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.
27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “percolation”
Rainfall intensification enhances deep percolation and soil water content at the Kellogg Biological Station, Hickory Corners, MI (2015 to 2016)
Dataset AbstractData supporting the paper Hess, L., E. L. Hinckley, G. P. Robertson, S. K. Hamilton, and P. Matson. 2018. DOI: 10.2136/vzj2018.07.0128original data source http://lter.kbs.msu.edu/datasets/198
Data of "H2S dosimeter with controllable percolation threshold based on semi-conducting copper oxide thin films" published in JSSS 2017
<p>Raw data to the Paper "H2S dosimeter with controllable percolation threshold<br> based on semi-conducting copper oxide thin films" published in "Journal of Sensors and Sensor Systems".</p> <p>Acknowledgement and Funding in the txt.file</p>
Integrated Lysimeter Study (Deep Percolation) Data from Saint-Nicéphore, Quebec
<p>This dataset results from a study on various landfill final covers constructed at a landfill site in Saint-Nicéphore, Quebec. The study involved three large experimental plots, each containing drainage lysimeters and sensors to measure soil moisture, temperature, and suction. For detailed construction information, please consult the construction report included in this dataset and the references. </p> <p>The dataset is intended to facilitate future research on deep percolation in cold regions, providing comprehensive data for the development and validation of hydrological models.</p> <p>The dataset includes:</p> <ol> <li>Hourly deep percolation rates, measured by four drainage lysimeters within the soil enclosures.</li> <li>Half-hourly measurements of soil moisture, temperature, and suction at various depths.</li> <li>Hourly and daily meteorological data for the duration of the study period.</li> <li>Laboratory-estimated soil parameters, including saturated hydraulic conductivity, dry density, and soil texture.</li> </ol> <p>Important: Please be aware that the timestamps in these datasets may not be evenly spaced. This means that some data points might be missing or irregularly collected. Before performing any time-series analysis or data visualization, you may need to preprocess the data to ensure consistent time intervals.</p> <p>For further information or inquiries, please contact the creators.</p>
Dataset: Connectivity and rigidity percolation of cytoskeletal networks.
<p>Dataset containing information for "Connectivity and rigidity percolation of cytoskeletal networks."</p> <p>File: Fig1A_MEDYAN_Unbranched_timeseries_motor_333_linker_1500_tmax_122.csv<br> Description:<br> Average MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> Columns:<br> Last_Timestep: Last time step of the simulations<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> Simulation: Number of simulations<br> M_p: Number of plus ends<br> M_m: Number of minus ends<br> M_c: Number of free binding sites<br> M_M: Number of free motors<br> M_L: Number of free linkers<br> M_pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> M_cMc: Number of bound motors<br> M_cLc: Number of bound linkers<br> M_G: Number of free G-actin<br> M_b: number of free branchers</p> <p>File: Fig1A_ODE_Unbranched_timeseries_motor_333_linker_1500_tmax_10000.csv<br> Description:<br> Chemical kinetics calculations for transient concentrations of motor, linker and brancher for equivalent MEDYAN simulations of a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of minus ends<br> p: Number of plus ends<br> G: Number of free G-actin<br> M: Number of free motors</p> <p>File: Fig1A_Unbranched_MEDYAN.csv<br> Description:<br> Species concentrations in MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> The simulations can be found in the Simulations_Unbranched folder<br> Columns:<br> Last_Timestep: Measured timestep<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> chem_path: Path of the simulation<br> AD: Number of unbound G-actins<br> MD: Number of unbound motors<br> LD: Number of unbound linkers<br> FA: Number of bound F-actin monomers<br> PA: Number of plus ends<br> MA: Number of minus ends<br> LA: Number of bound linkers<br> MOA: Number of bound motors<br> Simulation: Simulation ID</p> <p>File: Fig1B_Branched_MEDYAN.csv<br> Description:<br> Species concentrations in MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, 300 branchers.