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 “diffusion”
24-shell diffusion MRI data
<p>24-shell diffusion MRI used in <em>Huynh, K.M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.H., Lin, W., Shen, D. and Yap, P.T., 2020. Probing tissue microarchitecture of the baby brain via spherical mean spectrum imaging. IEEE Transactions on Medical Imaging, 39(11), pp.3607-3618.</em></p>
Diffuse Optical Tomography and Fluorescence Simulation
<p>Diffusion of a source of light (Dirac) in a turbid medium. The object owns two inclusions, one more absorbant and one more diffusive than the background. These inclusions can be seen as tumours that have different optical and fluorescence properties compared to the "homogeneous" background.</p> <p>This simulation shows the forward problem solutions for choosen optical and fluorescence parameters and computed with FEEL++, a C++ library for Generalized Garlerkin methods (FEM, HP-FEM, ...).</p>
Diffusion in Temporal Networks: London Tube passenger flows
<p>This video shows the diffusion of visitation probabilities of a random walk in a temporal network that has been constructed based on time-stamped data on passenger flows in the London Tube network.</p> <p>The left panel shows a random walk based on the empirical temporal network. This temporal network exhibits non-Markovian characteristics as shown in [1]. The right panel shows a random walk based on a a shuffled version of the same network, in which all order correlations are destroyed. This corresponds to a Markovian temporal network in which all time-respecting path statistics correspond to what is expected based on the static, time-aggregated network.</p> <p>This video is an illustrative supporting animation for the following paper:</p> <p><em>[1] Ingo Scholtes, Nicolas Wider, René Pfitzner, Antonios Garas, Claudio Juan Tessone and Frank Schweitzer: </em><strong>Causality-driven</strong><strong> slow-down and speed-up of diffusion in non-Markovian temporal networks, </strong>Nature Communications, Vol. 5, Article 5024, September 24, 2014</p>
EBEWOX - ORCA PBE/def2-SVP +diffuse Rb (RI) SP + QTAIM CPs
<p>ORCA [Neese, 2012] v3.0.3 input (.txt) and output (.out) for an UPBE [Perdew et al., 1996] computation with RI approximation and Kollmar's DIIS SCF convergence method [1996] on the EBEWOX (Cryptand(2.2.2))-rubidium electride [Cambridge Structural Database; original citation Xie et al., 2000]. Geometry (.xyz), ORCA-native wavefunction (.gbw), and ORCA's Molden wavefunction conversion (.molden) are also included.</p> <p>Primary basis set for Rb was the all-electron non-relativistic SVPall [Ahlrichs and May, 2000], with an auxiliary basis automatically generated by ORCA. The Rb basis was augmented with single additional s and p diffuse functions per the 'minimal augmentation' approach of Zheng et al. (2011). Primary and auxiliary bases for all other atoms were def2-SVP [Schäfer et al., 1992; Eichkorn et al., 1997]. (Note: the citation for SVPall is incorrect in the .txt file.)</p> <p>The image (.png) shows the system geometry with critical points and (3,-3)--(3,-1) line paths from MultiWFN v3.3.7 [Lu and Chen, 2012]. The (3,+1) and (3,+3) critical points marked with red arrows in the dataset without the Rb diffuse functions [doi:10.5281/zenodo.20639] are absent from these results.</p>
Simulation of freely-diffusing smFRET data of a static mixture of 2 populations
<p>Simulation data created with PyBroMo smFRET simulation software.</p>
Simulations of passive diffusion through the nuclear pore complex
<p>Repository for simulations of passive diffusion through the nuclear pore complex, associated with the manuscript:</p> <p>Simple rules for passive diffusion through the nuclear pore complex. Timney B<em>, </em>Raveh B, Mironska R, Trivedi JM, Kim SJ, Russel D, Wente SR, Sali A, and Rout MP Journal of Cell Biology (2016) DOI: 10.1083/jcb.201601004</p>
Level A Pan Europe Nutrients, point and diffuse sources, E-HYPE 2.5
Inputs of nitrogen and phosphorus (including fractions of the soluble P from urban point sources (PS1) , industrial point sources (PS3) and rural households (Loc). These values are calculated for each subbasin according to population in the subbasin, assumed connectivity to urban wastewater treatment or not, assumed emissions per person and assumed treatment levels (based on country). These can be downloaded as simulated load to the E-HYPE2.5 subbasins (= subbasin level, subbasin resolution = 215 km2) or as simulated load at the catchment, (subbasin and contribution from upstream area.) Original data source: HYDE population database, EEA treatment level method, EUROSTAT, HELCOM PLC5, E-PRTR. Tools for repurposing: WHIST. Data format: Table (Excel file) with unique subID which could be linked to the shapefile EHYPE_polygons_v2pt5.shp.
