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3,264 results for “fasting”
Fast Symbolic Computation of Bottom SCCs - TACAS 2024 artifact
<p>This is the artifact for the paper "<em>Fast Symbolic Computation of Bottom SCCs</em>", by Anna B. Jakobsen, Rasmus S. M. Jørgensen, Jaco van de Pol and Andreas Pavlogiannis, appearing in TACAS 2024.</p> <p>The artifact contains the LTSmin toolset, extended with an implementation of the algorithms from the paper, to compute the Bottom Strongly Connected Components of a directed graph, provided symbolically by BDDs (Binary Decision Diagrams).</p> <p>The artifact also contains the data set, consisting of directed graphs (state spaces) specified in DVE (Divine), PNML (Petri Nets) and BN (Boolean Networks).</p> <p>The file README.md contains the instructions how to setup the artifact on Ubuntu and how to run the experiment scripts.</p>
Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis
<p>MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.</p>
FAST loss-cone electron precipitation database
<p>A FAST electron precipitation database derived from FAST EESA observations, covering beginning of mission (October 1996) through 2009.</p> <p>Number flux ('j') and energy flux ('je') moments are produced by integrating EESA measurements over all energies above 70 eV<br> up to the EESA detector limit (30 keV), and over all pitch angles within the earthward portion of the loss cone (see references). </p> <p>================================<br> EXPLANATION OF DATAFRAME COLUMNS<br> ================================</p> <p>'j' : units of #/cm^2-s (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> 'je' : units of mW/m^2 (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> <br> 'jerr' : units of #/cm^2-s (Number flux uncertainty, calculated using the Gershman et al (2015) method)<br> 'jeerr' : units of mW/m^2 (Energy flux uncertainty, calculated using the Gershman et al (2015) method)<br> <br> 'orbit' : FAST orbit number<br> 'alt' : FAST geodetic altitude, in km. (FAST altitude ranges from ~300-4180 km)<br> 'apexmlt' : Magnetic local time in Apex-110 coordinates (see Laundal and Richmond (2016))<br> 'apexmlat' : Magnetic latitude in Apex-110 coordinates (see Laundal and Richmond (2016))</p> <p>'shadowRegion110' : Integer indicator of the region of the Earth's shadow that FAST's field-line footpoint at 110-km altitude lands ind.<br> Takes on values [0,1,2], corresponding to ['Umbra','Penumbra','Sunlit']. <br> Calculated by mapping FAST's location to 110-km altitude in Apex coordinates and then following the methodology of Jia et al. (2015).</p> <p>'mono' = 0,1,2 : 'not monoenergetic','weak monoenergetic','strict monoenergetic'<br> 'broad' = 0,1,2 : 'not broadband','weak broadband','strict broadband'<br> 'diffuse' = 0,1 : 'not diffuse','diffuse'</p> <p>'mono', ' broad', and 'diffuse' follow the Hatch et al. (2016) FAST adaptation of the Newell et al. (2009) classification scheme<br> *NOTE: I do NOT force 'mono' and 'broad' to be exclusive categories! I consider it fine for precipitation to be identified as both 'broad' and 'mono'</p> <p>'drop' : Boolean indicating whether a row should be dropped from the DataFrame</p> <p>==========<br> REFERENCES<br> ==========<br> Gershman, D. J., Dorelli, J. C., F.-Viñas, A., & Pollock, C. J. (2015). The calculation of moment uncertainties from velocity distribution functions with random errors. Journal of Geophysical Research A: Space Physics, 120(8), 6633–6645. https://doi.org/10.1002/2014JA020775</p> <p>Hatch, S. M., Chaston, C. C., & LaBelle, J. (2016). Alfvén wave-driven ionospheric mass outflow and electron precipitation during storms. Journal of Geophysical Research: Space Physics, 121(8), 7828–7846. https://doi.org/10.1002/2016JA022805</p> <p>Hatch, S. M., Labelle, J., Lotko, W., Chaston, C. C., & Zhang, B. (2017). IMF control of Alfvénic energy transport and deposition at high latitudes. Journal of Geophysical Research: Space Physics, 122(12). https://doi.org/10.1002/2017JA024175</p> <p>Jia, X., Xu, M., Pan, X., & Mao, X. (2017). Eclipse Prediction Algorithms for Low-Earth-Orbiting Satellites. IEEE Transactions on Aerospace and Electronic Systems, 53(6), 2963–2975. https://doi.org/10.1109/TAES.2017.2722518</p> <p>Laundal, K. M., & Richmond, A. D. (2016). Magnetic Coordinate Systems. Space Science Reviews, 1–33. https://doi.org/10.1007/s11214-016-0275-y</p> <p>Newell, P. T., Sotirelis, T., & Wing, S. (2009). Diffuse, monoenergetic, and broadband aurora: The global precipitation budget. Journal of Geophysical Research, 114, A09207. https://doi.org/http://dx.doi.org/10.1029/2009JA014326<br> </p>
Data from "Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography"
<p>Data presented in the Science Advances manuscript "<em>Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography</em>" by Yushchenko M., Sarracanie M., Salameh N.</p> <p>See further details in <em>Description.txt.</em></p> <p>The 3D wave datasets acquired in humans at 0.1 T can be used for elastogram reconstruction with appropriate methods.<br> <br> </p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling" to be published in the journal Animal - Open Space. </p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
