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2,214 results for “Strength”
Dataset for "Impact of C-S-H seeding on hydration and strength of slag blended cement"
<p>The data come along with the publication "Impact of C-S-H seeding on hydration and strength of slag blended cement" submitted to Cement and Concrete Research. 2022</p> <p>The file 00_mixdesign.csv contains the indexing and the labels, the same as Table 5 from the manuscript.</p> <p>Units of the values are not always specified in the dataset, but can be found in the manuscript.</p> <p>To understand the structure and labels of the dataset, please refer to the paper. </p> <p>April. 2022<br> Xuerun Li, Julien Bizzozero and Christoph Hesse<br> BASF Construction Additives GmbH, Dr-Albert-Frank-Strasse 32, 83308 Trostberg, Germany</p>
A sequential two-step priming scheme reproduces diversity in synaptic strength and short-term plasticity
<p>Please consult the uploaded word file '<a href="https://zenodo.org/api/files/274d8784-bd00-47da-b7e7-7350c0aa3659/figure_4_public.docx">figure_4_public.docx</a>' for a description.</p>
Strength-mass scaling law governs mass distribution inside honey bee swarms
<p>To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000–10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the ≈1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.</p>
Fault strength and rupture process controlled by fault surface topography
<p>Experimental source data for the study "Fault strength and rupture process controlled by fault surface topography"</p>
Shear strength characterization and statistical modelling of 12 hardwood timber species from the Congo Basin
Open the record for dataset details and reuse information.
Data for 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'
<p>All data supporting the paper 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'. Includes gifs (movies) of the high temperature in-situ EBSD experiments and the scripts used for analysis.</p>
Interfacial bonding strength along normal and shear direction of FMLs
<p>Interfacial bonding strength along normal and shear direction - lap_shear_Sandblasted at 1,3,5 bar</p>
Breitenfeld et al. (2024) Dataset: Effect of Particle Size on Raman Signal Strength of Silicate Minerals
<p>This dataset includes a CSV file with the raw Raman spectra of six mineral samples (two forsterites, augite, dioside, labradorite, and bytownite) each at 10 particle size ranges. It also includes a CSV file with the cooresponding metdata (minerals names and particle size ranges in microns) for each spectrum.</p>
Decoupled hydroclimate of Central and Southwestern Iran controlled by the strength of southerly-westerly jets during Marine Isotope Stage 3
<p><span>Abstract</span></p> <p><span>The regional impact of abrupt glacial climate variability remains poorly constrained for arid southwestern Asia, particularly winter dynamics during Marine Isotope Stage 3, due to limited paleoarchives in the Middle East. Here, we present novel continuous speleothem records of δ<sup>18</sup>O and δ<sup>13</sup>C for Southwestern and Central Iran, spanning the interval ~50-30 ka. Stable-isotope signals in the two stalagmites are generally uncorrelated and do not exhibit a consistent response</span><span> </span><span>to Greenland stadials or interstadials; however, both show a positive δ<sup>18</sup>O excursion that coincides with Heinrich event 4. We explore the potential mechanisms for intermittent coupling of speleothem </span><span>δ</span><sup><span>18</span></sup><span>O across Iran through isotope-enabled atmospheric modelling outputs, from which we utilize the spatial </span><span>δ</span><sup><span>18</span></sup><span>O gradient as a proxy for wintertime westerly vs. southerly jet strength. Our results suggest that during Heinrich event 4 and several Greenland stadials, stronger westerly winds enhanced Mediterranean moisture contributions to both sites and reduced aridity in southern Iran.</span></p>
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, vol. 271, p. 108171, 2022.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Data from "Crossed-state bowing and the strength of binary dislocation junctions"
<p>Contains data and analysis files used in the journal publication:</p> <p>Duan I. and Sills R. B., Crossed-state bowing and the strength of binary dislocation junctions, J. Mech. Phys. Sol., Vol. 190, 105696, 2024.</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105696" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jmps.2024.105696</span></a></p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Tensile strength of Ganoderma pure mycelium - raw data
<p>The dataset consists of tensile strength values of pure mycelium of <em>Ganoderma lingzhi</em> and <em>Ganoderma sessile</em> produced using liquid state fermentation. The substrate for liquid state fermentation was supplemented with 0,5% of lignin and 2% of lignin (reference was 0% lignin). Raw data from universal testing machine are also provided. Another variant is a modification of obtained mycelium leather with glycerol treatment (immersion of samples in 20% glycerol solution for 24 hours).</p>
Features of speed-strength training of young taekwondo athletes
<p><strong><em>Abstract.</em></strong> <strong><em>Sanсharova N</em></strong><strong><em>.</em></strong><strong><em>, Ogar G</em></strong><strong><em>.</em></strong><strong><em>, Сych D</em></strong><strong><em>. Features of speed-strength training of young taekwondo athletes. </em></strong><strong><em>Purpose: </em></strong><em>is to work out methodology of speed-power preparation for young taekwondo athletes 14 and experimentally to ground her efficiency. <strong>Material and methods:</strong> Twenty young taekwondo athletes, 14 years of age, participated in the experiment. They were divided into 2 groups: experimental, training sessions in which were conducted according to an experimental technique and a control one, the athletes of which were engaged in the usual technique common in most sections of taekwondo. Each group had 10 subjects. The effectiveness of the methodology was determined by the dynamics of the fitness indicators of young athletes, where the overall physical preparedness and special shock preparedness of the subjects were assessed. Research methods: analysis of scientific sources, pedagogical observations, pedagogical testing, video shooting, mathematical and statistical processing of research results.</em> <strong><em>Results.</em></strong><em> a study of the dynamics of the fitness of young taekwondo athletes of the experimental group, during the experiment, showed a significant improvement in the results in all the tests (p<0</em><em>,</em><em>05-0</em><em>,</em><em>001). In young taekwondo athletes of the control group, during the experiment, significant changes occurred only in four tests out of nine (p<0</em><em>,</em><em>05-0</em><em>,</em><em>01). Thus, the dynamics of positive changes in the preparedness of athletes of the control group is significantly inferior to positive changes in the experimental group.</em> <strong><em>Conclusions:</em></strong><em> The experimental methodology of speed-strength training of young taekwondo athletes with the use of a wide range of means was developed, the effectiveness of its influence on the special shock training of athletes is proved by the results of the study.</em> <em>Thus, it can be argued that the speed-strength orientation of the training process of young taekwondo athletes allows athletes to better realize their technical and tactical potential. Trainers, teachers, during the training sessions, should not be limited to competitive and special means, especially when working with young athletes.</em></p>
Figure 1 in A study on European anchovy (Engraulis encrasicolus) swimbladder with some considerations on conventionally used target strength
Figure 1. An example showing the postprocessing image analyses of anchovy swimbladder.
