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453 results for “Dynamic dataset”
Dataset of paper: Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation (PLOS One)
<p>The data set contains number of user, user's physiological signals (Pulse, SCL, SCR, Respiration rate, Skin temperature), Label, Difficulty level from relax to stress. Label is codified from 1 to 5 corresponding to the Difficulty level.</p>
NDVI datasets related with the article "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance"
<p>AHVRR GIMMS and MODIS NDVI input datasets and main results on seasonal and long-term NDVI dynamics related with the manuscript "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance" recently published in Remote Sensing 8(2), 159, doi: 10.3390/rs8020159.</p>
Datasets for the article "Emerging patterns of CO2:O2 dynamics in rivers and their link to ecosystem carbon processing "
<p>Datasets supporting the article "Emerging patterns of CO2:O2 dynamics in rivers and their link to ecosystem carbon processing". The datasets are analized using the acompanying repository in github (https://github.com/rocher-ros/O2_CO2_rivers).</p> <p> </p> <p>Currently the publication is under review, for further information and details on the analysis visit the github repository or the acompannying article. </p>
Datasets, models and demos associated to "Celldetective: an AI-enhanced image analysis tool for unraveling dynamic cell interactions"
<p>This repository contains datasets, models and demos associated to <a href="https://github.com/remyeltorro/celldetective">Celldetective</a>, a software for single-cell analysis from multimodal time lapse microscopy images. </p> <h1>Demos</h1> <h2>Cell-cell interaction assay: ADCC</h2> <p>We imaged a co-culture of MCF-7 breast cancer cells (targets) and human primary NK cells (effectors), interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). The nuclei of all cells are marked with the Hoechst nuclear stain, the dead nuclei with the propidium iodide nuclear stain, the cytoplasm of the NK cells with CFSE. The system in epifluorescence and brightfield at either 20 or 40X magnification. We provide a single position demo for the ADCC assay, as "demo_adcc.zip". After unzipping, the demo_adcc folder can be loaded in Celldetective for testing. </p> <h2>Cell-surface interaction assay: RICM</h2> <p>We imaged human primary NK cells engaging in spreading with a surface coated with a bispecific antibody similar to the one used in the ADCC assay (replacing the target cells with a flat surface). The system is imaged using the RICM technique. Images are normalized using a median estimate of the background, pooled from all the positions in a well and dividing the images by this estimate. Here, we provide a single position demo for the cell-surface interactiona assay imaged in RICM, as "demo_ricm.zip". As above, after unzipping, the experiment can be tested and processed in Celldetective.</p> <h1>Datasets</h1> <h2>Image annotations for segmentation</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated two sets of annotations from images of a co-culture of MCF-7 breast cancer cells and human primary NK cells, interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). Since there are two separate cell populations of interest, the targets (MCF-7) and effectors (NK cells), we curated two datasets. Each sample in a dataset consists of a multichannel image (up to five channels in the context of ADCC, among brightfield , Hoechst nuclear stain, PI nuclear stain, CFSE, LAMP1), the associated instance segmentation annotation for the population of interest and a json file summarizing the content of each channel and the spatial calibration of the image. These sample data are generated directly in Celldetective, using a custom napari plugin.</p> <ul> <li>db_mcf7_nuclei_w_lymphocytes: MCF-7 cell nuclei are annotated specifically on images where primary NK cells (or rarely primary T cells), and RBCs co-exist. The annotation exploits up to four channels simultaneously.</li> <li>db_primary_NK_w_mcf7: human primary NK cells, with annotated cytoplasm (mostly from CFSE) but exploiting brightfield and Hoechst to segment out of focus or poorly labelled cells.</li> </ul> <p>These datasets are used to train several segmentation models to segment on one hand the MCF-7 nuclei and on the other hand the primary NK cells.</p> <h3>Cell-surface interaction assay: RICM</h3> <ul> <li>db_spreading_lymphocytes: we provide a dataset of primary NK cells (and occasionnaly mice T cells) imaged in RICM (with sometimes paired brightfield images). Cells are detected as soon as they start forming interferences on the image (hovering behavior). A pre-annotation was performed using a threshold based segmentation on the RICM modality. Manuel separation of cell-cell contacts and removal of false positive objects was performed by an expert annotator (using brightfield when available). RBCs are ignored in the annotations. </li> </ul> <h2>Single-cell signal annotations for classification and regression</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated several signal classification/regression datasets with Celldetective to characterize the ADCC assay. Briefly, for a given event cells can be classified as "the event