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982 results for “interface”
Data for "The hydrodynamic response of the sediment-water interface to coherent turbulent motions"
<p>The data provided can be used to reproduce the figures presented in the manuscript titled "The hydrodynamic response of the sediment-water interface to coherent turbulent motions" submitted to Geophysical Research Letters.</p>
Research raw data supporting "Activatable cell-biomaterial interfacing with photo-caged peptides"
<p>Raw data supporting the publication;</p> <p>Lin Y. et al., Activatable cell-biomaterial interfacing with photo-caged peptides, 2018, Chemical Science, DOI: 10.1039/c8sc04725a.</p>
SwarmTouch: Guiding a Swarm of Micro-Quadrotors with Impedance Control using a Wearable Tactile Interface
<p>The dataset "Flight_Experiments_Data.zip" represents flight experiments information of the SwarmTouch project. During the experiment, involved participants were given a task to navigate a formation of drones through the obstacles maze relying only on tactile or visual feedback. For the experiment, users overcame two different unknown setups of obstacles, each setup firstly with tactile and then with vision feedback (two trials with tactile and two with visual (without glove) feedback in total).</p> <p>The dataset "Patterns_Recognition_Data.zip " represents tactile patterns recognition experimental results. The tactile patterns were developed to represent the static and dynamic parameters of the drone swarm. This information is sent further to a human operator guiding a formation of drones with the help of tactile glove. During the experiment, each pattern was repeated once, and the subject was asked to enter the number of experienced stimuli. Each of the subjects experienced 64 stimuli (8 patterns were repeated 8 times in random order). The time of user response was also recorded.</p>
Brain Invaders Solo versus Collaboration: Multi-User P300-based Brain-Computer Interface Dataset (bi2014b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 38 subjects playing in pair to the multi-user version of a visual P300-based Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders </em>(Congedo et al., 2011). The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit a P300 response, an evoked-potential appearing about 300ms after stimulation onset. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during three randomized conditions (Solo1, Solo2, Collaboration). The experiment took place at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA</a>. The ID of this dataset is <em>bi2014b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2014b</em></p>
Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (bi2015b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 44 subjects playing in pair to the multi-user version of a visual P300 Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 or 2 Target, 35 or 34 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during four randomised conditions (Cooperation 1-Target, Cooperation 2-Targets, Competition 1-Target, Competition 2-Targets). The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA</a>. The ID of this dataset is <em>bi2015b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015b</em></p>
Robot trajectory data for "Biohybrid Fly-Robot Interface system performs active collision avoidance"
<p>This dataset includes the videos of the trajectories of the biohybrid robot (Fly-Robot Interface), performing collision avoidance at the patterned wall corners (90 and 60 degrees). A python script for the manual tracking is also attached, as well as the processed coordinates of each individual robot trajectory, where two tracking markers were chosen on the front-left and front-right corners of the robot.</p> <p>Serial Number <20: the videos at 90-degree wall corner</p> <p>Serial Number >20: the videos at 60-degree wall corner</p>
Wi-Fi Fingerprinting dataset with multiple simultaneous interfaces
<p> This dataset was collected within the context of a research project aiming to develop an indoor positioning system for autonomous industrial vehicles. This positioning system is based on fusing data from Wi-Fi sensors, magnetic encoders, and an IMU, using a particle filter. Wi-Fi data is first processed using Wi-Fi fingerprinting.</p> <p> One of the unique characteristics of this system is that it uses Wi-Fi fingerprints collected simultaneously from multiple, synchronised, Wi-Fi interfaces. Some results about the benefits of using multiple Wi-Fi interfaces are described in:</p> <p> Moreira, A., Silva, I., Meneses, F., Nicolau, M. J., Pendao, C., & Torres-Sospedra, J. (2017, September). Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning. In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8). IEEE. http://dx.doi.org/10.1109/IPIN.2017.8115914</p> <p> These data were collected at a university building that resembles an industrial floor plant, with a total area of around 1000 m2. The data were collected in July 2017.</p> <p> The data collection setup was based on a Raspberry Pi 3 Model B with its internal Wi-Fi interface, and four additional USB Wi-Fi interfaces (Edimax EW-7811un).</p> <p> </p>
