________<br> | Authorship and bibliography&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |_______________________________________________________________________________<br> | Author of the data &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| Sara Passuti (ESR 12)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | Related data&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |______________________________________________________________________________<br> | Files and data formats&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> |_______________________________________________________________________________<br> | Image format&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;tiff_16bit&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> |&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | Folders/files&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;| layer_#1_0.52_nm &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp;layer_#2_3.28_nm &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp;layer_#3_4.20_nm &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; layer_#4_5.12_nm &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; layer_#5_7.88_nm&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; layer_#6_9.72_nm &nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp;layer_#7_12.48_nm&nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp;layer_#8_17.08_nm&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; layer_#9_29.08_nm&nbsp; &nbsp; &nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp;substrate&nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; each of the folders contains the respective &quot;tiff&quot; folder containing&nbsp; &nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; the diffraction patterns in tiff format and the files for the analysis,<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; as well as the metadata file with the specific information of the&nbsp;&nbsp;&nbsp;<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |&nbsp; dataset&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> ______________________________________________________________________________<br> &nbsp;Notes:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br> _______________________________________________________________________________</p>

opencc-by-4.0Jul 2023View details →
zenodo36/100

Data: Precession-induced Tipping of the Atlantic Meridional Ocean Circulation

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2024View details →
zenodo36/100

Data Release for "Revisiting the evidence for precession in GW200129 with machine learning noise mitigation"

<p>Cleaned gravitational-wave data frame for the Livingston interferometer around GW200129 using NLSub, a machine-learning algorithm. For more details, see the publication on ArXiv: <a href="https://arxiv.org/abs/2311.09921">https://arxiv.org/abs/2311.09921</a>.</p><p>The data frame can be loaded in Python using e.g. GWPy TimeSeries class:</p><blockquote><p>from gwpy.timeseries import TimeSeries<br>tseries = TimeSeries.read('L-L1_DCS-CALIB_STRAIN_CLEAN_C01_NLSUB_P2300358_v4-1264314077-4078.hdf5')</p></blockquote><p>&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo36/100

Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis

<div>This dataset contains key analysis and plotting scripts, data, and sample images.</div> <div>&nbsp;</div> <div>Measurement of T1&rho; dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis</div> <div>&nbsp;</div> <div>Magnetic Resonance in Medicine Journal | DOI: 10.1002/mrm.30206</div> <div>&nbsp;</div> <div>&sect; Swetha Pala(1), &sect; Antti Paajanen(1), Aapo Ristaniemi(1), Ervin Nippolainen(1), Isaac O. Afara(1), Olli Nyk&auml;nen (1), Mikko J. Nissi (1*)</div> <div>&nbsp;</div> <div>1Department of Technical Physics, University of Eastern Finland</div> <div>&sect;Shared authorship&nbsp;</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>*Corresponding author</div> <div>Mikko J. Nissi</div> <div>Department of Technical Physics</div> <div>University of Eastern Finland, Kuopio Finland</div> <div>POB 1627</div> <div>70211 Kuopio</div> <div>mikko.nissi@uef.fi</div> <div>+358-50-5955517</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Keywords: Quantitative MRI, T1&rho; relaxation, T1&rho; dispersion, Compressed-sensing, radial acquisition.</div> <div>&nbsp;</div> <div>Included folders and files are:</div> <div>- Article_figures: all figures published in the manuscript (.eps format)</div> <div>- Data: MRI data files from 27 human cadaver samples with subfolders and files:</div> <div>- Human samples data: raw data files, along with generic analysis ROIs, zone divison inside specific samples folder, and within the parameter related data folder there are smaple specific analysis ROIs, computed profiles per spin lock amplitude.&nbsp;</div> <div>- CS reconstructed data files:&nbsp;</div> <div>- DataTables_used_for_analysis: Contains data tables per AF and reference data used for data analysis&nbsp;</div> <div>- Scripts: Matlab functions used for data processing and T1&rho; computation, aedes plugins, and data analysis with subfolders and files:</div> <div>&nbsp; &nbsp; - Aedes_plugins: Plugins for aedes (http://aedes.uef.fi) for calculation of profiles from ROI.&nbsp; &nbsp;</div> <div>&nbsp; &nbsp; - Data analysis: Key scripts used for analysis and plotting.</div> <div>&nbsp; &nbsp; - Common functions: Some common functions that are required by the scripts/plugins.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</div> <div>&nbsp;</div> <div>- README.txt: this file describing the contents of the dataset.</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>See more info in separate readme files included in sub-folders.</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>(Swetha Pala, 02 July 2024)</div> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
zenodo36/100

