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162 results for “invariability”
Figure 10 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 10. Principal component analysis based on growth-independent and growth-invariant characters of neanic chamberlets and nepionts. Grouping by samples (point colours) and groups (background colour of convex hulls). Points with yellow margins represent the position of 'L. group 2' within 'L. group 1'.
Figure 7 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 7. Growth functions of specimens pictured in Fig. 2, left for L. group 1, specimen 98C-1h(616), right for L. group 5, specimen P562(496).
Figure 8 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 8. Growth functions of specimens pictured in Fig. 2, left for L. group 1, specimen 98C-1h(616), right for L. group 5, specimen P562(496).
Figure 6 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 6. Lepidocyclina specimens with measurements of chamberlets (in sectors). 1070 measurements were taken for first and 1163 measurements for the second specimen demonstrating the high time and effort in measuring a single individual. A, 98C-1h(616) and B, P562(496).
Figure 5 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 5. Measurements in equatorial sections: A, spatulate chamberlet; B, arcuate chamberlet; C, nepiont.
Figure 3 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 3. Distribution of samples and assemblages LBF in Loma El Santo section, central Cuba (modified from Torres-Silva et al., 2019).
Figure 2 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 2. Distribution of the samples and LBF assemblages at Loma Candelaria section, western Cuba (modified from Torres-Silva et al., 2019).
Figure 4 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 4. Distribution of samples and LBF assemblages at Loma Viǵıa section, central Cuba (modified from Torres-Silva et al., 2019).
Figure 1. A in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 1. A, schematic tectonic map of western and central Cuba (after Iturralde-Vinent, 1994) with locations of the stratigraphical sections and samples. B, stratigraphical relations of the Eocene units in western and central Cuba slightly modified from Garćıa-Delgado & Torres-Silva (1997). Stratigraphical ranges of the studied sections, A (98LC-1), B (LM-52), C (LM-23), D (P-559, P-562), E (CA-215), F (CA-216).
Isomorph Invariant Dynamic Mechanical Analysis: A Molecular Dynamics Study
<p>This data set contains the data required to reproduce most of the figures in our paper, [arXiv:2204.06962] which will soon be submitted to a journal. Abstract of the paper:</p> <p>We simulate dynamic mechanical analysis experiments for the Kob-Andersen binary Lennard-Jones system. For this, the SLLOD algorithm with time-dependent strain rates is applied to give a sinusoidally varying strain at different densities and temperatures. The starting point is a temperature scan at a fixed reference density. Isomorph theory predicts that for other densities corresponding temperatures can be identified at which the mechanical properties are unchanged when scaled appropriately. We determine the isomorphically equivalent temperatures by analysing how<br> particle forces change upon scaling configurations to the new density. Loss moduli expressed in suitable reduced units are compared for isomorphic state points. When plotted against the unscaled temperatures, these reduced loss curves are observed to collapse indicating the validity of isomorph theory for dynamic mechanical analysis experiments. Two different methods to determine isomorphic temperatures are considered. While one of them breaks down for the largest density rescalings considered in this study, the other one is still applicable in this region. The decorrelation of force vectors upon rescaling is investigated as a possible origin of this effect. Our results demonstrate that the simplification of the phase diagram entailed by isomorph theory for a wide class of system<br> is relevant also for the mechanical properties of glasses.</p> <p> </p> <p> </p>
Data and code for Scale-invariant random geometry from mating of trees: a numerical study
<p>This entry contains the source code, simulation data and data analysis notebooks for the paper</p> <p>T. Budd, A. Castro, "Scale-invariant random geometry from mating of trees: a numerical study." <a href="https://arxiv.org/abs/2207.05355">arXiv:2207.05355</a>.</p> <p>This work is part of START-UP 2018 programme with project number 740.018.017, which is financed by the Dutch Research Council (NWO).</p>
Death anxiety scale (DAS): internal structure and factorial invariance in Peruvian adults
<p><span>Este material forma parte del artículo: "Death<span> </span>anxiety<span> </span>scale<span> </span>(DAS): internal Structure <span>and</span><span> </span>factorial<span> </span><span>invariance</span><span> </span>in Peruvian<span> </span>adults", afiliación del primer autor: Universidad César Vallejo.</span></p> <p> </p>
Informativeness, contingency and time scale invariance in associative learning
<p>Contemporary theories guiding the search for neural mechanisms of learning and memory assume that associative learning results from the temporal pairing of cues and reinforcers resulting in coincident activation of associated neurons, strengthening their synaptic connection. While enduring, this framework has limitations: Temporal-pairing-based models of learning do not fit with many experimental observations and cannot be used to make quantitative predictions about behavior. Here we present behavioral data that supports an alternative, information-theoretic conception: The amount of information that cues provide about the timing of reward delivery predicts behavior. Furthermore, this approach accounts for the rate and depth of both inhibitory and excitatory learning across paradigms and species. We also show that dopamine release in the ventral striatum reflects cue–predicted changes in reinforcement rates consistent with subjects understanding temporal relationships between task events. Our results reshape the conceptual and biological framework for understanding associative learning.<strong><br></strong></p>