OOPSLA 2022 Artifact for "A Fast in-place Interpreter for WebAssembly"
<p>This archive includes</p> <ul> <li>source-code snapshots of 3 Web Engines for executing WebAssembly</li> <li>3 non-Web engines: Wasm3, the WebAssembly Micro-Runtime, and the Wizard Research Engine</li> <li>binary builds for Linux x86-64</li> <li>build instructions</li> <li>PolyBenchC benchmark Wasm binaries</li> <li>benchmarking setup and scripts</li> <li>data collected from experiments included in the OOPSLA 2022 paper</li> <li>instructions for running the benchmarks on Linux systems</li> </ul>
Data, scripts, and figure of the article: The fasting heat production of broilers is a function of their body composition
<p>This data set contains the data, JMP scripts, and figures of the article titled "The fasting heat production of broilers is a function of their body composition" to be published in the journal Animal - Open Space.</p>
Johannes Fasting (f1710)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Johannes Fasting<br><u>musiXplora-ID</u>: f1710<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/f1710">https://musixplora.de/mxp/f1710</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 1751<br><u>Place of Birth</u>: Kopenhagen<br><u>Date of Death</u>: 1816<br><u>Place of Death</u>: Kopenhagen<br><u>First Mentioned</u>: 1773<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Blechblasinstrumentenbauer<br><u>Main Place of Activity</u>: Kopenhagen<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Vater</td><td>Johan Jacob Fasting</td><td><a href="https://musixplora.de/mxp/f1711">f1711</a></td></tr></tbody></table><br><u>Persönlicher Umkreis:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Netzwerk</td><td>Netzwerkpartner</td><td>Johann Abraham Peter Schulz</td><td><a href="https://musixplora.de/mxp/s4311">s4311</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Johan Jacob Fasting (f1711)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Johan Jacob Fasting<br><u>musiXplora-ID</u>: f1711<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/f1711">https://musixplora.de/mxp/f1711</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 23 October 1789<br><u>Place of Birth</u>: Kopenhagen<br><u>Date of Death</u>: 05 September 1847<br><u>Place of Death</u>: Kopenhagen<br><u>First Mentioned</u>: 1820<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Blechblasinstrumentenbauer<br><u>Main Place of Activity</u>: Kopenhagen<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Sohn</td><td>Johannes Fasting</td><td><a href="https://musixplora.de/mxp/f1710">f1710</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Data package for "Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation"
<p>This dataset contains supporting data for the publication:</p> <p><em>Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation</em></p> <p>A. Castagnède, L. Filion, and F. Smallenburg, J. Chem. Phys. 160 (2024), doi:10.1063/5.0209178, arXiv:2403:12755</p> <p> </p> <p><strong>Contents:</strong></p> <p>The main folder <em>data_package</em> contains three subfolders: <em>figures</em>, <em>SLNN</em>, and <em>snapshots</em>. The <em>figures</em> subfolder contains supporting data for each of the figures found in the publication, accompanied by details on statepoints and methods in individual README files. The <em>SLNN</em> subfolder contains the trained neural network classifier used in this work for crystalline phase identification, alongside usage instructions and an exemple system to analyze. Finally, the <em>snapshots</em> subfolder contains supplementary snapshots of the crystalline clusters obtained in simulations. </p> <p> </p>
Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions
<div> <div>The rich information of electron energy-loss spectroscopy (EELS) comes from the complex inelastic scattering process whereby fast electrons transfer energy and momentum to atoms, exciting bound electrons from their ground states to higher unoccupied states. To quantify EELS, the common practice is to compare the cross-sections integrated within an energy window or fit the observed spectrum with theoretical differential cross-sections calculated from a generalized oscillator strength (GOS) database with experimental parameters [1].</div> <div> </div> </div> <div> <div> <div> <div>The previous Hartree-Fock-based [2] or DFT-based [3] GOS was calculated from Schrödinger's solution of atomic orbitals, which does not include the full relativistic effects. Here, we attempt to go beyond the limitations of the Schrödinger solution in the GOS tabulation by including the full relativistic effects using the Dirac equation within the local density approximation using FAC [4], which is particularly important for core-shell electrons of heavy elements with strong spin-orbit coupling. This has been done for all elements in the periodic table (up to Z = 118) for all possible excitation edges using modern computing capabilities and parallelization algorithms. The relativistic effects of fast incoming electrons were included to calculate cross-sections that are specific to the acceleration voltage. We make these tabulated GOS available under an open-source license to the benefit of both academic users as well as allowing integration into commercial solutions.