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2,142 results for “by contact”
CVPIA Predation Contact Point Study - 2020: Impacts of Artificial Light At Night in the Upper Sacramento River
The Central Valley Project Improvement Act (CVPIA) has led to the implementation of a Decision Support Model (DSM) to assist in the prioritization of CVPIA restoration actions. The fall-run Chinook salmon DSM depends on a coarse-resolution salmon life-cycle model to predict the population benefits of different restoration actions and scenarios. One critical element of the life-cycle model is how to incorporate predation mortality during the juvenile rearing and outmigration portion of the salmon life-cycle in the Sacramento-San Joaquin Delta. Of particular importance to potential restoration activities, is the predation mortality that occurs in proximity to, and as a result of contact points between predator and prey fishes. Sacramento River winter-run Chinook salmon (Oncorhynchus tshawytscha) are a genetically distinct Evolutionary Significant Unit (ESU) with a unique life history and are listed as endangered at both state and federal levels. Predation of juvenile winter-run by piscivorous fishes is considered to be an important stressor that may reduce the population size of this ESU. Notably, the presence of artificial illumination at night (ALAN) has been shown to aggregate and slow out-migrating salmonids and increase predation by piscivores, and may be an important contact point affecting winter-run Chinook salmon, especially given the vast majority of winter-run Chinook salmon are born and rear within the city limits of Redding, CA. Perhaps the most significant source of ALAN in this region of the Sacramento River is the iconic Sundial Bridge. This bridge is illuminated at night and regional biologists have long been concerned on its potential impacts on winter-run Chinook salmon, as mediated through predation by Rainbow Trout. We therefore performed a field-based experiment to better inform this management concern. To assess the impacts of Sundial Bridge ALAN on fishes, our first objective was to determine whether variable ALAN intensities altered the relati
Higher-Mode Contact Resonance Operation of a High-Aspect- Ratio Piezoresistive Cantilever Microprobe (Data)
<p>Raw data, Ansys Workbench Projects and figures used for the article "Higher-Mode Contact Resonance Operation of a High-Aspect- Ratio Piezoresistive Cantilever Microprobe", published in the proceedings of SMSI 2020, which did not take place because of Covid-19 virus pandemic.</p> <p>The data can be opened by a text editor<br> Ansys projects are compressed using 7-Zip and can be opened by Ansys Workbench.</p>
Optimizing a Cantilever Measurement System towards High Speed, Nonreactive Contact-Resonance-Profilometry (Data)
<p>Raw data, scripts and figures used for the article "Optimizing a Cantilever Measurement System towards High Speed, Nonreactive Contact-Resonance-Profilometry", published in <em>Proceedings </em>on 21 Nov 2018.</p> <p>The data/scripts can be opened/executed by the software "Matlab"</p>
Contact changes in shear-stabilized jammed packings
<p>Authors are listed in alphabetical order.</p> <p>This data set contains the result of small simple shear deformations applied to approximately 140,000 shear-stabilized jammed packings (see [...]), focusing on contact changes, as described in [1,2,3,4].</p> <p>These packings contain <span class="math-tex">\(N = 16 \ldots 4096\)</span> particles with harmonic interactions, under a confining pressure <span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16). </p> <p>In addition, a data file summarizing properties of the first contact change for each packing is provided.</p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L² is the simulation box area, p the externally applied pressure, k=1 the spring constant and </p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| & \textrm{if } |\vec{r_{ij}}| < R_i + R_j, \\ 0 & \textrm{otherwise.} \end{array}\right.\)</span></p> <p> </p> <p>During shear, the boundary conditions are changed, and the system is relaxed to the new state. The simulation uses a bisection algorithm to efficiently step towards each subsequent contact change; see [3,4] for details.</p> <p><strong>Data files</strong></p> <p>The packings are stored in HDF5 data files. For each ensemble, we provide two files: one with and one without particle positions:</p> <ul> <li>N1024~P3162e-3_shear_noparticles.h5 includes all simulation data, but omits particle positions (see below for which data is included).