<br> The simulations can be found in the Simulations_Branched folder<br> Columns:<br> Last_Timestep: Measured timestep<br> N_Motors: Number of motors<br> N_Linkers: Number of linkers<br> chem_path: Path of the simulation<br> AD: Number of unbound G-actins<br> BD: Number of unbound branchers<br> MD: Number of unbound motors<br> LD: Number of unbound linkers<br> FA: Number of bound F-actin monomers<br> PA: Number of plus ends<br> MA: Number of minus ends<br> LA: Number of bound linkers<br> MOA: Number of bound motors<br> BA: Number of bound branchers<br> Simulation: Simulation ID</p> <p>File: Fig1B_MEDYAN_Branched_timeseries_motor_333_linker_1500_tmax_122.csv<br> Description:<br> Average MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, 300 branchers.<br> Columns:<br> Last_Timestep: Last time step of the simulations<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> Simulation: Number of simulations<br> M_p: Number of plus ends<br> M_m: Number of minus ends<br> M_c: Number of free binding sites<br> M_M: Number of free motors<br> M_L: Number of free linkers<br> M_pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> M_cMc: Number of bound motors<br> M_cLc: Number of bound linkers<br> M_G: Number of free G-actin<br> M_B: total number of branchers<br> M_cBm: number of bound branchers<br> M_b: number of free branchers</p> <p>File: Fig1B_ODE_Branched_timeseries_motor_333_linker_1500_tmax_10000_v2.csv<br> Description:<br> Chemical kinetics calculations for transient concentrations of motor, linker and brancher for equivalent MEDYAN simulations of a 1um3 box with 333 motors and 1500 linkers, and 300 branchers.<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> p: Number of plus ends<br> cMc: Number of bound motors<br> cBm: Number of bound branchers<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of minus ends<br> M: Number of free motors<br> G: Number of free G-actin<br> B: Number of free branchers</p> <p>File: Fig1C_Ps_timeseries_unbranched.csv<br> Description:<br> Flory-Stockmayer results for unbranched chemical kinetics calculations<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of free minus ends<br> p: Number free of plus ends<br> G: Number of free G-actin<br> M: Number of free motors<br> P0: Probability that an F-actin monomer is connected to another one on its plus end<br> P1: Probability that an F-actin monomer is connected to another one on its minus end<br> P2: Probability that an F-actin monomer is connected to another one on its binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Nb: Average number of bonds per F-actin monomer<br> Nn: Mean cluster size<br> Nw: Mean weighted cluster size<br> Ratio: Nw/Nn Ratio</p> <p>File: Fig1D_Ps_timeseries_branched.csv<br> Description:<br> Flory-Stockmayer results for branched chemical kinetics calculations<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cBm: Number of bound branchers<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of free minus ends<br> p: Number free of plus ends<br> G: Number of free G-actin<br> M: Number of free motors<br> B: Number of free branchers<br> P0: Probability that an F-actin monomer is connected to another one on its plus end<br> P1: Probability that an F-actin monomer is connected to another one on its minus end<br> P2: Probability that an F-actin monomer is connected to another one on its binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Nb: Average number of bonds per F-actin monomer<br> Nn: Mean cluster size<br> Nw: Mean weighted cluster size<br> Ratio: Nw/Nn Ratio<br> Qm: Probability that an F-actin monomer is not connected to an infinite cluster through the minus end<br> Qp: Probability that an F-actin monomer is not connected to an infinite cluster through the plus end<br> Qc: Probability that an F-actin monomer is not connected to an infinite cluster through the binding site</p> <p>File: Fig2_Two-step.csv<br> Description:<br> Representative steady state concentrations for a non-cooperative two-step model of linker binding.