Scanning precession electron diffraction data of partly overlapping magnesium oxide nanoparticles

<p>Scanning precession electron diffraction (SPED) data of cubical magnesium oxide (MgO) nanoparticles are provided. The MgO particles in the data are partly overlapping and some share the same orientation. The dataset was used for demonstration of nanocrystal segmentation in SPED data, which is presented in the article entitled &quot;Nanocrystal segmentation in scanning precession electron diffraction data&quot; [1]. In this publication, two methods for nanocrystal segmentation are presented based on; i) virtual dark-field imaging and ii) non-negative matrix factorisation, both incorporating watershed image segmentation. The workflows and code used for the segmentation demonstrated in the article are available open-source [2].</p> <p>Here, two files are provided based on one raw SPED dataset:</p> <p>- &quot;SPED_MgO_1.hdf5&quot;: raw data cropped in navigation space to dimensions (219, 228|144, 144) and exported to hdf5, and</p> <p>- &quot;SPED_MgO.hdf5&quot;: the same data binned by 2 in navigation space to yield dimensions (109, 114|144, 144).</p> <p>Adrian Lervik is acknowledged for specimen preparation.</p> <p>[1] Bergh, T., Johnstone, D., Crout, P., H&oslash;g&aring;s, S., Midgley, P., Holmestad, R., Vullum, P. And Van Helvoort, A. (2019), Nanocrystal segmentation in scanning precession electron diffraction data. Journal of Microscopy. doi:<a href="https://doi.org/10.1111/jmi.12850">10.1111/Jmi.12850</a></p> <p>[2] Duncan N. Johnstone, Phillip Crout, Simon H&oslash;g&aring;s, Tina Bergh, Joonatan Laulainen, &amp; Stef Smeets. (2019). pyxem/pyxem-demos: pyxem-demos v0.10.0. Zenodo. http://doi.org/10.5281/zenodo.3533670</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2019View details →
zenodo36/100

Precession experiments based on AWIESM2-wiso

<p>This dataset is from a set of 24 experiments performed using an isotope-enabled climate model AWIESM2-wiso.&nbsp;</p> <p>&nbsp;</p> <p>climatology_daily_temp_precip.tar.gz:&nbsp; climatological daily surface temperature (var169), 2-m temperature (var167) and precipitation (var260).</p> <p>climatology_monthly_temp_precip.tar.gz:&nbsp; climatological monthly surface temperature (var169), 2-m temperature (var167) and precipitation (var260).</p> <p>insolation.tar.gz: &nbsp;climatological monthly incoming solar radiation.</p> <p>delta18O_in_precip.tar.gz: precipitation-weighted mean delta18O in precipitation.</p> <p>vegetation.tar.gz: vegetation related variables, for details please refer to jsbach_codes.txt.</p> <p>moisture_source.tar.gz: mass-weighted mean moisture source locations and properties. The following gives a description of each variable:</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;latitude: &nbsp;&nbsp;mass-weighted mean open ocean evaporative source latitude.</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; longitude: &nbsp;&nbsp;mass-weighted mean open ocean evaporative source longitude.</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; sst: &nbsp;&nbsp;mass-weighted mean open ocean evaporative source sea surface temperature.</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; rh2m: &nbsp;&nbsp;mass-weighted mean open ocean evaporative source 2-m relative humidity.</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; wind10: &nbsp;&nbsp;mass-weighted mean open ocean evaporative source 10-m wind speed.</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; land1/land2: percentage of precipitation sourced from continental evaporation.</p>

opencc-by-4.0Aug 2024View details →
zenodo36/100

Determination of the spinel content in cycled Li1.2Ni0.13Mn0.54Co0.13O2 using three-dimensional electron diffraction and precession electron diffraction

<p>(hkl,I)-lists for the 150 times cycled NMC material</p>

opencc-by-4.0Dec 2020View details →
zenodo36/100

CESM data: Response of the low-level jet to precession and itsimplications for proxies of the Indian monsoon

