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2,188 results for “diffusion”
Time series of methane and carbon dioxide diffusive fluxes using an Ultraportable Greenhouse Gas Analyzer (UGGA) for Falling Creek Reservoir and Beaverdam Reservoir in southwestern Virginia, USA during 2018-2025
Diffusive fluxes of methane and carbon dioxide were measured using an Ultraportable Greenhouse Gas Analyzer (UGGA) at the surface of Falling Creek Reservoir (FCR) and Beaverdam Reservoir (BVR; Vinton, Virginia, USA). FCR and BVR are owned and operated by the Western Virginia Water Authority as drinking water sources for Roanoke, Virginia. The dataset consists of calculated diffusive fluxes of methane and carbon dioxide measured at the deepest site of the reservoir adjacent to the dam (2018–2025) and additional reservoir upstream sites in FCR (2018, 2023) and BVR (2022). In 2025, two littoral sites were measured at the northernmost wetland inflow to FCR. Measurements were collected approximately fortnightly in FCR throughout the summer stratified periods of 2018–2021 and 2023-2025, while measurements from BVR were only taken in 2018 and 2022-2024.
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
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diffusion_fMRI
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Dataset of "Photoelectrochemical generation of H2O2 using hematite (α-Fe2O3) and gas diffusion electrode (GDE)"
<p>In contrast to the industrial-scale production of H2O2 the electrochemical or photoelectrochemical synthesis is environmentally friendly. In the present work, <br>the photoelectrochemical generation of H2O2 was studied by combining the hematite (α-Fe2O3/FTO/glass) photoanode and gas diffusion electrode (GDE) modified by <br>incorporation of tin (II) phthalocyanine (SnPc) in its hydrophilic layer. The experiments were carried out in a photoelectrochemical cell with two compartments <br>separated by a proton exchange membrane under applied bias and AM1.5 irradiation (100 mW/cm2). The generated amount of H2O2 was determined by chemical analysis <br>(visible light spectrophotometry) of the electrolyte. As a tool to determine the efficiency of such a process, the Faradaic efficiency (FE) was calculated. The <br>best configuration used air as an inlet gas for GDE and phosphate buffer (pH 6.4) as an electrolyte in the cathodic compartment. The combination of hematite and <br>GDE (with SnPc) was the most effective in H2O2 photoelectrochemical generation. The highest value of FE was 52.4 % for GDE (O2 reduction to H2O2) and 0.4 % for <br>hematite photoanode (H2O oxidation to H2O2).</p>
Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output
<p>Model output used in the publication:</p> <p>M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. <em>Proc. Natl. Acad. Sci. </em>(2021) https://doi.org/10.1073/pnas.207541118</p> <p>See README.md for a description of the data.</p>
A whole-cortex probabilistic diffusion tractography connectome
<p>This is a collection of the results data for the eNeuro article of the same name, <a href="http://doi.org/10.1523/ENEURO.0416-20.2020">https://doi.org/10.1523/ENEURO.0416-20.2020</a>. Please cite this publication when using these data. Files with the .mat extension are matlab v7.3 files. The raw data from which these data were derived are available from <a href="https://db.humanconnectome.org">https://db.humanconnectome.org</a> and <a href="https://f-tract.eu">https://f-tract.eu</a>.</p>
EDEN2020 Ovine Diffusion Tensor Magnetic Resonance Tractography Atlas
<p>This dataset has been created to share the first <em>in vivo, </em>population-averaged Diffusion Tensor Magnetic Resonance Imaging (DTI) Ovine Tractography Atlas (OTA), where the course of the main white matter fiber bundles of the ovine brain has been reconstructed. The OTA has been described in the related paper ‘In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles’ by Pieri <em>et al. </em>(2019) <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a></p> <p>In the context of the EU’s Horizon EDEN2020 project, in vivo brain MRI protocol for ovine animal models was optimized on a 1.5T scanner. High resolution conventional MRI scans and DTI sequences (b-value = 1,000 s/mm<sup>2</sup>, 15 directions) were acquired on ten anesthetized sheep <em>ovis aries</em>, to define the diffusion features of normal adult ovine brain tissue. Topography of the ovine cortex was studied, and DTI maps were derived, to perform DTI deterministic tractography reconstruction of the corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP), and occipitofrontal fascicle (OF), bilaterally for all the animals. Binary masks of the tracts were then coregistered and reported in the space of a standard stereotaxic ovine reference system ('ovine_model_05.nii', Nitzsche B. <em>et al.</em>, <em>Front. Neuroanat.</em> 9:69. <a href="https://doi.org/10.3389/fnana.2015.00069">https://doi.org/10.3389/fnana.2015.00069</a>). Finally, these were combined across animals to obtain population probability masks for each tract, representing voxel- by-voxel probability of the presence of the tract in the 10 animals, thus ranged between 0 and 10. </p> <p>Please don't forget to cite this publication when using the Ovine Tractography Atlas: </p> <p>Pieri V., Trovatelli M., Cadioli M., Zani D.D., Brizzola S., Ravasio G., Acocella F., Di Giancamillo M., Malfassi L., Dolera M., Riva M., Bello L., Falini A., & Castellano A. (2019). In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles. <em>Front Vet Sci, 6</em>(345), 345 <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a> </p> <p>This work has been carried out in the context of the EDEN2020 (Enhanced Delivery Ecosystem for Neurosurgery in 2020, www.eden2020.eu) project, that received funding from the European Union’s EU Research and Innovation programme Horizon 2020 under Grant Agreement No. 688279.</p>
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
various ZIF modifications and diffusivities of various gases
