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2,194 results for “Diffusers”
Imaging subsurface structure of an urban area based on Diffuse-Field Theory concept using seismic ambient noise
<p>Ambient noise data for a small urban area of NER India. The data set constitute all the raw files that were used for figures in the paper by Bora et al.</p>
Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads
<p>This map contains the data used in the study 'Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads'. In this study a new method is presented to estimate average diffuse aquifer recharge of water table aquifers in temperate climates using time series analysis of water table level fluctuations. Recharge is estimated from time series models fitted to observed heads under the additional constraint that the seasonal harmonic of the observed head is reproduced as the sum of the transformed seasonal harmonics present in precipitation, evaporation, and pumping. The method is applied to measured heads obtained from piezometers situated on and around the ice-pushed sand ridge of Salland in the Netherlands. Results are compared with recharge estimates based on the saturated zone chloride mass balance.</p> <p>The data are ascii text files saved in the following compressed maps:<br> - Groundwater_chloride_concentration.zip<br> - Groundwater_head_time_series.zip<br> - Makkink_reference_evaporation_time_series.zip<br> - Measured_precipitation_time_series.zip<br> - Precipitation_chloride_concentrations.zip<br> - Pumping_time_series.zip</p> <p>For further details, please refer to the readme files contained in each submap.</p>
Polluted White Dwarfs: Mixing Regions and Diffusion Timescales
<p>Tables of diffusion timescales and surface mass fractions in MESA models for polluted DA white dwarfs.</p> <p>Includes python interpolation routines and and example plotting script, along with inlists and instructions for reproducing the MESA runs that built these tables.</p> <p>If you wish to make use of these tables in your work, please cite this paper: <a href="http://adsabs.harvard.edu/abs/2019ApJ...872...96B">http://adsabs.harvard.edu/abs/2019ApJ...872...96B</a> </p>
Modules for Experiments in Stellar Astrophysics (MESA): Convective Boundaries, Element Diffusion, and Massive Star Explosions
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJS..234...34P/abstract">Modules for Experiments in Stellar Astrophysics (MESA): Convective Boundaries, Element Diffusion, and Massive Star Explosions</a></p>
Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture
<p>Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. These are measurements of a module of the same type as “<a href="zenodo.org/record/2667772">Outdoor monitoring data of an Insolight B-series module - CPV sub-module</a>” however, in the previous measurements the module was mounted on a two-axis tracker to benchmark its performance, while in these measurements, the module’s integrated planar micro tracking system was used. This data was presented at IEEE PVSC 46 in June 2019 in Chicago. <strong><a href="https://zenodo.org/record/3349781">See preprint of conference article</a>.</strong></p> <p><strong>Monitoring campaign:</strong></p> <ul> <li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li> <li>Fixed Mounting Angle: Due South, Slope Angle = 30°</li> <li>Starting date: 30 May 2019</li> <li>End date: 14 June 2018</li> </ul> <p><strong>Description of data file:</strong></p> <ul> <li><strong>Data files format:</strong> single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file</li> <li><strong>Measurement time</strong>: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously). <ul> <li>Date Time (dd/mmm/yyyy HH:MM:SS): time in CEST / UTC+2</li> </ul> </li> <li><strong>Measured meteorological data</strong>: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times. <ul> <li>DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5°</li> <li>DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º</li> <li>DNI_Mid (W/m2): equivalent direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º</li> <li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm.</li> <li>G(41°) (W/m2): Global Inclined Irradiance as measured with a pyranometer mounted facing due south and at a slope angle of 41° (near to local latitude). Spectral range: 305 – 2800 nm.</li> <li>T_Amb (°C): ambient temperature </li> <li>Wind Speed (m/s): wind speed</li> <li>Wind Dir. (m/s): wind direction</li> </ul> </li> <li><strong>Processed meteorological data</strong>: these values are calculated from the above meteorological data and provided for convenience <ul> <li>DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun’s known declination and hour angle from the time.</li> <li>GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°).</li> <li>SMR_Top_Mid (n.d.): “Spectral Matching Ratio”. This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells.</li> </ul> </li> <li><strong>Measured module data:</strong> The module was placed in a short-ciruit condition and allowed to track using its integrated tracking system. The short circuit current was measured using shut resistors and integrated A/D channels. This hybrid module features both III-V micro cells (under concentration, with planar microtracking) and large area silicon solar cells (for diffuse capture). <ul> <li>ISC_measured_IIIV (A):</li> <li>ISC_measured_Si (A)</li> </ul> </li> <li><strong>Estimated module data:</strong> As is explained in the IEEE PVSC 46 manuscript (<a href="https://zenodo.org/record/3349781">see Preprint</a>) the following values are estimated using the previously listed measured data. <ul> <li>T_Backplane (°C)</li> <li>PMP_estimated_IIIV (W)</li> <li>PMP_estimated_Si (W)</li> </ul> </li> </ul>