<p>This repository contains the simulation output of precession minimum and maximum experiments carried out using the CESM 1.2.0. The experimental setup is the same as that of Bosmans et al. (2014). Each simulation is run of 100&nbsp;years, and the Jun-Jul-Aug mean of the&nbsp;last 50 years of the simulation is provided here. The climatological monthly mean of total precipitation from the pre-industrial control simulation is also deposited here.&nbsp;</p> <p>&nbsp;</p> <p>Contact: Chetankumar Jalihal<br> email: jalihal@iisc.ac.in</p> <p>&nbsp;</p> <p>Acknowledgments:<br> These simulations were carried out as a part of Chetankumar Jalihal&#39;s Ph.D. thesis, funded by the Ministry of Human Resource Development, Government of India, and the&nbsp;Centre for Excellence in the Divecha Centre for Climate Change (DCCC), supported by the Department of Science and Technology, Government of India. We also acknowledge the&nbsp;Supercomputer Education and Research Centre (SERC), Indian Institute of Science, Bangalore, for making available the computation facilities to carry out the simulations.</p>

opencc-by-4.0Jul 2021View details →
zenodo36/100

Precession electron diffraction dataset from nanocrystaline Cu-Ag (FCC) alloy collected on pixelated TVIPS detector

<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam + precession mode (PED) of a nanocrystaline Cu-Ag sample, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from a nanocrystalline Cu-Ag thin film. The details are described in the following publication:</p> <p>Oellers, Tobias, et al. &quot;Thin-Film Microtensile-Test Structures for High-Throughput Characterization of Mechanical Properties.&quot; <em>ACS combinatorial science</em> 22.3 (2020): 142-149.</p> <p>The sample was prepared by punching a 3 mm diameter disc out, gluing this to a Cu single hole grid, and subsequent Ar+ ion milling until perforation at 2.5 kV using a PIPS II system (Gatan). The rough milling was followed with a 0.5 kV cleaning.</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>PED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5) and a condenser aperture size of 10 &mu;m. The probe diameter was ~ 1 nm with a convergence angle of ~2 mrad. A precession frequency of 100 Hz and a precession angle of 0.5&deg; were applied during the nanobeam scanning. Data was collected on a TemCam-XF416 pixelated CMOS<br> detector (TVIPS). The camera length as indicated in the operating software was 15 cm, and collected images were 2k by 2k in size (hardware binning of 2). The dataset comprises 150x150 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 100 GB in size and is no longer available. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 512x512. A median filter was also applied to the data.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 150 x 150 pixels</p> <p>Image shape: 512 x 512 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01155 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation). It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. A large number of the scan points are worthless. In addition, the detector background was not properly subtracted in the image, resulting in striped artifacts in the images.</p>

opencc-by-4.0Oct 2021View details →
zenodo36/100

Data Release for "The curious case of GW200129: interplay between spin-precession inference and data-quality issues"

<p>Data Release associated with&nbsp;<a href="https://arxiv.org/abs/2206.11932">The curious case of GW200129: interplay between spin-precession inference and data-quality issues Data Release</a>. We release frame files and result file for selected parameter estimation runs in the paper.</p> <p>&nbsp;</p> <p>Each directory contains .json bilby result files for the PE run described by that directory. The specific channel names and frame files that we used in the PE runs are listed below.&nbsp; The frames for the PE runs with BayesWave glitch subtraction are included in this release and are in BW_frames/frame_{glitch label}.</p> <p>&nbsp;</p> <p>L1 data with glitch subtraction:</p> <p>Channel: L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_P1800169_v4</p> <p>Link: https://zenodo.org/record/5546680/files/L-L1_HOFT_CLEAN_SUB60HZ_C01_P1800169_v4-1264314068-4096.gwf</p> <p>&nbsp;</p> <p>L1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (L1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080/L-L1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p>&nbsp;</p> <p>H1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (H1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080HL-H1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p>&nbsp;</p> <p>Channels for&nbsp;runs with BayesWave glitch-subtracted frames--</p> <p>(Note you need to read in the correct gwf file to access each channel; for example the channel for BayesWave glitch A should be accessed after reading in&nbsp;the gwf file in `BW_frames/frame_A/`)</p> <p>BayesWave glitch A (applies only to L1):&nbsp;DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_30000</p> <p>BayesWave glitch B (applies only to L1):&nbsp;DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_28395</p> <p>BayesWave glitch C&nbsp;(applies only to L1):&nbsp;DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_32752</p>