Supporting data: "How collective asperity detachments nucleate slip at frictional interfaces"
<p>This repository supports:</p> <p><strong>T.W.J. de Geus, M. Popović, W. Ji, A, Rosso, M. Wyart. How collective asperity detachments nucleate slip at frictional interfaces. Proc. Natl. Acad. Sci. U.S.A. 2019. <a href="https://dx.doi.org/10.1073/pnas.1906551116">doi: 10.1073/pnas.1906551116</a>, <a href="http://arxiv.org/abs/1904.07635">arXiv: 1904.07635</a></strong></p> <p>In particular, it provides all used data, all codes used to produce this data (including clones to all the used open-source libraries), and simple functions to plot the data. All data and code is free to use under the CC-BY-4 license, but: <em>Please cite the above research article</em> when using code or data (inspired) from this repository (or the open-source projects <a href="https://www.github.com/tdegeus/GooseFEM">GooseFEM</a> and <a href="https://www.github.com/tdegeus/GMatElastoPlasticQPot">GMatElastoPlasticQPot</a>), in addition to this dataset (<a href="https://dx.doi.org/10.5281/zenodo.3477938">doi: 10.5281/zenodo.3477938</a>).</p> <p>(c) T.W.J. de Geus | 2019 | contact: <a href="/Volumes/data/dataset/Geus_PNAS/tom%40gems.me">tom@geus.me</a>, <a href="http://www.geus.me">www.geus.me</a></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contents</strong></p> <ol> <li>In brief</li> <li>Data files</li> <li>Code</li> <li>Plots</li> </ol> <p><strong>1. In brief</strong></p> <p>All codes (<code>codes/</code>) are written in C++ using a number of open-source libraries (<code>libraries/</code>). All data (<code>data/</code>) is stored in the HDF5 format. All plots (<code>data/</code>) are generated using Python and a number of open-source libraries.</p> <p>All codes are developed and tested on macOS and Linux. The notation used here is consistent with these Unix-based platforms. Windows based compilation and use might differ from the description here.</p> <p><strong>2. Data files ("data/")</strong></p> <p>The different ensembles (datasets) are included in different directories in <code>data/</code>. They are distinguished through their directory name that comprises the system size (denoted <code>nx=...</code>) and the shape factor of the Weibull distribution from which the yield strains are drawn (denoted <code>weibull=...</code>).</p> <p>Each ensemble consists of a number of realisations of the random yield strains at the frictional interface. Each realisation is stored in a separate file (<code>id=xxx.hdf5</code>). This file serves as input for the event-driven code (<code>code/Run/main.cpp</code>). This code stores the displacement field at the end of each event-driven step (for which it may take significant time for energy to be minimised). With these displacement fields, all other quantities (stress, strain, plastic strain, ...) can be reconstructed. The relevant reconstructed data for the entire ensemble is collected in <code>EnsembleInfo.hdf5</code>.</p> <p>For the manually triggered avalanches at different stresses (and fixed relative strain increment w.r.t. the last system spanning event) only selected output is stored to limit storage usage (<code>code/AvalancheAfterPush...</code>). Please note that the simulations are stopped when an event becomes system spanning to save on computation time, for this case the output thus does not correspond to a state of mechanical equilibrium. By contrast, any simulation that was not system-spanning does correspond to a state of mechanical equilibrium.