<br> Columns:<br> Fc: Concentration of free binding sites<br> FcL: Concentration of linkers bound to a single binding site<br> FcLFc: Concentration of linkers bound to a pair of binding sites<br> L: Concentration of unbound linkers<br> Fc0: Total concentration of binding sites<br> L0: Total concentration of linkers</p> <p>File: Fig3_two_step_heatmap.csv<br> Proportion of the concentration of crosslinks to the concentration of total binding sites as a function of the linker binding equilibrium constant<br> Description:<br> 2D matrix, where the columns indicate the linker binding constant multiplied by the total concentration of binding sites, the rows indicate the total concentration of linkers per binding site , and the value corresponds to the total number of linkers bound to two binding sites divided by the total concentration of binding sites.</p> <p>File: Fig5A_Ps_unbranched.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher.</p> <p><br> File: Fig5B Ps_branched.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model with brancher.<br> </p> <p>File: Fig6_Ps_Branched_var.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of branchers to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher or motors.</p> <p><br> File: Fig7B_Ps_unbranched_linkeronly.csv</p> <p>Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher. The clusters are defined here as F-actin monomers connected by linkers, and without including motor connections.</p> <p>File: Fig7D_Ps_branched_linkeronly.csv</p> <p>Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model with brancher. The clusters are defined here as F-actin monomers connected by linkers or branchers, and without including motor connections.</p> <p>File: Fig9_data.csv<br> Description:<br> Minimum motor concentration to reach rigidity percolation as a function of the linker concentration for systems with and without brancher, considering both linker and motor connections or just motor connections and for different values of linker rigidity. The motor and linker concentrations are measured as the proportion of linkers or motors to actin.<br> Columns:<br> L: linker concentration <br> "M (unbranched, linkers and motors, bcLc=0)"<br> "M (unbranched, linkers and motors, bcLc=1)"<br> "M (unbranched, linkers and motors, bcLc=2)"<br> "M (unbranched, linkers and motors, bcLc=3)"<br> "M (unbranched, linkers and motors, bcLc=4)"<br> "M (unbranched, linkers and motors, bcLc=5)"<br> "M (unbranched, linkers and motors, bcLc=6)"<br> "M (branched, linkers and motors, bcLc=0)"<br> "M (branched, linkers and motors, bcLc=1)"<br> "M (branched, linkers and motors, bcLc=2)"<br> "M (branched, linkers and motors, bcLc=3)"<br> "M (branched, linkers and motors, bcLc=4)"<br> "M (branched, linkers and motors, bcLc=5)"<br> "M (branched, linkers and motors, bcLc=6)"<br> "M (unbranched, linkers only, bcLc=0)"<br> "M (unbranched, linkers only, bcLc=1)"<br> "M (unbranched, linkers only, bcLc=2)"<br> "M (unbranched, linkers only, bcLc=3)"<br> "M (unbranched, linkers only, bcLc=4)"<br> "M (unbranched, linkers only, bcLc=5)"<br> "M (unbranched, linkers only, bcLc=6)"<br> "M (branched, linkers only, bcLc=0)"<br> "M (branched, linkers only, bcLc=1)"<br> "M (branched, linkers only, bcLc=2)"<br> "M (branched, linkers only, bcLc=3)"<br> "M (branched, linkers only, bcLc=4)"<br> "M (branched, linkers only, bcLc=5)"<br> "M (branched, linkers only, bcLc=6)"</p> <p>File: FigS1_data.csv<br> Description:<br> Connectivity percolation as a function of the probabilities that an F-actin monomer site is bound to another F-actin.<br> Columns:<br> ppm: probability that an F-actin monomer plus end is connected to another F-actin monomer minus end<br> pcc: probability that an F-actin monomer binding site is connected to another F-actin monomer binding site<br> Pcm: probability that an F-actin monomer binding site is connected to another F-actin monomer minus end<br> Qp: Probability that an F-actin monomer is not connected to an infinite cluster through the plus end<br> Qm: Probability that an F-actin monomer is not connected to an infinite cluster through the minus end<br> Qc: Probability that an F-actin monomer is not connected to an infinite cluster through the binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Percolated: Whether the system is percolated or not.</p> <p>File: simulations.tar.gz<br> Description: Contains the MEDYAN simulations used for figure 1. Each folder contains an individual simulation, with the following files:<br> systeminput.txt: Contains the input for the system conditions and settings<br> chemistryinput.txt: Contains the input for the chemical species<br> chemistry.traj: Output trajectory containing number of species in the simulations<br> snapshot.traj: Output trajectory containing the coordinates of the species.<br> For more information please reference the MEDYAN user guide and reference:<br> K Popov, JE Komianos and GA Papoian (2016) MEDYAN: Mechanochemical Simulations of Contraction and Polarity Alignment in Actomyosin Networks. PLoS Comput Biol 12(4): e1004877. doi:10.1371/journal.pcbi.1004877</p>
Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases - dataset
<p><strong>Abstract</strong>:<br>from [1]</p> <p>This contribution builds the concluding step of a multiscale approach to effectively capture the mechanical <br>behavior of polymer nanocomposites (PNCs), in this case, silica-modified polystyrene. By introducing <br>continuum-based representative volume elements (RVEs) that employ previously identified elastoplastic property <br>gradients for the interphases surrounding the fillers, the effects of particle size, particle volume fraction, <br>and agglomeration on the mechanical performance are investigated. Uniaxial tension tests are simulated with <br>the respective finite-element RVEs, and stress–strain curves are derived. The elastic and plastic material <br>properties of the RVE can then be extracted and analyzed quantitatively by fitting the stress–strain curves <br>with a Voce-type elastoplasticity formulation. <br>At small degrees of agglomeration, i.e., good particle dispersion, in combination with sufficiently large <br>particle volume fraction, percolation bands form, leading to improved elastic and plastic properties. Higher <br>degrees of agglomeration or particle clusters behave like large single particles, which has an adverse effect, i.e., <br>the nanoscale size effect is thereby neutralized. Therefore, the precise MD-informed elastoplastic interphase <br>representation of our RVEs enables the investigation of the transition from beneficial percolation to unfavorable <br>agglomeration. Ultimately, this contribution establishes a link between the effects of particle size, particle <br>volume fraction, agglomeration, and percolation, which have so far only been discussed separately in the <br>literature. <br>Our methodology offers new insights into the structure–property relations of PNCs and their resulting <br>mechanical behavior. The underlying multiscale approach with a systematic transition from molecular to <br>microscopic scales is required to complement experimental observations and exploit the full potential of PNCs. </p> <p><br><strong>Contact</strong>:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p><strong>Software</strong>:</p> <p>All finite element simulations were performed with Simulia Abaqus/CAE2018 </p> <p><strong>License</strong>:</p> <p>Creative Commons Attribution Non Commercial 4.0 International</p> <p><strong>Context</strong>:</p> <p>Data set supplementing journal paper:</p> <p>[1] E.-M. Richter, G. Possart, P. Steinmann, S. Pfaller, & M. Ries, “Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases,” Composites Part B: Engineering, vol. 281, p. 111477, 2024.</p> <p><strong>Content</strong>:</p> <p>- excel sheet summarizing all RVE simulations in combination with the elastoplastic constitutive model calibration: elastoplastic_constitutive_model_calibration.xlsx<br>- input data for each RVE FE simulation in *.inp format following the naming convention:<br> agg_<degree of agglomeration>-fillercont_<filler content>Percent-fillerrad_<filler radius>nm<br> - degree of agglomeration is defined in [1]<br> - filler content is given in volume percent<br> - filler radius is given in nanometer </p> <p> </p>
Supporting data for "Percolation transition and bimodal density distribution in hdyrogen fluoride", J. Chem. Phys. 160, 204503 (2024)
<p>GROMACS input files for the simulation of hydrogen fluoride using the polarizable model of Pártay, Jedlovszky and Vallauri [<em>J. Chem. Phys. </em>124, 184504 (2006)] adapted for the Drude oscillator model, as published in <em>J. Chem. Phys.</em> 160, 204503 (2024) <a href="https://doi.org/10.1063/5.0207202" target="_blank" rel="noopener">https://doi.org/10.1063/5.0207202</a></p>
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </p>
Data from: Local suppression by link rewiring reveals discontinuous percolation transitions
Open the record for dataset details and reuse information.