occured during the observation", "no event occured during the observation", "the event already occured prior to observation". If the event occurred during the observation, we can estimate when (the regression). Each single-cell is a dictionary with a collection of signals. The attribute "class" sets the class and "t0" the time of event (default is -1 for absence of event). </p> <ul> <li>db-si-NucPI: classification and regression of single-cells with respect to lysis events characterized by a strong PI increase upon lysis (also associated with decreasing nuclear area and sometimes a decreasing Hoechst)</li> <li>db-si-NucCondensation: classification and regression of single-cells with respect to nucleus shrinking events characterized by a decreasing nuclear area (UPDATE on 23/01/2024)</li> </ul> <h1>Models</h1> <h2>Segmentation models</h2> <h3>Generalist models</h3> <p>We integrated in Celldetective select published models for cellular segmentation from StarDist and Cellpose. We wraped the models with an input configuration to help Celldetective handle the normalization, rescaling and channel selection upon inference. </p> <ul> <li>Cellpose [1,2]: <em>cyto3</em>, <em>livecell</em>, <em>tissuenet</em>, <em>nuclei</em></li> <li>StarDist [3]: <em>versatile_fluo</em>, <em>versatile_he</em></li> </ul> <p>If you use any of these models your research, don't forget to cite the StarDist or Cellpose papers accordingly!</p> <h3>ADCC models</h3> <ul> <li>MCF-7 (in the presence of lymphocytes): <em>mcf7_nuc_multimodal, mcf7_nuc_stardist_transfer</em></li> <li>primary NKs (in the presence of MCF-7): <em>primNK_multimodal</em>, <em>primNK_SD</em>, <em>primNK_cfse</em></li> </ul> <h3>Spreading-assay models</h3> <ul> <li>Lymphocytes: <em>lymphocytes_ricm</em></li> </ul> <h2>Signal analysis models</h2> <p>We developed Deep Learning models that classify and regress the time of events from single-cell signals, applied to the ADCC assay.</p> <ul> <li> lysis detection: <em>lysis_H_PI</em>, <em>lysis_PI_area</em><em>. </em>Detect lysis events characterized at least by an increase of PI from one or more measurements (respectively PI+Hoechst and PI+nucleus area, trained on db-si-NucPI)</li> <li>nucleus shrinking detection:<em> NucCond</em>. Detect nucleus shrinking events from nuclear area signal (db-si-NucCondensation)</li> </ul> <h1>References</h1> <ol> <li>Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021).</li> <li>Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022).</li> <li>Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30.</li> </ol> <p> </p> <p> </p>
Dataset for Article: Potential of dynamic wind farm control by axial induction in the case of wind gusts
<p>This is the dataset for the article "Potential of dynamic wind farm control by axial induction in the case of wind gusts". We provide the data as OUT-files from FAST.Farm simulation. Submission of revised manuscript: November 2023.</p>
Data from: Temporal dynamics in psychological assessments: a novel dataset with scales and response times
<p>This dataset is collected from February 27 to March 17, 2021, it includes responses from 24,292 students to four recognized psychological scales: PHQ-9, GAD-7, ISI, and PSS. A unique aspect of this dataset is the inclusion of response time data, which reflects the duration students took to answer each question. This temporal information offers a new dimension in understanding respondent behavior and enhances the reliability of these scales.</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer
<p><span>Non-small cell lung cancer (NSCLC) is a type of lung cancer associated with translocation of the EML4 and ALK genes on the short arm of chromosome 2. This leads to the development of an aberrant protein kinase with a deregulated catalytic domain, the cdALK<sup>+</sup>. Currently, different ALK inhibitors (iALKs) have been proposed to treat ALK<sup>+ </sup>NSCLC patients.</span> <span>However, the recent resistance to iALKs stimulates the exploration of new iALKs for NSCLC. Here, we describe an <em>in silico</em> approach to finding FDA-approved drugs that can be used by pharmacological repositioning as iALK. We used homology modelling to obtain a structural model of cdALK<sup>+</sup> protein and then performed molecular docking and molecular dynamics of the complex cdALK<sup>+</sup>-iALKs to generate the pharmacophore model. The pharmacophore was used to identify potential iALKs from FDA-approved drugs library by ligand-based virtual screening. Four pharmacophores with different atomistic characteristics were generated, resulting in six drugs that satisfied the proposed atomistic positions</span> <span>and coupled at the ATP-binding site. Mitoxantrone, riboflavin and abacavir exhibit the best interaction energies with 228.29, 165.40 and 133.48 kjoul/mol respectively. In addition, the special literature proposed these drugs for other types of diseases due to pharmacological repositioning. This study proposes FDA-approved drugs with ALK inhibitory characteristics. Moreover, we identified pharmacophores sites that can be tested with other pharmacological libraries</span><span>.</span></p>
Dataset from the project entitled HimFunDiff. Related to research article: Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity.