opencc-by-4.0Oct 2022View details →
zenodo36/100

Improving magnetic STEM-differential phase contrast imaging using precession

<p>Scanning transmission electron microscopy datasets and processing files&nbsp;used in the journal publication &quot;<strong>Improving Magnetic STEM-Differential Phase Contrast Imaging using Precession</strong>&quot;.</p> <p>DOI link to publication:&nbsp;<a href="https://doi.org/10.1093/micmic/ozad001">https://doi.org/10.1093/micmic/ozad001</a></p> <p>&nbsp;</p> <p><strong>Prerequisites</strong></p> <p>To run the scripts necessary to process the files, the open source packages JupyterLab, HyperSpy, pyXem, and fpd need to be installed. These notebooks were created with these package versions:</p> <ul> <li>hyperspy 1.6.4</li> <li>pyxem 0.13.3</li> <li>fpd 0.2.0</li> <li>jupyterlab 3.2.0</li> </ul> <p>&nbsp;</p> <p><strong>Data files and processing scripts</strong></p> <p>Data files are collected in .zip folders and have names that start with &quot;d00..&quot;, while processing scripts are in the Jupyter Notebook .ipynb data format whose names start with &quot;p00..&quot;. These files are divided into three main processing steps, outlined as follows:</p> <ol> <li><strong>Processing of raw data</strong>: Raw data files can be found in the d001_scans.zip folder. These are processed with the p002_get_dpc_raw.ipynb script which uses either the center of mass or phase correlation methods.</li> <li><strong>D-scan correction</strong>: The processed files from the previous step are saved in the d002_dpc_raw.zip folder. The p003_get_dpc_cor.ipynb script performs a d-scan correction on these files and saves the output in the&nbsp;d003_dpc_cor.zip folder. <ul> <li><strong>Virtual segmented detector algorithm</strong>: For comparison purposes to the other processing algorithms, a virtual segmented detector algorithm was developed and can be found in the p004_segmented_detector.ipynb script. This algorithm extracts a linear d-scan plane from already processed phase correlation files found in d002_dpc_raw.zip, subtracts it from the raw data files found in d001_scans.zip, and finally performs the processing algorithm.</li> </ul> </li> <li><strong>Plotting files</strong>:<strong>&nbsp;</strong>The p005_plot_dpc_images.ipynb script creates the figures as seen in the journal publication. The input files are those found in d003_dpc_cor.zip from the previous processing step.</li> </ol>

opencc-by-4.0Nov 2022View details →
zenodo36/100

Data in support of: `Two-Dimensional Strain Mapping with Scanning Precession Electron Diffraction: An Investigation into Data Analysis Routines'

<p>This upload contains data in support of a manuscript currently under review. More details to follow.</p>

opencc-by-4.0May 2023View details →
zenodo36/100

Phantom measurement data for 'Pure balanced steady-state free precession imaging (pure bSSFP)'

<p>This dataset contains the phantom bSSFP phase-cycled measurement data used in the published article: Sch&auml;per et al. &#39;Pure balanced steady-state free precession imaging (pure bSSFP)&#39;, Magn Reson Med. 2022;87:1886-1893 (doi: 10.1002/mrm.29086).<br> The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a 20 channel head coil. The data contains four datasets for different TR values (1.5 ms, 3 ms, 5 ms and 8 ms), each was measured with eight phase cycles (0&deg;, 45&deg;, 90&deg;, 135&deg;, 180&deg;, 225&deg;, 270&deg; and 315&deg;). For further measurement details, please refer to the mentioned original article.</p>

opencc-by-4.0Sep 2023View details →
zenodo36/100

Dataset: "GW190521: tracing imprints of spin-precession on the most massive black hole binary"

<p>This dataset contains the results presented in&nbsp;&quot;<em>GW190521: tracing imprints of spin-precession on the most massive black hole binary</em>&quot;: <a href="https://arxiv.org/abs/2310.01544">https://arxiv.org/abs/2310.01544</a>.</p> <p>The accompanying repository used to take this data and generate the figures in the paper can be found at&nbsp;<a href="https://github.com/simonajmiller/gw190521-timedomain-release/">https://github.com/simonajmiller/gw190521-timedomain-release/</a>.</p>

opencc-by-4.0Sep 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record