<p>A dataset that includes ZIFs where their sub-units are varied, along with molecular dynamics simulation results for: the aperture size of each ZIF, the stretched aperture when a gas lies in its center, and diffusivities for each gas molecule.</p> <p>The modifications are in the form of replacement for the metal center, organic linker and the functional group. The gas molecules are: He, H2, O2, CO2, N2, CH4, C2H4, C2H6, C3H6, C3H8, n-C4H10, iso-C4H10.</p> <p>The dataset is used in Machine Learning routing, to find the structure modification-gas diffusivity correlation.</p>
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Data from: "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression"
<p>The datset contains raw data used for the work presented in the journal paper "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression".<br>Specifically, it contains machine recorded data and video recordings (either SEM or with optical microscope) of the compression tests on small scale single edge notched specimens made of IM7/8552 (carbon/epoxy) and HyBor 52 FPI (carbon-boron fibre hybrid composite). It also contains specimens pictures taken during and after the tests (including SEM and optical micrographs).</p> <p>For more details, please refer to the full paper.</p>
Navigating protected areas networks for improving diffusion of conservation practices
<p>The Natura 2000 protected area network is the cornerstone of European Union's biodiversity conservation strategy. These protected areas range across multiple biogeographic regions, and they include a diversity of species assemblages along with a diversity of managing organizations, altogether making difficult to pool relevant sites to facilitate the flow of knowledge significant to their management. Here we introduce an approach to navigating protected area networks that has the potential to foster systematic identification of key sites for facilitating the exchange of knowledge and diffusion of information within the network. To demonstrate our approach, we abstractly represented Romanian Natura 2000 network as a co-occurrence network, with individual sites as nodes and shared species as edges, further combining into our analysis network topology, community detection, and network reduction methods. We identified most representative Natura 2000 sites that may increase the transfer of information within the national network of protected areas, detected clusters of sites and key sites for maintaining network cohesiveness, and highlighted the subsample of sites that retain the characteristics of the entire network. Our analysis provides implications for protected area prioritization by proposing a network perspective approach to collaboration rooted in ecological principles.</p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Dataset: Ensemble results comparing L-dependent radial diffusion
<p>Simulation data used in the creation of plots in "Two methods to analyse radial diffusion ensembles: the peril of space- and time- dependent diffusion".</p>
Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes
<p>This entry contains the data related to the publication<br><strong>A. Szczęsna-Chrzan <em>et al.</em>, “Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes,”<em> J. Mater. Chem. A</em>, vol. 11, no. 25, pp. 13483–13492, 2023, doi: 10.1039/D3TA01217D.</strong><br><br>It contains experimentally determined conductivity, viscosity and self-diffusion coefficients of anions of the Hückel-type salts lithium 4,5-dicyano-2-(trifluoromethyl)imidazolide (LiTDI), lithium 4,5-dicyano-2-(pentafluoroethyl)imidazolide (LiPDI) and lithium 4,5-dicyano-2-(n‑heptafluoropropyl)imidazolide (LiHDI) for various concentrations of the conducting salts (0 M - 1.5 M) in a solvent mixture containing ethylene carbonate (EC) and ethyl methyl carbonate (EMC) in a ratio of 3:7 by weight.</p> <p>The Python scripts used for the analysis of the NMR data are also included in the dataset.</p>
Dataset for publication "Enhancing C≥2 product selectivity in electrochemical CO2 reduction by controlling the microstructure of gas diffusion electrodes"
<p>Data used for publication:</p> <p>Broad topic: electrochemical reduction of CO2 using gas diffusion electrodes and neutral electrolyte</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/Power Point files are all contained in the subfolders </p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas and liquid chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p>
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
<p>We demonstrate a novel realization of Interstitial fiber, broadband, Time Domain Diffuse Optical Spectroscopy (TD-DOS) in Null Source-Detector separation (NSDS) approach without temporal gating, by using a Superconducting Nanowire single photon detector (SNSPD) for acquisition. As per the MEDPHOT protocol, we test experimentally, the absorption linearity of the system on tissue-equivalent liquid phantoms, and demonstrate the scattering-independent retrieval of the absorption spectrum of water using Intralipid phantoms in the wavelength range of 600-1100 nm.</p> <p>This work has been published in the Journal of Biomedical Optics - https://doi.org/10.1117/1.JBO.28.12.121202. Here, we present the dataset containing the acquired data pertaining to the aforementioned publication, including a brief overview, the tools to read it and the analysis corresponding to the figures in the article.</p>
Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
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Taming the fixed-node error in diffusion Monte Carlo via range separation
<p>Suplementary information.</p> <p>Contains the org-mode computational notebook with all the input data (geometries, basis sets, pseudo-potentials) and output data (computed energies, densities, number of determinants) related to the article.</p> <p>A csv file is created by the notebook and an HTML export of the notebook is also provided.</p>
Dataset for publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction"
<p>Dataset for the publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction" containing war and processed data used to compose the various figures. </p> <p>DOI Publication: <a href="https://doi.org/10.1021/acsaem.2c03054">https://doi.org/10.1021/acsaem.2c03054</a> </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.