Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,194
datasets available to search
ShareScore release 0.9.0
Dataset results
2,194 results for “Diffusers”
Large Eddy Simulation data of flow in a rectangular diffuser
<p>This dataset contains the results of large-eddy simulations of the flow in a rectangular asymmetric diffuser. Here only the D2 diffuser's data are provided because experimental and DNS data are avilable for D1. .</p>
Data for diffusion coefficient and offset figures
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/75fc5afb67cd43425ac5554449aba4bddab52e84">this GitHub repo</a>.
msd-errors - Data for diffusion coefficient and offset figures3
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
msd-errors - Data for diffusion coefficient and offset figures
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
Diffusive regimes in a two-dimensional chiral fluid - Supplementary Material
<p>Supplementary datasets and additional videos. Data files are included for the experiments corresponding to Figures 1(a), 2 and 4(a), they are stored in a compressed pickle format, they can be easily read in Python with:</p> <pre><code class="language-python">df = pd.read_pickle('path_to_file', compression='xz')</code></pre> <p>The experiment ids of the experimental data files correspond to the following cases:</p> <ul> <li>155e9e3bf635544e8ff7240c760bee7b: packing fraction 0.45, mean vorticity -0.12</li> <li>f509f3b902a7ada8064a9247404d7019: packing fraction 0.45, mean vorticity 0.03</li> <li>edc6f35457554fd45382f6becdf41d97: packing fraction 0.45, mean vorticity 0.26</li> </ul>
Data sets for "Water enhancement of Si self-diffusion in wadsleyite" by D. Druzhbin et al.
<p>This is FTIR and SIMS data for the article "Water enhancement of Si self-diffusion in wadsleyite" by D. Druzhbin et al.</p>
Tracer diffusivity in an idealized MITgcm simulation
<p>Model setup files, output data and python scripts that are used for a submitted manuscript to JAMES.</p>
Theoretical investigations on shadow band correction factors for diffuse radiation under isotropic conditions without approximation dataset
<p>The database consists of: </p> <p>1. The data for the correction factors C<sub>h</sub> and C<sub>e </sub> (<strong>Data file for factors Ch and Ce</strong>);</p> <p>2. The data for figures 20, 21 and 22 (<strong>Data file for Figure 20, 21 and 22</strong>) ;</p> <p>3. The data for figure 23 (<strong>Data file for Figure 23</strong>) ;</p> <p>4. The data for figure 24 (<strong>Data file for Figure 24</strong>) ;</p> <p>5. The data for figure 25 (<strong>Data file for Figure 26</strong>) ;</p> <p>6. The data for figure 26 (<strong>Data file for Figure 26</strong>);</p> <p>7. The data for figure 27 (<strong>Data file for Figure 27</strong>) ;</p> <p>8. The data for figure 28 (<strong>Data file for Figure 28</strong>) ;</p> <p>9. The data for the correction factors C<sub>h</sub>, C<sub>e</sub> and C<sub>B</sub> (<strong>Data file of comparison with Burek</strong>) ;</p> <p>10. The data for the correction factors C<sub>h</sub>, C<sub>e</sub> and C<sub>D</sub> (<strong>Data file of comparison with Drummond</strong>);</p> <p>11. Reference 7 (<strong>Michael J. Brooks and Lance W. Roberts (2016)</strong>)</p>
Simulation results of the technology diffusion model for electrolysis capacity
<p>This upload contains the pre-run simulation output of the technology diffusion model for the article:</p> <p>Odenweller, A., Ueckerdt, F., Nemet, G. F., Jensterle, M., and Luderer, G.: "Probabilistic feasibility space of scaling up green hydrogen supply".</p> <p>You may use the files to run the model code (available on GitHub) in reproduction mode, which reproduces all figures of the article without having to run the time-consuming simulation yourself. All files are provided in the rds format.</p> <p>Contents of this upload:</p> <ul> <li>01_conventional_growth_parameters.rds - Parameters of the distributions in the conventional growth case</li> <li>02_conventional_growth_sample.rds - Sample of the distributions in the conventional growth case</li> <li>03_conventional_growth_results.rds - Simulation results of model in the conventional growth case</li> <li>04_unconventional_growth_parameters.rds - Parameters of the distributions in the unconventional growth case</li> <li>05_unconventional_growth_sample.rds - Sample of the distributions in the unconventional growth case</li> <li>06_unconventional_growth_results.rds - Simulation results of model in the unconventional growth case</li> </ul>
Dataset for Diffusion of Circumpolar Deep Water towards Antarctica
<p>Dataset used for "Diffusion of Circumpolar Deep Water Towards Antarctica" submitted to JGR-Oceans.</p>
Tracer and Observationally Derived Constraints on Diapycnal Diffusivities in an Ocean State Estimate
<p>Data used to generate figures in Trossman et al. (2022) in Ocean Science</p>