Interviewees and mapping of interview questions to areas of Innovation Diffusion Theory
<p>This submission includes two files complementing the F1000Research article "Preprints and Scholarly Communication: Adoption, Practices, Drivers and Barriers" - <a href="https://doi.org/10.12688/f1000research.19619.1">https://doi.org/10.12688/f1000research.19619.1</a></p> <p>The table '<strong>Extended data</strong>' shows how interview questions for our study were built based on the features of information diffusion theory (IDT) (Rogers, 2003). It should be noted that not all interview questions were asked to all stakeholder groups based on appropriateness and time available. Some questions in the table may appear to be repeated: this is because slightly different phrasing was used based on the stakeholder interviewed.</p> <p>Legend:</p> <ul> <li>Research Funder: RF</li> <li>Research Performing Organisation: RPO</li> <li>Preprint Server: PS</li> <li>Other Service Provider: OSP</li> <li>Engaged researcher: ER</li> <li>Unengaged researcher: UR</li> </ul> <p>The table '<strong>List of interviewees</strong>' includes all stakeholders interviewed in the context of this research. More information on this study is also available in the form of a <a href="http://doi.org/10.5281/zenodo.3357727">report</a>.</p>
Magnetic diffusion in Solar atmosphere produces measurable electric fields
<p>Stokes spectra in 397, 630, and 854 nm with a field-of-view of 10"x10" that contains the analyzed Ellerman bomb</p>
Diffusion-relaxation MRI data from biomimetic phantoms
<p>Diffusion-relaxation MRI data and tools to estimate the inner fibre radius of biomimetic phantoms, as reported here:</p> <blockquote> <p><strong>Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI</strong>. Erick J. Canales-Rodríguez, Marco Pizzolato, Feng-Lei Zhou, Muhamed Barakovic, Jean-Philippe Thiran, Derek K. Jones, Geoffrey J.M. Parker, Tim B. Dyrby. Magn Reson Med. 2024; 91: 2579-2596. doi: 10.1002/mrm.29991 <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29991" rel="nofollow">https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29991</a></p> </blockquote> <p>Run the code to replicate the figures reported in the paper. We provide the raw diffusion-relaxation data (spherical mean signal) to facilitate future evaluations and developments.</p> <p> </p> <p> </p>
Diffuse Optical Tomography Dataset - Unstructured Scans
<p>This repository houses a simulated dataset designed for Frequency-Domain Diffuse Optical Tomography (FD-DOT) experiments. It includes a total of 60,000 examples, each comprised of a target volume representing 3D absorption and reduced scattering properties - randomized within a biologically realistic range for human breast tissue - plus amplitude and phase components of corresponding frequency-domain reflectance measurements, for a randomly selected subset of high-density source detector positions. The dataset encompasses raw data, preprocessed data, mesh information, and supplementary metadata.</p>
CR diffusion in micromirrors
<p>This dataset provides the relevant data used to generate the plots illustrating cosmic ray (CR) diffusion in the research paper titled <em>Efficient Micromirror Confinement of Sub-TeV Cosmic Rays in Galaxy Clusters</em>. The data captures key metrics, including the diffusion coefficients, energies, and simulation parameters, used to evaluate CR transport along magnetic field lines in galaxy clusters. The dataset enables the reproduction of the main results, including the influence of micromirrors on CR confinement and the spatial distribution of diffusion rates.</p>
Wave-Cascade Nested Model: The Urban-rural Diffusion Model of Suzhou(Essays on Linguistics 2025(1))
<p>This is the high-resolution images (Figure 3) from published paper entitled Wave-Cascade Nested Model: The Urban-rural Diffusion Model of Suzhou (Essays on Linguistics 2025(1)) (in Chinese 波浪-级联嵌套模型”:苏州城乡方言的扩散模式 (《语言学论丛》2025年第1期)) . </p> <p>Given the journal doesn't provide color printing or needs to compress images due to size limitations, it offers readers access to the high-quality and full-color image.</p> <p>If you have any question, please contact me without any hesitation! Thank you very much!</p>
Conditional Neural Field Latent Diffusion Model for Generating Spatiotemporal Turbulence
Open the record for dataset details and reuse information.