</p> <p><strong>2a. Realisation ("data/.../id=xxx.hdf5")</strong></p> <p>See code <code>code/Run/main.cpp</code> and generation <code>code/Generate/generate.py</code></p> <ul> <li>Mesh (input)<br> <ul> <li><code>/coor</code>: Nodal coordinates <code>[nnode, ndim]</code> (<code>ndim == 2</code>)</li> <li><code>/conn</code>: Connectivity <code>[nelem, nne]</code> (<code>nne = 4</code>)</li> <li><code>/dofs</code>: Degrees-of-freedom (DOF) per node <code>[nnode, ndim]</code></li> <li><code>/iip</code>: Prescribed DOFs <code>[n_iip]</code><br> </li> </ul> </li> <li>Material model (input)<br> <ul> <li><code>/elastic/elem</code>: Elastic elements <code>[n_elasic]</code></li> <li><code>/elastic/G</code>: Shear modulus <code>[n_elasic]</code></li> <li><code>/elastic/K</code>: Bulk modulus <code>[n_elasic]</code></li> <li><code>/cusp/elem</code>: Elasto-plastic elements <code>[n_cusp]</code></li> <li><code>/cusp/G</code>: Shear modulus <code>[n_cusp]</code></li> <li><code>/cusp/K</code>: Bulk modulus <code>[n_cusp]</code></li> <li><code>/cusp/epsy</code>: Yield strains <code>[n_cusp, n_potentials]</code></li> <li><code>/uuid</code>: Unique identifier for the realisation<br> <br> Note that <code>n_elasic + n_cusp == nelem</code><br> </li> </ul> </li> <li>Simulation (input)<br> <ul> <li><code>/alpha</code>: Background damping coefficient <code>[nelem]</code> (homogeneous)</li> <li><code>/rho</code>: Mass density <code>[nelem]</code> (homogeneous)</li> <li><code>/run/dt</code>: Time-step</li> <li><code>/run/epsd/kick</code>: Size of the strain kick</li> <li><code>/run/epsd/max</code>: Local strain at which to stop<br> </li> </ul> </li> <li>Output<br> <ul> <li><code>/completed</code>: Completion signal, emitted when <code>/run/epsd/max</code> was reached locally</li> <li><code>/stored</code>: Stored event-driven step numbers <code>[n_event]</code></li> <li><code>/t</code>: Time at the end of each event-driven step <code>[n_event]</code></li> <li><code>/kick</code>: Strain kick (yes/no) per event-driven step <code>[n_event]</code></li> <li><code>/disp/...</code>: Nodal displacements per event-driven step <code>[nnode, ndim]</code></li> </ul> </li> </ul> <p><strong>2b. Simulation output ("data/.../EnsembleInfo.hdf5")</strong></p> <p>See code and help <code>code/EnsembleInfo/main.cpp</code>.</p> <p><strong>2c. Distribution P(x) ("data/.../EnsembleYieldDistance*.hdf5")</strong></p> <p>See code and help <code>code/EnsembleYieldDistance_stressControl/main.cpp</code> and <code>EnsembleYieldDistance_strainControl/main.cpp</code>.</p> <p><strong>2d. Manual triggering of events ("data/.../AvalancheAfterPush*.hdf5")</strong></p> <p>See code and help <code>code/AvalancheAfterPush_stressControl/main.cpp</code> and <code>code/AvalancheAfterPush_strainControl/main.cpp</code>.</p> <p><strong>3. Code ("code/")</strong></p> <p>The relevant codes to generate the datasets are referenced above. All non-standard libraries have been cloned under <code>libraries/</code>. Please note that they are subject to evolution: their cloned versions allow one to rerun the code in this dataset, however, for further development one is strongly encouraged to use the latest version. Please check out the development of:</p> <ul> <li><a href="https://github.com/tdegeus/GooseFEM.git">GooseFEM (v0.2.3)</a></li> <li><a href="https://github.com/tdegeus/GMatElastoPlasticQPot.git">GMatElastoPlasticQPot (v0.2.1)</a></li> <li><a href="https://github.com/tdegeus/cpppath.git">cpppath (v0.0.7)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor.git">xtensor (v0.20.8)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor-blas.git">xtensor-blas (v0.16.1)</a></li> <li><a href="https://github.com/xtensor-stack/xtl.git">xtl (v0.6.5)</a></li> <li><a href="https://github.com/xtensor-stack/xsimd.git">xsimd (v7.2.5)</a></li> <li><a href="https://github.com/BlueBrain/HighFive.git">highfive (master)</a></li> <li><a href="https://github.com/docopt/docopt.git">docopt (master)</a></li> <li><a href="https://github.com/fmtlib/fmt.git">fmt (master)</a></li> <li><a href="https://github.com/tdegeus/pyxtensor.git">pyxtensor (v0.0.5)</a></li> <li><a href="https://github.com/tdegeus/GooseMPL.git">GooseMPL (v0.2.24)</a></li> <li><a href="https://github.com/tdegeus/GooseEYE.git">GooseEYE (v0.2.0)</a></li> <li><a href="https://github.com/h5py/h5py.git">h5py (master)</a></li> </ul> <p>To compile code, follow the following structure:</p> <pre>cd code/... mkdir build cmake .. make</pre> <p>Then to run use:</p> <pre>./Run ...</pre> <p>(use <code>./Run --help</code> for help, and/or read the code). For some codes a support function generates commands. They can be generated and run as follows:</p> <pre>python makeJob.py source commands.txt</pre> <p><strong>4. Plots ("data/.../*.py")</strong></p> <p>Basic plot functions are included with the datasets. Note that all scripts require <code>numpy</code>, <code>matplotlib</code>, <code>h5py</code>, and <code>GooseMPL</code> to be installed. The latter two are included here, the other two are considered standard.</p>
RAW DATA - Control of Intermolecular Interactions toward the Production of Free-Standing Interfacial Polydopamine Films - ACS Applied Materials & Interfaces 2023 15 (30), 36922-36935 DOI: 10.1021/acsami.3c05236