</div> <div> </div> <div>If you wish to be notfied by the database updates, please register <a href="https://forms.gle/ddpJSPrCbPZNL1oH7" target="_blank" rel="noopener">here</a>.</div> <div> </div> <div>For details, you can find the paper on <a href="https://arxiv.org/abs/2405.10151">arxiv</a>.</div> </div> </div> </div> <p>Database Details:</p> <ul> <li>Covers all elements (Z: 1-108) and all edges</li> <li>Large energy range: 0.01 - 4000 eV</li> <li>Large momentum range: from minimum momentum transfer to double Bethe ridge for each edge. Adaptive momentum sampling is developed in such a manner to maximize the physical information for a given finite number of sampling points. For example, for C edge this range is 0.14 -67 Å-1 </li> <li>Fine log sampling: 128 points for energy and 256 points for momentum</li> <li>Data format: GOSH [3]</li> </ul> <p>Calculation Details:</p> <ul> <li>Single atoms only; solid-state effects are not considered</li> <li>Unoccupied states before continuum states of ionization are not considered; no fine structure</li> <li>Plane Wave Born Approximation</li> <li>Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when a core-shell</li> <li>electron is excited</li> <li>Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; A modified local density approximation is used for the correct asymptotic behavior of the exchange energy; continuum states are normalized against asymptotic form at large distances</li> <li>Both large and small component contributions of Dirac solutions are included in GOS</li> <li>Final state contributions are included until the contribution of the last states falls below 0.1%. A convergence log is provided for reference.</li> </ul> <p>Version 1.6.5 release note:</p> <ul> <li>Add a compact version of the database which uses (a) single precesion, (b) 80x80 sampling in the energy and momentum space (c) 'gzip' to compress the gos data array. This helps for user with limited bandwidth for downloading.</li> </ul> <p>Version 1.6.1 release note:</p> <ul> <li>Add missing metadata</li> </ul> <p>Version 1.6 release note:</p> <ul> <li>Improved convergence for M and N edges for some elements</li> </ul> <p>Version 1.5 release note:</p> <ul> <li>Adaptive sampling for momentum space (previously it is fixed at 0.05 -50 Å-1, now adaptive for each edge)</li> <li>Improved convergence</li> </ul> <p>Version 1.2 release note:</p> <ul> <li>Add “File Type / File version” information</li> </ul> <p>Version 1.1 release note:</p> <ul> <li>Update to be consistent with GOSH data format [3]</li> <li>All the edges are now within a single hdf5 file.</li> <li>A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å.</li> <li>Great thanks to Gulio Guzzinati for his suggestions and sending conversion script for GOSH format. </li> </ul> <p> </p> <p>[1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224.</p> <p>[2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243.</p> <p>[3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765</p> <p>[4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689.</p>
Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method
<p>This dataset contains a GitHub repository containing all the data, analysis, Nextflow workflows and Jupyter notebooks to replicate the manuscript titled "Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method".</p> <p>It also contains the Multiple Sequence Alignments (MSAs) generated and well as the main figures and tables from the manuscript.</p> <p>The repository is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`.</p> <p>For details on how to use the regressive alignment algorithm, see the T-Coffee software suite (https://github.com/cbcrg/tcoffee).</p>
Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)
<p>This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br>The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase.<br>For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder.<br>For further measurement details, please refer to the mentioned original article.</p>
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
Fast calculation methods for the magnetic field of particle lattices: Datasets and scripts