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding"
<h1>Info</h1> <p>This dataset contains the supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding". </p> <p>For <strong>source code </strong>and <strong>detailed instructions on usage, </strong>please refer to our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> .</p> <h1>Supplementary data</h1> <h2>weights.tar.gz</h2> <p>The trained model weights used in the paper.</p> <h2>dataset.zip</h2> <p>CATH-60 Dataset used in the paper. In the notebook directory of our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> , we provide an example on encoding and visualize it with our trained encoder.</p> <h2>design.zip</h2> <p>The 21 novel mainly-beta designs selected for experiment validation. Along with the generated backbone, we also provide the prediction results from AlphaFold and ESMFold.</p> <h2>benchmark_sample.zip</h2> <p>Sampled backbones used for all benchmark experiment (All methods and variants included).</p> <h2>evaluation.tar.gz</h2> <p>Precomputed CATH reference data for coverage metric computation. Need to be downloaded for using evaluation scripts. </p>
Synthbuster: Towards Detection of Diffusion Model Generated Images
<p>Dataset described in the paper "Synthbuster: Towards Detection of Diffusion Model Generated Images" (Quentin Bammey, 2023, <i>Open Journal of Signal Processing</i>)</p><p>This dataset contains synthetic, AI-generated images from 9 different models:</p><ul><li>DALL·E 2</li><li>DALL·E 3</li><li>Adobe Firefly</li><li>Midjourney v5</li><li>Stable Diffusion 1.3</li><li>Stable Diffusion 1.4</li><li>Stable Diffusion 2</li><li>Stable Diffusion XL</li><li>Glide</li></ul><p> </p><p>1000 images were generated per model. The images are loosely based on raise-1k images (Dang-Nguyen, Duc-Tien, et al. "Raise: A raw images dataset for digital image forensics." Proceedings of the 6th ACM multimedia systems conference. 2015.). For each image of the raise-1k dataset, a description was generated using the Midjourney /describe function and CLIP interrogator (https://github.com/pharmapsychotic/clip-interrogator/). Each of these prompts was manually edited to produce results as photorealistic as possible and remove living persons and artists names.</p><p> </p><p>In addition to this, parameters were randomly selected within reasonable values for methods requiring so.</p><p>The prompts and parameters used for each method can be found in the `prompts.csv` file.</p><p> </p><p>This dataset can be used to evaluate AI-generated image detection methods. We recommend matching the generated images with the real Raise-1k images, to evaluate whether the methods can distinguish the two of them. Raise-1k images are not included in the dataset, they can be downloaded separately at (http://loki.disi.unitn.it/RAISE/download.html).</p><p> </p><p>None of the images suffered degradations such as JPEG compression or resampling, which leaves room to add your own degradations to test robustness to various transformation in a controlled manner.</p><p> </p>
Supporting information for "Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p><p> </p><p>All data and code (notebooks) required to reproduce the analysis can be found within the "supporting_data_analysis.zip" archive. This archive contains two sub-directories:</p><ul><li>insituAnalysis<ul><li>Jupyter notebook outlining how to perform the `on-the-fly' analysis.</li><li>Data directory containing all temporally sliced neutron reflectometry data.</li></ul></li><li>MaxEnt<ul><li>Jupyter notebook outlining how to perform the maximum entropy modelling approach for polymer volume fraction profiles.</li><li>Data directory containing relevant neutron reflectometry data and PCHIP spline modelling by Gresham et al. (<a href="www.doi.org/10.1107/S160057672100251X">10.1107/S160057672100251X</a>).</li><li>Code available on the <a href="https://github.com/refnx/refnx-models/tree/master/MaxEntVFP">refnx-models GitHub repo</a>.</li></ul></li></ul><p> </p>
Aquaporins Embedded in a Cell Membrane with O2 Diffusion
<p>This is an illustration of an aquaporin embedded in a cell membrane with O2 diffusion as well. </p>
Dataset from : Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser
<p>This dataset pertains to the publication 'Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser', V. Moënne-Loccoz, I. Trébinjac, N. Poujol, P. Duquesne. International Journal of Turbomachinery, Propulsion and Power. MDPI (2020).</p><p>Where possible, each figure is provided in CSV format.</p>
Synthetic IAM with latent diffusion models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
Dataset of "Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport"
<p>This dataset underpins the following article published in the Advanced Science:</p> <p>"Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport"</p> <p> </p> <p>Raw and processed data for the article "Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport". For further details see the readme.txt file.</p>
An estimate for thermal diffusivity in highly irradiated tungsten using Molecular Dynamics simulation
<p>The changing thermal conductivity of an irradiated material is among the principal design considerations for any nuclear reactor, but at present few models are capable of predicting these changes starting from an arbitrary atomistic model. Here we present a simple model for computing the thermal diffusivity of tungsten, based on the conductivity of the perfect crystal and resistivity per Frenkel pair, and dividing a simulation into perfect and athermal regions statistically. This is applied to highly irradiated microstructures simulated with Molecular Dynamics. A comparison to experiment shows that simulations closely track observed thermal diffusivity over a range of doses from the dilute limit of a few Frenkel pairs to the high dose saturation limit at 3 displacements per atom (dpa).<br> </p>
Diffuse Supernova Neutrino Background search at Super-Kamiokande (arXiv:2109.11174, PRD 104, 122002)
<p>This is the data release for the PRD 104, 122002 article about the Diffuse Supernova Background search at Super-Kamiokande. The figures which can be reproduced with this release are listed in the README of the data_release folder.</p> <p>The spectral_analysis_npy.SKMC.tar.gz folder contains the files needed to reproduce the spectral analysis in section VII of the paper. To incorporate it to the spectral analysis code please follow the instructions given in the README of <a href="https://github.com/soso128/spectral_analysis">the spectral analysis Github folder</a>.</p>
Time dependence of advection-diffusion coupling for nanoparticle ensembles
<p>Data appearing in the figures of the article DOI:10.1103/PhysRevFluids.6.064201.</p>
Agonopterix subpropinquella Flügelspannweite über 20 mm. Im Mittelraum stehen zwei schräg übereinander liegende dunkle Punkte, gleich anschliessend folgt ein grösserer, etwas diffuser Fleck, anschliessend auf der Querader ein dunkler Punkt, der auch undeutlich sein kann. Sehr variable Art: Vor allem in Südeuropa Exemplare mit ledergelben Flügeln, schwarzen Punkten und schwarzem Thorax. in Agonopterix flurii sp. nov. aus dem Wallis, Schweiz (Lepidoptera, Depressariidae)
Agonopterix subpropinquella Flügelspannweite über 20 mm. Im Mittelraum stehen zwei schräg übereinander liegende dunkle Punkte, gleich anschliessend folgt ein grösserer, etwas diffuser Fleck, anschliessend auf der Querader ein dunkler Punkt, der auch undeutlich sein kann. Sehr variable Art: Vor allem in Südeuropa Exemplare mit ledergelben Flügeln, schwarzen Punkten und schwarzem Thorax.
Agonopterix carduella Meistens an der leicht rötlichen Grundfarbe zu erkennen. Flügelspannweite meistens unter 20 mm. Im Mittelraum stehen zwei schräg übereinander liegende dunkle Punkte, gleich anschliessend folgt ein grösserer, etwas diffuser Fleck, anschliessend auf der Querader ein dunkler Punkt, der auch sehr undeutlich sein kann. Es gibt Exemplare, die äusserlich kaum von A. subpropinquella zu unterscheiden sind in Agonopterix flurii sp. nov. aus dem Wallis, Schweiz (Lepidoptera, Depressariidae)
Agonopterix carduella Meistens an der leicht rötlichen Grundfarbe zu erkennen. Flügelspannweite meistens unter 20 mm. Im Mittelraum stehen zwei schräg übereinander liegende dunkle Punkte, gleich anschliessend folgt ein grösserer, etwas diffuser Fleck, anschliessend auf der Querader ein dunkler Punkt, der auch sehr undeutlich sein kann. Es gibt Exemplare, die äusserlich kaum von A. subpropinquella zu unterscheiden sind
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.