<p>We examined a total of 9 populations of Rhododendron anthopogon, which were located between 3200 m and 4200 m above sea level (asl). These populations were distributed across three geographically distant transects, with each transect consisting of three sites (along an elevation gradient). The transects are referred to as northern, intermediate, and southern, while the sites at each transect are categorized as low, mid, and high (as depicted in Thakur et al 2024). The northern transect exhibited colder temperatures and lower rainfall compared to the other two transects. On the other hand, the two remaining transects had relatively similar temperatures, but the southernmost transect received higher levels of precipitation. The mean annual temperature of these populations ranged from 2 °C to 5 °C from 2021 through 2022, while volumetric soil moisture levels varied from 0.198 to 0.377 based on onsite measurements using TMS4 dataloggers (Wild et al., 2019).</p> <p>We collected a total of 81 wood disc samples, with 9 samples obtained from each of the 9 sites studied (9 populations × 9 discs). The samples were collected by cutting a single piece from the thickest stem segment, approximately 5 cm in length, from 81 different mature and healthy individuals. Within each site, the 9 samples were obtained from three separate plots (three samples per plot), each covering an area of approximately 100 m2. The selection criteria for these plots included: (1) the presence of Rhododendron anthopogon as one of the dominant species; (2) minimal anthropogenic disturbance; and (3) the absence of large shrubs or trees. The sampled individuals within a plot were spaced at least 5 m apart from each other, and the plots themselves were at least 20 m apart. To prevent rapid drying, the cut stem samples were immediately placed in a wet paper towel. Within 48 hours of sampling, the stem samples underwent dehydration by being immersed in 50 % ethanol for the initial 3 days, followed by 70 % ethanol for the subsequent 7 to 10 days. After the ethanol dehydration process, the samples were air-dried for 72 hours and then stored in paper bags until further processing.</p> <p>Plant age and growth data for each of the sampled individuals were obtained following established protocols (Doležal et al., 2018). In the laboratory, we utilized a sledge microtome to cut cross-sections from each stem sample. These cross-sections were then stained with Astra Blue and Safranin and permanently affixed to microscope slides using Canada Balsam (Doležal et al., 2022). High-resolution images of the fixed sections were captured using an Olympus BX53 microscope equipped with an Olympus DP73 camera. The software CellSense Entry 1.9 was employed to analyse the best image obtained from each individual. We measured annual radial growth increments from pith to bark to the nearest micrometre. </p> <p>More details are given in the article entitled </p> <h1>Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.170252" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.170252</a></h1>
Dataset: Intercavity polariton slows down dynamics in strongly coupled cavities
<p>Dataset for the experimental data of article: Intercavity polariton slows down dynamics in strongly coupled cavities </p>
Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf. </p>
Data from: Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset
<p>Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near-term bird migration forecasts can inform such decisions. Although near-term forecasts are usually created with long-term datasets, the pace of environmental alteration due to wind energy calls for urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use five years of tracking bird radar data collected off the western Dutch coast, weather, and phenological variables to develop seasonal near-term forecasts of low-altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short-term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near-term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 hours in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the 'international first' offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data-driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.</p>
ToneTwist AFx Dataset: Custom Dynamic Fuzz