Stacking Sequence, Interlayer Bonding, Termination Group Stability and Li/Na/Mg diffusion in MXenes
<p>Input-files and structures.</p> <p>tree-structure of files: </p> <p>.<br> ├── h2_molecule_in_vacuum<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── readme<br> ├── ti3c2t2<br> │ ├── octahedral<br> │ │ ├── 1x1_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_li_2_image<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 03<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ └── neb.dat<br> │ │ ├── neb_li_5_image<br> │ │ │ ├── neb.dat<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 03<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 04<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 05<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ ├── fe.dat<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── 06<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── old_POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── POSCAR<br> │ │ │ └── neb.dat<br> │ │ ├── neb_li_endpoint_2image<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_li_endpoint_5image<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_li_startpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_mg<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── fe.dat<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ ├── POSCAR<br> │ │ │ │ └── fe.dat<br> │ │ │ ├── 03<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ └── neb.dat<br> │ │ ├── neb_mg_5_image<br> │ │ ├── neb_mg_endpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_mg_startpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_na<br> │ │ │ └── output<br> │ │ │ ├── 00<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 01<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 02<br> │ │ │ │ ├── CONTCAR<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── 03<br> │ │ │ │ ├── OUTCAR<br> │ │ │ │ └── POSCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ └── neb.dat<br> │ │ ├── neb_na_endpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_na_startpoint<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── neb_template<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── site_test<br> │ │ ├── cmcm<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── mtmt<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── tctc<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── singlelayer<br> │ │ ├── sl_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── sl_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── trigonal<br> │ ├── 1x1_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_li_2_image<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_li_5_image<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 04<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 05<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 06<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_li_endpoint_2image<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_li_endpoint_5image<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_li_startpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_mg<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_mg_5_image<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 04<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 05<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 06<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_mg_endpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_mg_startpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_na<br> │ │ └── output<br> │ │ ├── 00<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 01<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 02<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 03<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ └── neb.dat<br> │ ├── neb_na_endpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── neb_na_startpoint<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── neb_template<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── v2ct2<br> │ ├── octahedral<br> │ │ ├── 1x1_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── 1x1_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── singlelayer<br> │ │ ├── sl_clcl<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_fo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_ohho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── sl_oho<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── sl_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── trigonal<br> │ ├── 1x1_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── 1x1_oo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── xrd_ti3c2t2<br> │ ├── octahedral<br> │ │ ├── 1x1_ff<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── 1x1_oo<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── mixed1<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ ├── mixed2<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── mixed3<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── trigonal<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── mixed1<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── mixed2<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── mixed3<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── zr3c2t2<br> ├── octahedral<br> │ ├── 1x1_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 1x1_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── 1x1_oo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── singlelayer<br> │ ├── sl_clcl<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_ff<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_fho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_fo<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_ohho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── sl_oho<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── sl_oo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── trigonal<br> ├── 1x1_clcl<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_ff<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_fho<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_fo<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_ohho<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── 1x1_oho<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── 1x1_oo<br> └── output<br> ├── CONTCAR<br> ├── INCAR<br> ├── KPOINTS<br> ├── OUTCAR<br> └── POSCAR</p> <p> </p>
Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes
<p>Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management, and infodemic controlling. However, predicting the scale of an information cascade in a long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the follower-followee network, retweet network, tweet content, and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension to information diffusion process and may assist in the control of such process.</p>
Dataset for the paper "Boosting Charge Carrier Mobilities in Upgraded Metallurgical Grade Silicon by Phosphorous Diffusion Gettering"
<p>Dataset related to the publication "Boosting Charge Carrier Mobilities in Upgraded Metallurgical Grade Silicon by Phosphorous Diffusion Gettering" in Advanced Energy and Sustainable Research 2022, 2200077 (https://doi.org/10.1002/aesr.202200077)</p>
Geochemical, timescales data in orthopyroxenes from Kizimen 2010 eruption and database of studies linking diffusion timescales with monitoring signals.
