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2,194 results for “Diffusers”
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (Inter-Mito)
<p>Microscopy movies corresponding to interphase and mitotic cells in publication (add DOI when available).</p>
Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination
<p>We uploaded the daset releatet t the manuscript: Fusco R, Granata V, Pariante P, Cerciello V, Siani C, Di Bonito M, Valentino M, Sansone M, Botti G, Petrillo A. Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination. Magn Reson Imaging. 2021 Jan;75:51-59. doi: 10.1016/j.mri.2020.10.008. Epub 2020 Oct 17. PMID: 33080334.</p>
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (code)
<p>GP-FBM and GP-Tool codes and algorithms as in publication day. For updated version: https://github.com/guilmont/GP-Tool</p>
Dataset (part 1) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"
<p>Dataset (part 1) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"</p>
Dataset (part 2) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"
<p>Dataset (part 2) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"</p>
Can volcanic trace elements facilitate Covid-19 diffusion? A hypothesis stemming from the Mount Etna area, Sicily
<p>In December 2019, severe cases of pneumonia of unknown aetiology were reported in Wuhan city, in China. Lately, the pneumonia was related to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and the diseases was termed coronavirus disease-2019 (COVID-19). At the end of January 2020, the infection spread all over Italy, but with high infection rates and mortality in the northern part, especially in Lombardy, the most industrialized and polluted region of the country. It is noteworthy that a strong association between severe viral respiratory disease and air pollution has been described. Air pollutant could be solid particles, liquid droplets, or gases and can be of natural origin (such as ash from a volcanic eruption) or released from motor vehicle depletes (carbon monoxide gas) or factories (sulfur dioxide). Volcanic eruptions release large amounts of sulphuric acid, hydrogen sulfide, and hydrochloric acid into the atmosphere. Pulmunary diseases spreadby means of small droplets in thebreath, also called aerosols, and air pollution may facilitate the outside survival of viruses. We suppose that ash and gases emitted from the Mount Etna contributed to air pollution, potentially favouring the major contagion of COVID-19 in the eastern flank of the mountain, as in Catania city. In fact, ash and gases (with regard to radon) are usually particularly intense in winter, with a reduction of emission of specific metals with warmer weather. This is the first paper that elaborates the hypothesis of a potential role of volcanic gases and heavy metals-related air pollution, combined to specific climatic conditions and regional topography, in favouring severe COVID-19 diffusion in Sicily. Clinical and epidemiological studies are needed to support the hypothesis and plan the due prevention and awareness-raising campaigns.</p>
Training data and prediction results for predicting water diffusion in various polymers using MD-GAN
<p>Training data and prediction results for predicting water diffusion in various polymers using MD-GAN.</p> <p>MD trajectories used as input, MSDs calculated from MD, predicted MSDs, and diffusion coefficient values.</p> <p>MD trajectories were obtained from the following paper.<br> Kojima, H.; Handa, K.; Yamada, K.; Matubayasi, N. Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions. <em>The Journal of Physical Chemistry B</em> 2021, 125, 9357-9371.</p> <p> </p>
Characterizing diffusion-controlled release of small-molecules using quantitative MRI: Application to orthopedic infection - dataset
<p>Dataset for paper submitted to Scientific Reports titled "Characterizing diffusion-controlled release of small-molecules using quantitative MRI: Application to orthopedic infection"</p>
Bergmiller et al Raw mRNA Diffusion Data
<p>Raw tracking data of mRNA particle diffusion in E. coli</p>
FIGURE 16. Opuntia hyptiacantha. A. Elliptical cladode. B. Elliptical areola, short black trichomes. C. Erect and diffuse white spines with yellowish apex. D in Molecular and morphological notes on Opuntia ser. Streptacanthae (Cactaceae)
FIGURE 16. Opuntia hyptiacantha. A. Elliptical cladode. B. Elliptical areola, short black trichomes. C. Erect and diffuse white spines with yellowish apex. D. Acute flower buds, reddish segments of perianth with auminate apex. E. Flower button, side view, elliptical pericarpel. F. Flower in anthesis.
FIGURE 8. Opuntia lasiacantha. A. Obovate cladode. B. Elliptical areola, short black trichomes. C. Erect and diffuse yellowish white spines. D in Molecular and morphological notes on Opuntia ser. Streptacanthae (Cactaceae)
FIGURE 8. Opuntia lasiacantha. A. Obovate cladode. B. Elliptical areola, short black trichomes. C. Erect and diffuse yellowish white spines. D. Acute flower buds, red segments of perianth with acuminate apex. E. Flower button, side view, slightly cylindrical pericarpel. F. Flower in anthesis.