<div>This repository contains RAW data for the experiment described in the publication:</div> <div> </div> <div>Jakub Szewczyk, Visnja Babacic, Adam Krysztofik, Olena Ivashchenko, Mikołaj Pochylski, Robert Pietrzak, Jacek Gapiński, Bartłomiej Graczykowski, Mikhael Bechelany, and Emerson Coy, Control of Intermolecular Interactions toward the Production of Free-Standing Interfacial Polydopamine Films, ACS Applied Materials & Interfaces 2023 <em>15</em> (30), 36922-36935, DOI: 10.1021/acsami.3c05236.</div> <div> </div> <div>For more information please contact the corresponding authors.</div>
VPS13B is localized at the interface between Golgi cisternae and is a functional partner of FAM177A1
<p>Source Data For Main Figures 1-4 and Supplemental Figures 1-3</p> <p>Tabular Data For Main Figure 1-3 and Supplemental Figure 3</p> <p>Training Dataset for Main Figure 4 embedded in <a href="../api/records/11243617/draft/files/Ilastik%20trained%20Data%20(Golgi%20reformation).ilp/content" target="_blank" rel="noopener noreferrer">Ilastik trained Data (Golgi reformation).ilp</a> file that requires you to download ilastik software.</p>
Accompanying dataset for the paper "An explicit dynamics framework suited to highly non-smooth interface behaviors"
<h2>Contributions</h2> <ul> <li>Author #1 carried out most of the study, performed numerical simulations, and drafted the manuscript</li> <li>Author #2 helped with implementation and numerical issues</li> <li>All authors developed the methodology, conceived the study, and participated in its design, coordination, and critical review of the manuscript. All authors read and approved the final manuscript.</li> </ul> <h2>Funding sources</h2> <ul> <li>We gratefully acknowledge the French National Association for Research and Technology (ANRT, CIFRE grant number 2021/0957).</li> <li>This work was supported by the "Manufacture Française de Pneumatiques Michelin"</li> </ul> <h2>Data structure and information</h2> <ul> <li><code>data</code> -- folder for raw data<ul> <li><code>Peeling3Dv7.dgibi</code> -- <a href="https://www-cast3m.cea.fr">CAST3M</a> input file to produce the mesh (Gibiane language)</li> <li><code>Peeling3Dv7.inp</code> -- input FE data file (ASCII AVS UCD format)</li> <li><code>Peeling3Dv7.m</code> -- main matlab/Octave source file uses the open-source library matlabEF for reading input data and producing the FE required operators, which is available at <a href="https://github.com/dureisse/matlabEF.git">https://github.com/dureisse/matlabEF.git</a> and <a href="https://hal.science/hal-04647638">https://hal.science/hal-04647638</a>.</li> </ul> </li> <li><code>workflows</code><ul> <li><code>install.sh</code> -- script to reinstall the dependencies</li> <li><code>reproduce.sh</code> -- script for re-running the study</li> </ul> </li> </ul> <h2>Paper Description</h2> <p>Dynamic systems, and in particular mechanical structures, may be subjected to non-smooth loadings such as impacts or shocks. Moreover, their behavior itself may exhibit more or less non-smooth evolutions, as when fracture occurs. Therefore, robust simulation models are of interest to capture such behaviors. A particular focus is made herein on time-stepping explicit dynamics schemes to allow efficient simulations, and non-smoothness is embedded within the discrete resolution model, so that robust simulations can be obtained, with a minimum number of numerical parameters. The original contributions of this article lie in the way the non-smooth behavior is formulated to be embedded in an explicit dynamics framework. This study focuses on the solver for dynamics with non-smooth interface behavior, rather than on the behavior models themselves. The applications concern non-smooth interface behaviors at macroscopic scale, between displacement jump on the 2D interface surface with no thickness, and interfacial force distributions acting on the bodies apart the interface. The proposed test cases which can serve as benchmarks for simulation codes, concern in a first step contact and perfectly plastic interface behavior (for illustrative purpose, on a 0D example). The last numerical test deals with contact, friction, fracture and adhesion for an extrinsic perfectly brittle interface behavior, to exemplify the feasibility on a full 3D finite element model.</p>
Determination of hordenine in beer samples and bodybuilding supplement at the electrified liquid-liquid interface
<p>Data set for the paper " Determination of hordenine in beer samples and bodybuilding supplement at the electrified liquid-liquid interface"</p>