Percolator
ID no.: 3896 Museum: The Museum of Pharmacy at the Jagiellonian University Medical College in Kraków https://muzea.malopolska.pl/en/objects-list/565 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Enameled Coffee Percolator
This yellow enameled coffee percolator was discovered by construction crews in October, 2018. Construction crews conducting work in an area where a known historic homestead once stood and contacted CAP once this coffee percolator and bricks were discovered. CAP members conducted a quick salvage operation at the location and found the remains of two glass bleach bottles dating to 1951, ceramic drain pipes, cement foundations, and the crumbling remains of brick walls, possibly part of an old basement. Based on the distribution, it is likely that trash and reduse was dumped into the basement before the structure was torn down and the foundations burried. The artifacts found here associated with the coffee percolator, as well as artifacts found in this area during the summer of 2018, CAP choose this location for excavations for the 2019 field season. No doubt other exciting finds such as this one will be uncovered! Source: Objaverse 1.0 / Sketchfab
Thermal and percolative analysis of 3D diffuse-interface composite microstructure
<p>This dataset contains supplementary data and utilities of the publication "Data-driven thermal and percolative analysis of 3D diffuse-interface composite microstructure" (<a href="https://doi.org/10.1016/j.matdes.2023.111746">Fathidoost, 2023</a>).</p> <p>This dataset documents homogenized anisotropic thermal conductivity of the corresponding microstructure (identified by volume fraction (Vf) and aspect ratio (Ar)) as a tensor with normalized interface thermal resistance (see Table 1). Voxelized digital microstructures and utilities are also attached for visualizing the overall thermal anisotropy.</p> <p><em>Table 1: The geometrical and thermal parameters employed in the generated microstructures.</em></p> <table> <thead> <tr> <th>Parameters</th> <th>Value (Unit)</th> <th>Type</th> <th>Increment</th> </tr> </thead> <tbody> <tr> <td>Minor principal axes length</td> <td>5 (nm)</td> <td>Constant</td> <td>-</td> </tr> <tr> <td>Aspect ratio, <span class="math-tex">\(A_\mathrm{r}\)</span></td> <td>[1 ,6]</td> <td>Linear</td> <td>1</td> </tr> <tr> <td>Inclusion volume fraction, <span class="math-tex">\(V_\mathrm{f}\)</span></td> <td>[5, 60]</td> <td>Linear</td> <td>5</td> </tr> <tr> <td>Thermal conductivity ratio, <span class="math-tex">\(K_\mathrm{r}\)</span></td> <td>[15, 100]</td> <td>Linear</td> <td>15</td> </tr> <tr> <td>Normalized interface resistance, <span class="math-tex">\(\tilde{R}_\mathrm{s} \)</span></td> <td>[1e-6, 1e10]</td> <td>Logarithmic</td> <td>1e2</td> </tr> </tbody> </table> <p><strong>Notice:</strong> The digital microstructure has been voxelized and stored in the ExodusII format, which can be loaded and visualized by the post-processing software, such as ParaView. In order to perform the homogenization, interface smoothening is required, i.e., to generate diffuse interfaces. In this work, we smoothened the interface by operating transient Allen-Cahn calculation with finite timesteps. Sec. 2.1 of the publication for more information).</p>
Advancing and retreating fronts in a changing climate: a percolation model of range shifts
<p>Climate change causes considerable shifts in the geographic distribution of species worldwide. Most data on range movements, however, derive from relatively short periods, within which it is difficult to distinguish directional shifts from random fluctuations. For detecting a shift, it is indispensable to delineate the range precisely. We propose a new method for delineation based on percolation theory. We suggest marking the boundary between the connected and fragmented occurrence of the species (the hull). We demonstrate the advantages of this connectivity-based method on simulated examples in which a metapopulation is advancing vs. retreating along an environmental gradient with different velocities. The simulations show that the hull is a fractal and has the same dimension (7/4) even when the front is advancing or retreating relatively fast, compared to the generation time. It is particularly robust in the retreating (trailing) edge. Accordingly, we propose marking the range edge at the mean position of the hull, the 'connectivity limit' of the species. Theoretical considerations suggest that the position of the connectivity limit is statistically more reliable than those limits that are delineated according to the outermost occurrences, and the connectivity-based method is broadly applicable to real-life data.</p>
Advancing and retreating fronts in a changing climate: a percolation model of range shifts
Open the record for dataset details and reuse information.