</li> <li>For small data sets, N1024~P3162e-3_shear.h5 contains all simulations and all particle positions</li> <li>For large data sets, N1024~P3162e-3_shear_partial.h5 contains <em>a subset</em> of all simulations, but with all particle positions.</li> <li>Full particle positions for all simulations are available upon request to the authors. Please contact Martin van Hecke .</li> </ul> <p>All files follow the same HDF5 layout:</p> <ul> <li>Example name: N1024~P3162e-3_shear.h5 and N1024~P3162e-3_shear_noparticles.h5 <ul> <li>Packings with <span class="math-tex">\(N=1024\)</span> particles</li> <li>Pressure <span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090/SR for the packing with id 0090. <ul> <li>This directory contains a table 'data' indicating system parameters for each simulation step: <ul> <li>boundary conditions L1 and L2 (also as L, alpha, delta)</li> <li>pressure P,</li> <li>strain gamma,</li> <li>stresses s_xy (simple shear), s_xx and s_yy, </li> <li>number of contacts Ncontacts, contact number Z and number of rattlers #rattler</li> <li>number of changed contacts for this contact change bisection Nchanges, N+ (created), N- (broken)</li> <li>contact number Z</li> <li>energy U, enthalpy H and their change in the last relaxation step (dU, dH)</li> <li>step runtime t_run (seconds), #CG, #FIRE (number of conjugate gradient and FIRE iterations)</li> <li>path to the packing corresponding to this state (does not always exist for each state for older simulations)</li> </ul> </li> <li>Each state is saved in /N1024/P3.1620e-03/0090/SR/0000 (initial), /N1024/P3.1620e-03/0090/SR/0001, ...etc. <ul> <li>States are not included in the _noparticles.h5 files</li> <li>Some files omit intermediate positions, and only store positions just before and just after a contact change.</li> <li>The format of these directories is the same as in https://dx.doi.org/10.5281/zenodo.59216.</li> </ul> </li> </ul> </li> </ul> <p>Finally, we provide a summary file (shear_summary_cache.h5) which contains one table ('data') with properties of the first contact change of all packings. We provide the following columns:</p> <ul> <li>The variable postfix determines whether the value was calculated in the initial state (_base), just before the first contact change (_min) or just after the first contact change (_plus).</li> </ul> <p> </p> <ul> <li>General/simulation properties <ul> <li>Number of particles 'N'</li> <li>Random seed ['num', 'PackingNumber_base']</li> <li>External pressure 'P0_base'</li> <li>Simulation step ['i_min', 'i_plus']</li> </ul> </li> <li>Relaxation statistics <ul> <li>Last change in enthalpy during relaxation ['dH_base', 'dH_plus', 'dH_min']</li> <li>Last change in energy during relaxation ['dU_base', 'dU_plus', 'dU_min']</li> <li>Maximum gradient ['maxGrad_base', 'gg_min', 'gg_plus']</li> <li>Initial simulation runtime ['runtime (s)_base']</li> </ul> </li> <li>State properties <ul> <li>Number of rattlers 'N - Ncorrected_base'</li> <li>Number of non-rattler particles ['Neff_min', 'Neff_plus']</li> <li>Number of contacts ['Ncontacts_plus', 'Ncontacts_min']</li> <li>Contact number z ['Z_base', 'Z_min', 'Z_plus']</li> <li>Internal pressure ['P', 'P_base', 'P_min', 'P_plus']</li> <li>Mean overlap δ ['mean_delta_base']</li> <li>Packing fraction ['phi_base', 'phi_min', 'phi_plus']</li> <li>Enthalpy ['H_base', 'H_plus', 'H_min']</li> <li>Energy ['Uhelper_base', 'U_min', 'U_plus']</li> <li>Simple shear parameter alpha ['alpha_base', 'alpha_min', 'alpha_plus']</li> <li>Pure shear parameter delta ['delta_base', 'delta_plus', 'delta_min']</li> <li>Square root of area ['L_base', 'L_min', 'L_plus']</li> <li>Stresses on boundaries: <ul> <li>xx ['sxx_base', 's_xx_min', 's_xx_plus',]</li> <li>yy [ 'syy_base', 's_yy_plus', 's_yy_min',]</li> <li>xy ['sxy_base', 's_xy_plus', 's_xy_min']</li> </ul> </li> <li>Elastic moduli: <ul> <li> ['c1_base', 'c1_min', 'c1_plus',</li> <li>'c2_base', 'c2_min', 'c2_plus',</li> <li>'c3_base', 'c3_min', 'c3_plus',</li> <li>'c4_base', 'c4_plus', 'c4_min',</li> <li>'c5_base', 'c5_plus', 'c5_min',</li> <li>'c6_base', 'c6_min', 'c6_plus',</li> <li>'Dac_base', 'Dac_plus', 'Dac_min',</li> </ul> </li> <li>AC component of G(θ) ['Gac_base', 'Gac_min', 'Gac_plus']</li> <li>DC component of G(θ) ['Gdc_base', 'Gdc_min', 'Gdc_plus',]</li> <li>AC component of U(θ) ['Uac_base', 'Uac_plus', 'Uac_min']</li> <li>DC component of U(θ) ['Udc_base', 'Udc_plus', 'Udc_min']</li> <li>Simple shear ['Galpha_base', 'Galpha_plus', 'Galpha_min' ]</li> </ul> </li> <li>Contact change properties <ul> <li>Applied strain gamma ['gamma_plus', 'gamma_min'] <ul> <li><em>gamma_min is used