Images of nematic liquid crystal interfaces under different flow conditions
<p>The images were collected from microfluidic 5CB-water interfaces. The channel geometries, flow configurations, interfacial 5CB anchoring conditions, image types, and imaging settings are indicated in the .xlsx files.</p>
A Lagrangian study of interfaces at the edges of cumulus clouds
<p>The upload contains DNS data that has been used to produce the results published in Nair et al, 'A Lagrangian study of interfaces in cumulus clouds,' Journal of the Atmospheric Sciences, 2021. Please read the README file for all necessary information on how to read and analyze the data.</p>
Dataset for article: Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
<p>Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (<b>Co</b>rrelated <b>Var</b>iation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggests that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, <b>Co-Var </b>web server (<a href="http://www.hpppi.iicb.res.in/ishi/covar/index.html">http://www.hpppi.iicb.res.in/ishi/covar/index.html</a>) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.</p>
MD trajectories for "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases"
<p>This dataset contains Amber MD trajectories for the MD simulations described in the manuscript "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases" by Christopher D. Fage, Simone Kosol, Matthew Jenner, Carl Öster, Angelo Gallo, Milda Kaniusaite, Roman Steinbach, Michael Staniforth, Vasilios G. Stavros, Mohamed A. Marahiel, Max J. Cryle, and Józef R. Lewandowski published in ACS Catalysis (<a href="https://doi.org/10.1021/acscatal.1c02113">https://doi.org/10.1021/acscatal.1c02113</a>). If you use these data please cite the original manuscript (follow the manuscript DOI for the final citation, which was not available at the time of publishing this data set). </p> <p>To reduce their size the trajectories were stripped of water and ions. Only frames every 1 ns or 5 ns were saved. Please see the Supporting Information of the source manuscript for the conditions for the simulations. </p>
3D Virtual Reality vs. 2D Desktop Registration User Interface Comparison
<p>Thank you for visiting this repository, which contains a ZIP file with the data for our Registration User Interface (RUI) 2D Desktop vs. VR user study.</p>
NSLS-II TES virtual beamline example for Sirepo SRW & Shadow interfaces
<p>Exported as a zip-archive from <a href="https://sirepo.com">sirepo.com</a>.</p> <p>From the following original sources:</p> <ul> <li><a href="https://www.sirepo.com/srw#/beamline/ReJMAvC6">https://www.sirepo.com/srw#/beamline/ReJMAvC6</a></li> <li><a href="https://www.sirepo.com/shadow#/beamline/TqRavgOQ">https://www.sirepo.com/shadow#/beamline/TqRavgOQ</a></li> </ul>
Engineering the Radiative Dynamics of Thermalized Excitons with Metal Interfaces
<p>Here we analyze the emission properties of excitons in two metal structures: placed near a single metal (silver) interface and placed symmetrically at the center of a Fabry-Perot microcavity. The exciton is modeled as a point dipole and as an extended 2D dipole. With the momentum dependent emission rates for the extended exciton, we can also examine its temperature dependent behavior by applying a momentum distribution. We investigate the cases of a Maxwell-Boltzmann statistical distribution and a Bose-Einstein statistical distribution (since excitons form composite bosons). </p> <p>The dataset provided are the Mathematica codes used to generate the plots in our paper. The point dipole results are contained in the notebook files: Emission Rate 2, Multiple Interfaces, Multiple Interfaces_Par (1), and Multiple Interfaces_Perp. The extended exciton results are contained in the notebook files: Extended Exciton 2-3 and BE stats. </p>
Molecular dynamics simulation of chitin nanocrystal-water interfaces
<p>This is the data repository for the paper "­Probing the structural details of chitin nanocrystal-water interfaces by three-dimensional atomic force microscopy" by Ayhan Yurtsever, Pei-Xi Wang, Fabio Priante, Ygor Morais Jaques, Kazuki Miyata, Mark J. MacLachlan, Adam S. Foster, and Takeshi Fukuma.</p> <p>It contains:</p> <p>- The system's starting geometry (water-chitin.pdb)</p> <p>- The production trajectory, in .dcd format (nvt_prod_chitin.tar.xz, uncompressed size 1.9 GB)</p> <p>- The resulting water density, in .cube format, computed on each of the chitin surfaces (chitin_cube_densities_vmd.tar.xz, uncompressed size 3.1 GB)</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.