<div>*********************************************** README.txt **************************************************</div> <div> </div> <div>Title: Fast calculation methods for the magnetic field of particle lattices: </div> <div>Datasets and scripts</div> <div>Version: 1.0</div> <div>Date of Release: 2024/10/11</div> <div>Identifier: doi:10.5281/zenodo.13930969</div> <div>Permalink: http://dx.doi.org/10.5281/zenodo.13930969</div> <div> </div> <div>*************************************************************************************************************</div> <div> </div> <div>Associated publication: I. Royo-Silvestre, D. Gandia, J. J. Beato-López, E. Garaio, C. Gómez-Polo </div> <div>"Fast calculation methods for the magnetic field of particle lattices" </div> <div>(paper yet to be published)</div> <div> </div> <div>Link to publication: (paper yet to be published)</div> <div> </div> <div>Suggested citation: Please reference the associated publication above when using any datasets or</div> <div> materials described in this README file.</div> <div> </div> <div>Contact information: Isaac Royo Silvestre, </div> <div>Universidad Pública de Navarra, </div> <div>Pamplona, Spain, </div> <div>isaac.royo@unavarra.es</div> <div> </div> <div>License: CC BY 4.0</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>This directory contains the following datasets and supplementary materials:</div> <div> </div> <div> ------------------------------</div> <div> SCRIPTS</div> <div> ------------------------------</div> <div> </div> <div> - scripts.zip Matlab scripts (compressed zip file) used to calculate the magnetic field of </div> <div>lattices of magnetic particles by analytical and semianalytical methods (more information in the associated paper) </div> <div> </div> <div> --------------------------------</div> <div> DATASETS</div> <div> --------------------------------</div> <div> </div> <div> - data.zip: Tabular data required to plot curves (compressed zip file) in csv format,</div> <div>also data used to obtain average values</div> <div> </div> <div> </div> <div>Specific documentation of each file is described in readme files.</div> <div> </div> <div>Refer to the original manuscript (see above) for additional information regarding the collection and generation of these data.</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div> ---------------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'scripts.zip'</div> <div> ---------------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>and multiple .m files. m files are Matlab scripts, text files that can be read using any text editor. However it has to be executed via Matlab, scripts contain documentation as comments.</div> <div> </div> <div> ---------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'data.zip'</div> <div> ---------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>multiple .dat files with data used to obtain averaged valus (see format in the readme.txt </div> <div>contained in the zip), and a folder "curves".</div> <div>The curves folder contains tabular data in .csv files, these files can be used to plot the curves</div> <div>in the manuscript.</div> <p> </p>
Dataset for A Fast Frozen Phonon Algorithm Using Mixed Static Potentials
<p>Multislice simulation dataset for "A Fast Frozen Phonon Algorithm Using Mixed Static Potentials"</p>
Theta and alpha power across fast and slow timescales in cognitive control
<p>This dataset contains the raw EEG data collected in a study investigating the neural signatures of extensive training. Specifically, 30 subjects performed a simple stimulus-action association task in which they learned to respond with one out of two key presses to a stimulus presented on screen. In each block, four different stimuli were presented, meaning that half of the stimuli was associated with a right response (press 'j') and the other half was associated with a left response (press 'f'). To investigate distinct timescale, stimuli repeated both within experimental blocks (each stimulus 8 times), and one set of stimuli repeated across experimental blocks (8/16 blocks contained the same four stimuli).</p> <p>This dataset contains the raw EEG data collected using a BioSemi 64 channel setup. Also, six external electrodes were used to measure eye activity (left and right mastoid; lateral canthi of both eyes; above and below left eye). The electrodes were placed using the 10-20 system, and this system contained a posterior CMS-DLR electrode combination.</p> <p><strong>The SUBID.npy files contain the behavioral data</strong> of the experiment phase (512 trials; i.e. exercise phase data is not included).<strong>The SUBID.bdf files contain EEG data</strong> of the experiment phase (512 trials; no data was collected during the exercise phase). Finally, the files titled "<strong>theta_alpha_beta_behavioural</strong>" are used in the analysis and plotting scripts (see GitHub).</p>
Data from: Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis
<p>Data and analysis in R for the publication "Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis" by Ossanna & Gornish (2023), <em>Journal of Applied Ecology</em>, <em>60</em>(2), 218-228. <a href="http://doi.org/10.1111/1365-2664.14324">https://doi.org/10.1111/1365-2664.14324</a>.</p>
Fast measurement of the gradient system transfer function at 7 T
<p>Measurement data complementing our publication "Fast measurement of the gradient system transfer function at 7 T" (DOI: https://doi.org/10.1002/mrm.29523). The corresponding MATLAB code is available at https://github.com/expRad/Fast_GIRF .</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.