as contact change strain</em></li> </ul> </li> <li>Number of created/broken contacts ['N+_plus', 'N+_min', 'N-_plus', 'N-_min']</li> <li>Number of changed contacts (=N<sup>+</sup> + N<sup>-</sup>) ['Nchanges_plus', 'Nchanges_min']</li> <li>Making & breaking strain from upar and uperp: <ul> <li>simple linear (SL) solution: ['gmk_SL_base', 'gbk_SL_base']</li> <li>full quadratic (FQ) solution: ['gmk_FQ_base' 'gbk_FQ_base']</li> </ul> </li> <li>G up to CC from fit σ=Gγ & error bar ['Glin', 'Glinerr']</li> <li>G up to CC from fit σ=Gγ + λγ² & error bar ['Gquad', 'Gquaderr']</li> <li>λ up to CC from fit σ=Gγ + λγ² & error bar ['lambdaquad', 'lambdaquaderr']</li> </ul> </li> </ul> <p>[1] Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke. <em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear. </em>Phys. Rev. Lett. <strong>109</strong>, 095703. http://dx.doi.org/10.1103/PhysRevLett.109.095703</p> <p>[2] Merlijn S. van Deen, Johannes Simon, Zorana Zeravcic, Simon Dagois-Bohy, Brian P. Tighe, and Martin van Hecke. <em>Contact changes near jamming</em>. Phys. Rev. E <strong>90</strong> 020202(R). http://dx.doi.org/10.1103/PhysRevE.90.020202</p> <p>[3] Merlijn S. van Deen, Brian P. Tighe, and Martin van Hecke. <em>Contact Changes of Sheared Systems: Scaling, Correlations, and Mechanisms</em>. arXiv:1606.04799. https://arxiv.org/abs/1606.04799</p> <p>[4] Merlijn S. van Deen. <em>Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements</em>. PhD Thesis, Leiden University, 2016. https://openaccess.leidenuniv.nl/handle/1887/40902</p>
IODP Expedition 397T Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 383 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
Simulated Local Electrical Impedance in Atrial Tissue With Varying Contact Force
<p>In this dataset we can find geometrical setups that served as an input to carry forward electrical impedance simulations with EIDORS. <br>A 3D geometrical models of one ablation catheters combining measurements of local impedance (LI) and contact force (CF) commercially available is included. The objective of these in silico experiments laid on understanding how CF and tissue deformation affect LI measurements.<br>To achieve it, using the catheter against the tissue, several grams of force are applying.<br>The dataset consists of the original geometrical models before deformation and a couple of examples of the deformed one.</p> <h2>Data structure</h2> <ul> <li>geos: original geometries of the catheter and the tissue in stl <ul> <li>catheter.stl</li> <li>tissue.stl</li> </ul> </li> <li>geos_deformed: deformed geometries at 5 and 10 grams, respectively. Includes the catheter, the mesh, and the tissue <ul> <li>5 g <ul> <li>catheter.stl</li> <li>tissue_5g.stl</li> <li>mesh_5g.stl</li> </ul> </li> <li>10 g <ul> <li>catheter.stl</li> <li>tissue_10g.stl</li> <li>mesh_10g.stl</li> </ul> </li> </ul> </li> </ul>
Contact data of Pienter3 (2016-2017) and PiCo (2020-2023) studies in the Netherlands
<p>Contact data of two cross-sectional, sero-epidemiological studies in the general population of the Netherlands:</p><ul><li>The Pienter3 study (2016-2017)</li><li>The PienterCorona (PiCo) study consisting of 10 rounds (2020-2023)</li></ul>
IODP Expedition 378 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
A Terrylene Bisimide based Universal Host for Aromatic Guests to Derive Contact Surface-Dependent Dispersion Energies
<p>Additional data to report <a href="https://doi.org/10.1002/anie.202318451">https://doi.org/10.1002/anie.202318451</a>:<br><br>π–π interactions are among the most important intermolecular interactions in supramolecular systems. Here we determine experimentally a universal parameter for their strength that is simply based on the size of the interacting contact surfaces. Toward this goal we designed a new cyclophane based on terrylene bisimide (TBI) π-walls connected by <em>para</em>-xylylene spacer units. With its extended π-surface this cyclophane proved to be an excellent and universal host for the complexation of π-conjugated guests, including small and large polycyclic aromatic hydrocarbons (PAHs) as well as dye molecules. The observed binding constants range up to 10<sup>8</sup> M<sup>−1</sup> and show a linear dependence on the 2D area size of the guest molecules. This correlation can be used for the prediction of binding constants and for the design of new host–guest systems based on the herewith derived universal Gibbs interaction energy parameter of 0.31 kJ/molÅ<sup>2</sup> in chloroform.</p>
IODP Expedition 367 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