Towards the Detection of Diffusion Model Deepfakes (Dataset)
<p>This upload contains the dataset used in <a href="https://arxiv.org/abs/2210.14571">Towards the Detection of Diffusion Model Deepfakes</a>.</p> <p>Further details and instructions are provided in the <a href="https://github.com/jonasricker/diffusion-model-deepfake-detection">project's Github repository</a>.</p>
Database for "A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves"
<p>Database for <strong>"A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves" </strong>submitted to Space Weather by Murphy et al. </p> <p>The repository contains 2 datasets:</p> <ul> <li>The power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> <li>The power spectral density of the azimuthal electric field from THEMIS and Van Allen Probes along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> </ul> <p>Both datasets are provided as an IDL save file and as an HDF5 file.</p> <p>The IDL data can be loaded using: </p> <pre><code>filename='electric_field_psd.sav' filename='magnetic_field_psd.sav' restore, filename, /verbose</code></pre> <p>The HDF5 file can be opened and investigate using (small changes will be required to store each variable):</p> <pre><code class="language-python">import h5py filename = 'magnetic_field_psd.h5' # magnetic field data filename = 'magnetic_field_psd.h5' # electric field data with h5py.File(filename, "r") as f: # loop through all keys (data) #print key and key attribute and size for i in f.keys(): print(f"{i} - {f[i].attrs['attributes']}, shape - {f[i].shape}") ds_obj = f[i] # returns as a h5py dataset object ds_arr = f[i][()] # returns as a numpy array</code></pre> <p>Below is a description of unique and common variables in each file. The psd variables have a shape [f,t], indicating the first dimension is frequency and the second is time, the f_mhz variable has shape [f], and all time series have shape [t]; here [f] and [t] denotes the number of elements in frequency and time arrays. </p> <p>------------------------------------</p> <p><strong>Magnetic field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>magnetic_field_psd.h5</li> <li>magnetic_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - nT<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Electric field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>electric_field_psd.h5</li> <li>electric_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the azimuthal electric field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - (mV/m)<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Common Data in the Magnetic and Electric Field Datasets (variable in file):</strong></p> <ul> <li>probe <ul> <li>Corresponding satellite of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>t <ul> <li>Time stamp of each time series; number of seconds since 1970 (UNIX time), [t]</li> <li>Units - s</li> <li>Shape [t]</li> </ul> </li> <li>f_mhz <ul> <li>Frequency of psd data, [f]</li> <li>Units - mHz</li> <li>Shape [f] - (19)</li> </ul> </li> <li>ae <ul> <li>OMNI AE index of each time stamp</li> <li>Units - nT'</li> <li>Shape [t]</li> </ul> </li> <li>al <ul> <li>OMNI AL index of each time stamp, units - nT</li> <li>Shape [t]</li> </ul> </li> <li>au <ul> <li>OMNI AU index of each time stamp </li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_t <ul> <li>OMNI IMF B of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_x <ul> <li>OMNI IMF Bx (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_y <ul> <li>OMNI IMF By (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_z <ul> <li>OMNI IMF Bz (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>dst <ul> <li>OMNI Dst of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>kp - <ul> <li>OMNI Kp (Kp*10) of each time stamp</li> <li>units - NA</li> <li>Shape [t]</li> </ul> </li> <li>l_sh <ul> <li>L-shell of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>ls_t05 <ul> <li>L* from TS05 of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>mlt <ul> <li>Magnetic Local Time of each time stamp</li> <li>Unit - hour</li> <li>Shape [t]</li> </ul> </li> <li>n <ul> <li>OMNI Solar Wind Proton Density of each time stamp</li> <li>Units - n/cc</li> <li>Shape [t]</li> </ul> </li> <li>pdyn <ul> <li>OMNI Solar Wind Dynamic Pressure (flow pressure) of each time stamp</li> <li>Units - nPa</li> <li>Shape [t]</li> </ul> </li> <li>symh - b'OMNI Sym-H of each time stamp, units - nT', shape - (477205,)</li> <li>v_t <ul> <li>OMNI Solar wind V of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_x <ul> <li>OMNI Solar wind Vx (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_y <ul> <li>OMNI Solar wind Vy (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_z <ul> <li>OMNI Solar wind Vz (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p>
Diffusion and perfusion imaging in rectal cancer restaging