<div> <div> <h3><strong>Settings</strong></h3> <table> <tbody> <tr> <td>Gain</td> <td>Sensitivity</td> <td>Attack</td> <td>Release</td> <td>Volume</td> </tr> <tr> <td>5</td> <td>10</td> <td>1ms</td> <td>2500ms</td> <td>10</td> </tr> </tbody> </table> <h3><strong>Dry with markers</strong></h3> </div> </div> <p>Dry inputs are a selection of clean guitar and bass recordings from different sources:</p> <ul> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html" target="_blank" rel="noopener">IDMT-SMT-GUITAR</a> - dataset 2 (7:23 min)</li> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html" target="_blank" rel="noopener">IDMT-SMT-GUITAR</a> - dataset 4 - Career SG (6:08 min)</li> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html" target="_blank" rel="noopener">IDMT-SMT-GUITAR</a> - dataset 4 - Ibanez 2820 (5:14 min)</li> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/bass_lines.html" target="_blank" rel="noopener">IDMT-SMT-Bass-Single-Track</a> - (5:58 min)</li> <li><a href="https://github.com/sdatkinson/neural-amp-modeler?tab=readme-ov-file#download-audio-files" target="_blank" rel="noopener">NAM: Neural Amp Modeler</a> - (3:11 min)</li> <li>Private Guitar Data - (5:19 min)</li> <li>YouTube Bass Recordings - (10:09 min)</li> </ul> <p>Pre-processing:</p> <ul> <li>All: <ul> <li>synchronization markers (2 impulses) added at start and end of every file</li> </ul> </li> <li>IDMT-SMT-GUITAR - dataset 2:<br> <ul> <li>peak normalized to -6dBFS</li> </ul> </li> <li>NAM:<br> <ul> <li>no pre-processing</li> </ul> </li> <li>Others:<br> <ul> <li>peak normalized to -0.1dBFS</li> <li>signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])</li> </ul> </li> </ul> <div> <h3><strong>Authors</strong></h3> <p><a href="https://mcomunita.github.io/" target="_blank" rel="noopener">Marco Comunità</a> - <a href="http://c4dm.eecs.qmul.ac.uk/" target="_blank" rel="noopener">Centre for Digital Music</a>, Queen Mary University of London</p> <h3><strong>Github</strong></h3> <p><a href="https://github.com/mcomunita/tonetwist-afx-dataset" target="_blank" rel="noopener">https://github.com/mcomunita/tonetwist-afx-dataset</a></p> <h3><strong>Reference</strong></h3> <p>If you make use of this dataset, please cite the following publications:</p> <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre> <pre> </pre> <pre><code>@misc{comunità2025nablafxframeworkdifferentiableblackbox, title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss}, year={2025}, eprint={2502.11668}, archivePrefix={arXiv}, primaryClass={cs.SD}, url={https://arxiv.org/abs/2502.11668}, }</code></pre> </div>
Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>. </p>
DArTseq genetic dataset associated with the article "Hybrids as mirrors of the past: genomic footprints reveal spatio-temporal dynamics and extinction risk of alpine extremophytes in the mountains of Central Asia"
<p>Description: This file stores genetic information on the single nucleotide polymorphism markers (SNPs) in the examined alkali grasses (Poaceae: Puccinellia). The dataset was generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism. The file contains raw data.<br> <br>Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle this file. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse this data file as an object of a class genlight (dartR) in the R environment. You may also handle the file as an object of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.<br> </p>
Design and assembly of core/shell nanostructures as investigated by microfluidics and molecular dynamics simulation_dataset_DLS_TEM_MD
<p><span>He we like to publish data related to modified and non-modified MSN cores analysed using microfluidics platform against acetalated dextran (AcDEX)/spermine modified acetalated dextran (SpAcDEX) polymers. </span></p> <p><span>The data contains Dynamic light scattering (DLS) and TEM images which help us to to the demarcation of combinations which formed successful core/shell particles along with, <em>in-silico</em> modelling and molecular dynamics (MD) simulations data showing molecular interactions between the core particles and <a>the encapsulant </a></span><span><span></span></span><span>polymer. </span></p>
Dynamical ising dataset for the paper Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
<p>This dataset provide the evolution in time for the magnetizaion in the 2D Ising model evolved with Gluber dynamics for a lattice of size 64 x 64.</p>
3D dataset of root soil bacteria dynamics obtained using large field of view light sheet microscope