Self-excited Contact Resonance Operation of a Tactile Piezoresistive Cantilever Microprobe with Diamond Tip (Data)
<p>Raw data and figures used for the article "Self-excited Contact Resonance Operation of a Tactile Piezoresistive Cantilever Microprobe with Diamond Tip", published in the proceedings of Sensor and Measurement Science International 2021; 2021-05-03 - 2021-05-06; digital.</p>
Research Infrastructure Contact Zones
<p>The landscape of biodiversity data infrastructures and organisations is complex and fragmented. Many occupy specialised niches representing narrow segments of the multidimensional biodiversity informatics space, while others operate across a broad front but differ from others by data type(s) handled, their geographic scope and the life cycle phase(s) of the data they support. To characterise the various dimensions of the biodiversity informatics landscape, we developed a framework to survey these dimensions for ten organisations (<a href="https://www.dissco.eu/">DiSSCo</a>, <a href="https://www.gbif.org/">GBIF</a>, <a href="https://ibol.org/">iBOL</a>, <a href="https://www.catalogueoflife.org/">Catalogue of Life</a>, <a href="https://www.inaturalist.org/">iNaturalist</a>, <a href="https://www.biodiversitylibrary.org/">Biodiversity Heritage Library</a>, <a href="https://geocase.eu/">GeoCASe</a>, <a href="https://www.lifewatch.eu/">LifeWatch</a>, <a href="https://www.lter-europe.net/elter-esfri">eLTER</a>, <a href="https://elixir-europe.org/">ELIXIR</a>), relative to both their current activities and long-term strategic ambitions.</p> <p>The results of the survey are presented in this dataset. Details of the assessment methodology, data model, scope and high-level results are described in an accompanying paper, which is currently under review and will be linked to this dataset on publication.</p>
The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts
<p>This dataset contains the joint toque measurements of a robot manipulator (<a href="https://blog.robotiq.com/bid/64944/Collaborative-Robot-Series-KUKA-s-Light-Weight-Robot-4">KUKA LWR4+</a>) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at <a href="https://www.ce.cit.tum.de/en/lsr/home/">Chair of Automatic Control Engineering</a>, <a href="https://www.tum.de/en/">Technical University of Munich</a>, Munich, Germany, by <a href="https://sites.google.com/view/zengjie-zhang/home">Dr. Zengjie Zhang</a>, under the supervision of <a href="https://www.ce.cit.tum.de/lsr/team/dozenten/dirk-wollherr/">Dr. Dirk Wollherr</a>, in 2017. Its detailed recording procedure is explained in the following work:</p> <p>[1] <strong>Zhang Z</strong>, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. <em>IEEE Transactions on Automation Science and Engineering</em>, 2020, 18(3): 1144-1156.</p> <p>The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm.</p> <p>The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB.</p> <p>This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.</p>
Water conformation at three-phases contact lines
<p>This dataset contains detailed trajectories obtained from molecular dynamics simulations performed using GROMACS. The simulated system consists in a quasi-2-dimensional SPC/E water meniscus confined between solid substrates composed by silica quadrupoles. Details on the molecular model and the simulation technique can be found in the references article. The data collected in this dataset are utilized in a ongoing project aimed to demystify the motion of three-phases contact lines over hydrophilic surfaces.</p>
CoMix social contact data (Italy)
<p>CoMix social contact data for Italy.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Daniela Paolotti at the ISI Foundation.</p>
CoMix social contact data (Denmark)
<p>CoMix social contact data for Austria.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Michael Bang Petersen at Aarhus University.</p>
CoMix social contact data (Greece)
<p>CoMix social contact data for Greece.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Elpida Pavi at the University of West Attica (UniWA).</p>
CoMix social contact data (Portugal)
<p>CoMix social contact data for Portugal.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Baltazar Nunes at the National Health Institute Doutor Ricardo Jorge (INSA).</p>
CoMix social contact data (Poland)
<p>CoMix social contact data for Poland.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Magdalena Rosinska at the National Institute of Public Health - National Institute of Hygiene.</p>
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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.