In vivo probabilistic atlas of white matter tracts of the human subthalamic area combining track density imaging and optimized diffusion tractography
<p>Sample code to reproduce the results of the paper "In vivo probabilistic atlas of white matter tracts of the human subthalamic area combining track density imaging and optimized diffusion tractography", by Basile et al., Brain Structure and Function 2022. </p> <p>The repository also includes the probabilistic atlas of the human subthalamic area.</p>
Advective-diffusive transport in fractured media
<p>Input and output files for DFN simulations in DFN.Lab</p> <p>Scritps for reading the results </p> <p>Scritps for figuures representing the theoretical behavior. </p>
Distribution. NE Egypt (coastal region of Sinai), S Israel (Negev Desert), and Palestine. Descriptive notes. Head-body 130-170 mm, tail 120-180 mm, ear 17-22 mm, hindfoot 30-41 mm; weight 125-275 g. A medium-sized jird, Buxton's Jird has tail of about same length as head-body length and partially hairy soles of hindfeet. Bicolored tail ends with well-developed pencil of black hairs. Dorsal pelage is reddish sandy, diffusely speckled with black hairs, and ventral is white. Enlarged tympanic bullae project over back of skull and represent c.35-37% ofskull length. Karyotype 2n = 46. in Muridae
Distribution. NE Egypt (coastal region of Sinai), S Israel (Negev Desert), and Palestine. Descriptive notes. Head-body 130-170 mm, tail 120-180 mm, ear 17-22 mm, hindfoot 30-41 mm; weight 125-275 g. A medium-sized jird, Buxton's Jird has tail of about same length as head-body length and partially hairy soles of hindfeet. Bicolored tail ends with well-developed pencil of black hairs. Dorsal pelage is reddish sandy, diffusely speckled with black hairs, and ventral is white. Enlarged tympanic bullae project over back of skull and represent c.35-37% ofskull length. Karyotype 2n = 46.
Data for "Transparent Josephson Junctions in Higher-Order Topological Insulator WTe2 via Pd Diffusion"
<p>Data for the publication "Transparent Josephson Junctions in Higher-Order Topological Insulator WTe<sub>2</sub> via Pd Diffusion".</p> <p>Updated version as accepted by Physical Review Materials. </p>
Data for: A comparative study on heterogeneity of clay rocks using pore-scale diffusion simulations and experiments
<p>This is the dataset for the paper "A comparative study on heterogeneity of clay rocks using pore-scale diffusion simulations and experiments" in JGR: Solid Earth</p>
Preliminary experiment for inject-and-diffuse approach in BITS
<p>Preliminary experiment for inject-and-diffuse approach in BITS</p> <p>Data collection: Dec 23, 2021</p> <p>NCI experimental hutch at PAL-XFEL</p> <p>Dataset: Filtered images by Cheetah program.</p>
Li diffusion modelling
<p>Data from Li diffusion modelling with QENS using different Li diffusion coeff sources</p>
Single-molecule tracking of Nodal and Lefty in live zebrafish embryos supports hindered diffusion model
<p><span>The hindered diffusion model postulates that the movement of a signaling molecule through an embryo is affected by tissue geometry and binding-mediated hindrance, but these effects have not been directly demonstrated <em>in vivo</em>. Here, we visualize extracellular movement and binding of individual molecules of the activator-inhibitor signaling pair Nodal and Lefty in live developing zebrafish embryos using reflected light-sheet microscopy. We observe that diffusion coefficients of molecules are high in extracellular cavities, whereas mobility is reduced and bound fractions are high within cell-cell interfaces. Counterintuitively, molecules nevertheless accumulate in cavities, which we attribute to the geometry of the extracellular space by agent-based simulations. We further find that Nodal has a larger bound fraction than Lefty and shows a binding time of tens of seconds. Together, our measurements and simulations provide direct support for the hindered diffusion model and yield insights into the nanometer-to-micrometer-scale mechanisms that lead to macroscopic signal dispersal.</span></p>
Pore network modeling as a new tool for determining gas diffusivity in peat
<p>The data and scripts are related to the manuscript “Pore network modeling as a new tool for determining gas diffusivity in peat” by Petri Kiuru, Marjo Palviainen, Arianna Marchionne, Tiia Grönholm, Maarit Raivonen, Lukas Kohl, and Annamari (Ari) Laurén submitted to Biogeosciences. The folder structure is similar to the one in the package “Peat macropore networks – new insights into episodic and hotspot methane emission” <a href="https://doi.org/10.5281/zenodo.6327112">https://doi.org/10.5281/zenodo.6327112</a>, and the remaining required data files can be found there.</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.