<p>This dataset comprises the geochemical data on whole rock analysis, melt inclusions, residual glasses, magnetites and orthopyroxenes in samples from the 2010 eruption of Kizimen volcano (Kamchatka), as well as the timescales modelled in the orthopyroxenes and a database of studies linking diffusion timescales with monitoring signals, associated with the publication: Ostorero, L., Balcone-Boissard, H., Boudon, G. <em>et al.</em> Correlated petrology and seismicity indicate rapid magma accumulation prior to eruption of Kizimen volcano, Kamchatka. <em>Commun Earth Environ</em> <strong>3</strong>, 290 (2022). https://doi.org/10.1038/s43247-022-00622-3</p> <p> </p>
Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death
<p>This dataset accompanies the article entitled <em>Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death</em>, published in ACS Cent. Sci. (<a href="https://doi.org/10.1021/acscentsci.2c01078">https://doi.org/10.1021/acscentsci.2c01078</a>).</p> <p><strong>subboxes_overview.xlsx: </strong>This spreadsheet contains information about the protein composition of each sub-box.</p> <p><strong>BOX_GEOMETRIES.zip: </strong>Starting structures (saved in Gromos87 .gro format) used for the production runs of the all-atom sub-boxes.</p> <p><strong>PROTEIN_COM_MSD.zip: </strong>Text files containing the mean square displacements (MSDs) of the centers of mass (COMs) of individual proteins, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds while the MSD values are given in nm<sup>2</sup>. For each sub-box, the protein numbering corresponds to the subboxes_overview.xlsx spreadsheet.</p> <p><strong>CHAIN_COM_MSD.zip: </strong>Text files containing the mean square displacements (MSDs) of the centers of mass (COMs) of individual polypeptide chains, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds while the MSD values are given in nm<sup>2</sup>. For each sub-box, the chain numbering corresponds to the subboxes_overview.xlsx spreadsheet.</p> <p><strong>CHAIN_ROTACF.zip: </strong>Text files containing the rotational autocorrelation functions for individual polypeptide chains, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds; the autocorrelation functions were calculated using a second-order Legendre polynomial. For each sub-box, the chain numbering corresponds to the subboxes_overview.xlsx spreadsheet.</p>
Diffusion jump structures for H, N and O atoms
<p>Structures of minima and TSs or directly entire minimum energy paths for diffusion jumps of H, N and O atoms on water clusters. </p>
Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography</em>”</strong> in Optics Express (doi.org/10.1364/OE.474279<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 10 minutes and 2D flow profile analysis may take up to one hour.</p> <p>For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, <em>Q</em>. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement.</p> <p>For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, <em>Q</em>. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Usability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 15-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 22-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 08-07-2022.zip</p> </td> <td> <p>2D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>All measurements</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>Processing.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines particle size from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Shape.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines axial beam shape from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Doppler Angle.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow Profile.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Number Density.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines particle number density raw OCT spectra.</p> </td> </tr> <tr> <td> <p>2D Flow Profile.py</p> </td> <td> <p>2D measurements</p> </td> <td> <p>This script determines 2D flow profiles from raw OCT spectra.</p> </td> </tr> </tbody> </table> <p> </p>
Dataset for publication: ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks
<p>Dataset related to article </p> <p>Simon, F., J.-Y. Tinevez, S. van Teeffelen (2023) ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks. Journal of Cell Biology</p> <p>Movies: example movies for PBP1b and RodZ. G0 = 370% expression level, G1 = 130% expression level, G2 = 30% expression level.</p> <p>Tracks: resulting tracks from the replicates, each replicate is composed of several movies (one xml file per movie). G0 = 370% expression level, G1 = 130% expression level, G2 = 30% expression level.</p> <p>See the material and method section for more details on the experimental conditions and the analysis pipeline.<br> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.