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
Compressive strength measurements on binary and ternary blended cementitious paste specimens produced with seawater-mixing
<p>The following file consists of data from the published article Rathnarajan et al., (2024) Comprehensive evaluation of early-age hydration and compressive strength development in seawater-mixed binary and ternary cementitious systems published in Archives of Civil and Mechanical Engineering. </p> <p>The excel file contains of compressive strength data of cement paste specimens of size 2 cm. </p> <p>1. From column B to Column N: Mix proportion details including the type of binders, total binder content, and clinker substitution level with SCMs are provided</p> <p>2. From Column O to Column AM: Compressive strength data of the paste specimens were listed out for FW-mixed and SW-mixed cementitious systems for the testing ages, 2, 7, 14, 28 and 90 days. </p> <p>3. Also the % change in compressive strength is calculated by calculating the difference in strength between SW-mixed and FW-mixed specimen divided by strength of FW-mixed specimens</p> <p>SYMBOLS AND NOTATIONS</p> <p>PC - CEM I <br>PF - CEM I + Fly ash <br>PS - CEM I + Slag<br>PM - CEM I + Metakaolin<br>PL - CEM I + Limestone<br>FW - Fresh water mixed <br>SW - Seawater mixed<br>SCM - Supplementary cementitious materials like Fly ash, slag, metakaolin and Limestone<br>SD - Standard deviation<br>%change - Percentage change in compressive strength with respect to strength of FW-mixed paste cubes. </p>
Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate their Absorption Strength and Onset of Absorption
<p>The uploaded datasets contain complex refractive indices of polymer thin films and a glass substrate and are published in connection with the following article:</p> <p>Kamptner, Scharber, Schiek.<br>Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate their Absorption Strength and Onset of Absorption.<br>ChemPhysChem 2024.</p> <p><a href="https://doi.org/10.1002/cphc.202400233">https://doi.org/10.1002/cphc.202400233</a></p> <p> </p> <p><strong>F8BT</strong> (or PFBT): Poly(9,9-dioctylfluorene-alt-benzothiadiazole).</p> <p><strong>MDMO-PPV</strong> (or OC1C10-PPV): Poly-[2-(3,7-dimethyloctyloxy)-5-methyloxy]-para-phenylene-vinylene.</p> <p><strong>PBDB-T-2F</strong> (or PBDB-T-F, PBDB-TF, PM6): Poly[(2,6-(4,8-bis(5-(2-ethylhexyl-3-fluoro)thiophen-2-yl)-benzo[1,2-b:4,5-b’]dithiophene))-alt-(5,5-(1’,3’-di-2-thienyl-5’,7’-bis(2-ethylhexyl)benzo[1’,2’-c:4’,5’-c’]dithiophene-4,8-dione)].</p> <p><strong>PDCBT</strong>: Poly[2,2''''-bis[[(2-butyloctyl)oxy]carbonyl][2,2':5',2'':5'',2'''-quaterthiophene] -5,5'''-diyl].</p> <p><strong>PTB7</strong>: Poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl][3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]].</p> <p><strong>ZZ50</strong> (or c-PCPDTBT): Poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)].</p> <p><strong>float glass</strong> objective slide (Marienfeld) "tin" and "air" side.</p>
Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects
<p>This uploads contains the datasets, phylogenetic tree and associated R code used to generate the results reported in the article: "Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects" published in Proceedings of the National Academy of Sciences USA (2024).<br>A detailed explanation of datasetS1 can be found in the supplementary data of the article. </p>
zaplotnik/Hadley-cell-strength: Hadley cell strength
<p>Timeseries of Hadley cell strength in the period 1979-2018 in ERA5 and ERA-Interim reanalyses. The timeseries and their trends are derived from different measures of HC strength.</p>
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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.