Data: Cities and Regions in Britain through hierarchical percolation
<p>The research in this paper can be reproduced with the two provided datasets from<br /> © Crown Copyright and Database Right [February 2016]. Ordnance Survey (Digimap Licence)</p> <p>1) UK_coordinates_mod.txt <br /> This file contains a list of the all the intersection points, with columns for id of point, its coordinate x and its coordinate y.</p> <p>2) UK_ncol_mod.txt<br /> This file contains the information about the whole network. It is in the form of id of node 1, id of node 2 and the weight is given by the real length of the road connecting these two points. </p> <p> </p>
FIGURE 2A–G in A remarkable new Nilotonia species (Acari, Hydrachnidia, Anisitsiellidae) from percolating water of a cave in Cat Ba island in Halong Bay, Vietnam
FIGURE 2A–G. Nilotonia sketi sp. nov. (A, D, F—male; B–C, E, G—female): A–B = genital field; C = IV-L-5 and 6; D = gnathosoma; E = palp (P-1 missing); F = palp; G = chelicera. Scale bars = 100 µm.
FIGURE 3A–E in A remarkable new Nilotonia species (Acari, Hydrachnidia, Anisitsiellidae) from percolating water of a cave in Cat Ba island in Halong Bay, Vietnam
FIGURE 3A–E. Nilotonia sketi sp. nov., male: A = I-L-2–6; B = II-L; C = III-L; D = IV-L; E = IV-L-6. Scale bars = 100 µm.
FIGURE 1A–E in A remarkable new Nilotonia species (Acari, Hydrachnidia, Anisitsiellidae) from percolating water of a cave in Cat Ba island in Halong Bay, Vietnam
FIGURE 1A–E. Nilotonia sketi sp. nov., male: A = idiosoma, ventral view; B = idiosoma, dorsal view; C = detail of dorsal integument. D–E Photographs of coxal and genital field: D = male; E = female. Scale bars = 100 µm.
Edge Mode Percolation and Equilibration in the Topological Insulator Cadmium Arsenide
Open the record for dataset details and reuse information.
Directed percolation and puff jamming near the transition to pipe turbulence
<p>The onset of turbulence in pipe flow has defied detailed understanding ever since Reynolds' first observations revealed the spatially-heterogeneous nature of the transition. While recent theoretical studies and experiments in simpler, shear-driven flows suggest that the onset of turbulence is a directed percolation non-equilibrium phase transition, whether these findings are generic and apply also to open or pressure-driven flows is unknown. In pipe flow, the extremely long time scales near the transition make direct observations of critical behavior virtually impossible. Here, we circumvent these limitations by experimentally characterizing all pairwise interactions between localized patches of turbulence ("puffs"), and using these interactions as input to renormalization group and computer simulations of minimal models that extrapolate to long length and time scales. We show that the universality class of the transition is directed percolation, from which emerges a jammed phase of puffs above the critical point. The stronger interactions in the jamming regime enable us to explicitly measure the turbulent fraction and confirm model predictions. Our work shows that directed percolation scaling applies beyond simple closed shear flows, and underscores how statistical mechanics can lead to profound, quantitative and predictive insights on turbulent flows and their phases.</p>
Network-Nanostructured ZIF-8 too Enable Percolation for Enhanced Gas Transport (Data Repository)
<p>Data repository </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.