<p>I uploaded the images of the manuscript: Diffusion and perfusion imaging in rectal cancer restaging</p>
Monitoring temporal changes of velocity and attenuation from ultrashort diffuse records
<p>This repository contains the raw field data in the manuscript entitled "Monitoring temporal changes of velocity and attenuation from ultrashort diffuse records" by Ning Gu, Haoran Meng, Bo Yang, Xin Liu, Yehuda Ben-Zion, Junxin Guo, Bin Luo, Zhen Guo, Shuye Huang, Shichuan Yuan, Xiaofei Chen.</p>
Supporting data for "Rapid determination of solid-state diffusion coefficients in Li-based batteries via intermittent current interruption method"
<p>This is the dataset of electrochemical and operando X-ray diffraction experiments for our publication "Rapid determination of solid-state diffusion coefficients in Li-based batteries via intermittent current interruption method". This archive contains the raw data and scripts written in R used in the analysis and presentation of the results in this manuscript.</p> <p><strong>Abstract of the manuscript:</strong></p> <p>The galvanostatic intermittent titration technique (GITT) is considered the go-to method for determining the Li<sup>+</sup>diffusion coefficients in insertion electrode materials. However, GITT-based methods are either time-consuming, prone to analysis pitfalls or require sophisticated interpretation models. Here, we propose the intermittent current interruption (ICI) method as a reliable, accurate and faster alternative to GITT-based methods. Using Fick’s laws, we prove that the ICI method renders the same information as the GITT within a certain duration of time since the current interruption. Via experimental measurements, we also demonstrate that the results from ICI and GITT methods match where the assumption of semi-infinite diffusion applies. Moreover, the benefit of the non-disruptive ICI method to operando materials characterization is exhibited by correlating the continuously monitored diffusion coefficient of Li<sup>+</sup> in a LiNi<sub>0.8</sub>Mn<sub>0.1</sub>Co<sub>0.1</sub>O<sub>2</sub>-based electrode to its structural changes captured by operando X-ray diffraction measurements.</p>
Data Archive for "Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification"
<p>This repository contains the training data and pretrained models for the paper "Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification".</p> <p>To use the data, clone the repository at <a href="https://github.com/MeteoSwiss/ldcast">https://github.com/MeteoSwiss/ldcast</a>. Unzip the files as follows:</p> <ul> <li>Demo files "ldcast-demo-20210622.zip" to the "data" directory</li> <li>Training and evaluation data archive "ldcast-datasets.zip" to the "data" directory</li> <li>Pretrained model archive "models-genforecast.zip" to the "models" directory</li> </ul>
Data for: "Diffusion during collective turns in bird flocks under predation"
<p>This repository contains the data accompanying the paper:</p> <p><em>Papadopoulou M, Hildenbrandt H, Hemelrijk CK (2023) Diffusion during collective turns in bird flocks under predation. Frontiers in Ecology and Evolution 11 (2023): 1198248. https://doi.org/10.3389/fevo.2023.1198248 <br></em></p> <p>The simulated data of bird flocks performing collective turns are generated by the computational model <em>ColT. </em>More information about the model and data can be found in the README file and the connected GitHub repository:<a href="http://github.com/marinapapa/ColT-Model/"> https://github.com/marinapapa/ColT-Model/</a></p> <p>The empirical data used in the paper are previously published for:</p> <p><em>Sankey DWE, Storms RF, Musters RJ, Russell WT, Hemelrijk CK, Portugal SJ. (2021) Absence of “selfish herd” dynamics in bird flocks under threat. Current Biology. <a href="https://doi.org/10.1016/j.cub.2021.05.009">https://doi.org/10.1016/j.cub.2021.05.009</a>, </em>and can be accessed at the Zenodo repository: <a href="http://doi.org/10.5281/zenodo.4993109">https://doi.org/10.5281/zenodo.4993109</a></p> <p><strong>This work has been financed to C.K.H. by the Netherlands Organisation for Scientific Research (NWO - https://www.nwo.nl), the Open Technology Programme (OTP), project: 'Preventing bird strikes: Developing RoboFalcons to deter bird flocks' (project number 14723). </strong></p>
Ambient Diffusion Checkpoints
<p>Checkpoints for AFHQ, CelebA-HQ and CIFAR-10 for ambient diffusion.</p>
Data files for 'Tan et al., (2020). Hydraulic fracturing induced seismicity in the southern Sichuan Basin due to fluid diffusion inferred from seismic and injection data analysis'
<p>CEDC catalog.xlsx : the seismic catalog from China Earthquake Data Center (https://data.earthquake.cn/)</p> <p>Local network catalog.xlsx : the seismic catalog of the local seismic network</p> <p>Injection data of N5&N7.xlsx : the injection data of N5 and N7 (with permission of the operator)</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.