<p>A tailor made dual-illumination light-sheet system acquired photons scattered from the plant whilst fluorescence emissions were simultaneously captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm<sup>3</sup> in size with as good as 5 µm resolution at a rate of up to one scan every 30 minutes. The dataset shows the dynamics of Bacillus subtilis populations in the rhizosphere of lettuce plants in real time.</p> <p> </p> <p> </p>
Dataset and simulation files on Quantifying Wetting Dynamics with Triboelectrification
<p>The uploaded data contains software and results about the simulation of Triboelectrification and Wetting Dynamics of Multiscale Polymer Surfaces.</p> <p>Folders structure:<br> A) Folder name: NanopillarTextureDropletSim<br> 1) Wolfram Mathematica Notebook file "laplace.solvers.random.nb";<br> Code for the simulation of droplet wetting on a nanopillar forest (nanopillar surface imported from input file). It consists of three sections: one for FEM simulation on deterministic forest arrangement (section fem), one for analytical modelling of droplet wetting on random forest (section fem random measured - analytical), and the final section with the FEM simulation on the random forest (fem random generated - FEM). The different sections return embedded results (graphs or numerical) and results saved to file.<br> 2) Input file of (single realization of) nanopillar forest "pillars.random.xls";<br> Single realization of nanopillar forest, xls file. Measured from optical acquisition. Needed as input for the "laplace.solvers.random.nb".<br> B) Folder name: MicrocubeTextureDropletSim<br> 1) Wolfram Mathematica Notebook file "MicroTextureGenerator.nb";<br> Code for generation of the random microcubes texture. The different sections return embedded results (graphs or numerical) and results saved to file, such as the file "functionpython.py" used as input surface for FEniCS wetting mchanics simulation.<br> 2) FEniCS input file "contact.angle.3D_mesh.refinement.v3.2018.py";<br> Python code to run simulation of wetting dynamics with FEniCS FEM solver. The code consists of a main computation loop and several subroutines, each dedicated to perform specific computation step (such as adaptive mesh refinement nearby triple lines). The most important numerical parameters are N (e.g. with assignment N = 128) controlling the initial side discretization number of the computation square, and max_number_of_refinements (e.g with assignment max_number_of_refinements = 4) which controls the number of consecutive refinements of the solution near the triple lines. The code will save several *.pvd files containing the deformed droplet surface (top surface) and corresponding bottom surface. The code will also save the file "wet_area.dat", which contains (in each line) the (dimensionless) applied squeezing pressure, the normalized wetting area and the droplet free-surface normalized area.<br> 3) Wolfram Mathematica Notebook file "contact.angle.micro.one.plate.nb";<br> The file is used to simulate the whole droplet contact on single plate, under constant droplet volume. The different sections return embedded results (graphs or numerical) and results saved to file. As input, among the other physical parameters, the effective contact angle is needed.<br> 4) Wolfram Mathematica Notebook file "contact.angle.micro.nb";<br> The code consists of two sections, the first for droplet on single plate and the second for the droplet among two plates. In particular, the second section allows to determine the penetration vs squeezing load for the two-plates case. As input, among the other physical parameters, the effective contact angles are needed.<br> C) Folder name: Sloshing<br> 1) Wolfram Mathematica Notebook file "cylinder.sloshing.nb";<br> It contains code for the simulation of sloshing dynamics in a partially-filled tank, as well as extra analysis on the sloshing dynamics.<br> D) Folder name: Example.FEniCS.results<br> It contains a full set of FEniCS simulation results (at three simulated squeezing pressures) for the input python code "temp.contact.angle.3D_mesh.refinement.v3.2018.py".</p>
Molecular dynamics of solids at constant pressure and stress using anisotropic stochastic cell rescaling - dataset
<p>Supporting data related to manuscript "Molecular dynamics at constant pressure and stress using anisotropic stochastic cell rescaling"</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.