Processed Hi-C contact matrices for "Three invariant Hi-C interaction patterns: applications to genome assembly"
<p>Processed Hi-C interaction matrices, saved in numpy npz format.</p> <p>Matrices were processed using Dekker lab cMapping pipeline.</p> <p>Raw sequence data was taken from:</p> <p>Hap1: Haarhuis et al 10.1016/j.cell.2017.04.013</p> <p>IMR90, H1ESC, MESC, MCORTEX: Dixon et al 10.1038/nature11082</p> <p>Worm: Crane et al 10.1038/nature14450</p> <p>Caulobacter: Le et al 10.1126/science.1242059</p> <p> </p>
Quasi-invariance of Tropical Meridional Surface Temperature Gradient in a Wide Range of Climates
<p>Data generated in the study "Quasi-invariance of Tropical Meridional Surface Temperature Gradient in a Wide Range of Climates" are archived here</p>
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
<p>A pre-trained model of the paper "Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections," CoRL 2020.</p>
Source: Photos courtesy of M.M. Le Roux (a–d) and K.S. Mashego (e–f) FIGURE 1: Morphological characters of Thesium confine (a, d, e) and T. durum (b, c, f) showing (a) the scale-like leaves found in both species; (b) involucral bracts that are invariably present in T. durum; (c) longitudinal section of a flower showing the dense hairs on the perianth lobes, stigma opposite the anthers and the twisted placental column; the habit of (d) T. confine and typical herbarium specimens of (e) T. confine and (f) T. durum. in A taxonomic evaluation of the Thesium confine species complex (Santalaceae)
Source: Photos courtesy of M.M. Le Roux (a–d) and K.S. Mashego (e–f) FIGURE 1: Morphological characters of Thesium confine (a, d, e) and T. durum (b, c, f) showing (a) the scale-like leaves found in both species; (b) involucral bracts that are invariably present in T. durum; (c) longitudinal section of a flower showing the dense hairs on the perianth lobes, stigma opposite the anthers and the twisted placental column; the habit of (d) T. confine and typical herbarium specimens of (e) T. confine and (f) T. durum.
Dataset: Invariant categorical color regions across illuminant change coincide with focal colors
<p>Experimental data for a paper titled "Invariant categorical color regions across illuminant change coincide with focal colors“ published in Journal of Vision.</p> <p>This repository contains following data. Please refer to the article for details about the dataset.</p> <p> </p> <p><strong>1. IlluminantsANDReflectances.xlsx</strong></p> <p>This file stores spectral radiance for each illuminant and OSA coordinates (jgL) and reflectance values for 424 OSA color samples.</p> <p> </p> <p><strong>2. Exp1_1_RawData</strong></p> <p>Raw data for Experiment 1-1.</p> <p>This folder has xlsx files, and filename indicates the illuminant condiion.</p> <p>In each xlsx file, different sheets contain a different observer’s data.</p> <p>The number (1-424) corresponds to the number of OSA sample (shown in IlluminantsANDReflectances.xlsx).</p> <p> </p> <p><strong>3. Exp1_2_RawData</strong></p> <p>Raw data for Experiment 1-2. The same format as Exp1_1_RawData.</p> <p> </p> <p><strong>4. Exp2_RawData.xlsx</strong></p> <p>Raw matching data for Experiment 2.</p> <p>The name of each sheet shows the observer & illuminant condition.</p> <p>Each row shows Ljg coordinates of the OSA sample and associated matching result (in RGB and xyL).</p>
Porous Invariants for Linear Systems: POROUS Tool and experimental data
<p>POROUS Tool and experimental data for Porous Invariants for Linear Systems.</p> <p>The code is maintained at https://github.com/davidjpurser/porous-tool</p> <p>Contains:</p> <ul> <li>the code in porous-tool-May2023.zip</li> <li>the results of experiements run on a Dell PowerEdge M620 with 2x Intel Xeon E5-2667 v2 CPUs and 256GB RAM in <ul> <li>document-journal.csv as a summary file, listing each generated instance type, file name, success status, and timing information. Used as input to analysis.py.</li> <li>journal-data.zip containing individual randomly generated instances and their output</li> </ul> </li> </ul> <p>Preliminary results were presented in the paper Porous Invariants. CAV 2021 by Engel Lefaucheux, Joël Ouaknine, David Purser and James Worrell.</p>
Accompanying data for the paper "Reduced order modeling of geometrically nonlinear rotating structures using the direct parametrisation of invariant manifolds"
<p>Links</p><ul><li>isSupplementTo <i>publication-article</i> <a href="https://doi.org/10.46298/jtcam.10430">https://doi.org/10.46298/jtcam.10430</a></li><li>isSupplementedBy <i>software</i> <a href="https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147">https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147</a></li></ul><p>Language</p><ul><li>English</li></ul><p>License</p><ul><li>Creative Commons Attribution 4.0</li></ul><p>Contributions</p><ul><li>Adrien MARTIN carried out the main part of study, defined the examples, performed the numerical simulations and drafted the manuscript;</li><li>Andrea OPRENI and Alessandra VIZZACCARO developed the methodology and built the main parts of the Julia code implementing the reduction method;</li><li>Andrea OPRENI developed the first version of the HBFEM code which has been updated for rotation in collaboration with Adrien MARTIN;</li><li>Marielle DEBEURRE performed all the simulations shown in Appendix C related to the Timoshenko beam model with continuation;</li><li>Loïc SALLES supervised the work, discussed applications to blades, and helped in designing and understanding the twisted plate model;</li><li>Attilio FRANGI supervised the work and help in the development of the methodology;</li><li>Olivier THOMAS helped in all discussions related to the comparisons with the thin beam example and wrote Appendix C;</li><li>Cyril TOUZE supervised the work, carried out most of the writing and developed the methodology;</li></ul><p>All authors read and approved the final manuscript.</p><p>Data collection: period and details</p><ul><li>Datasets produced between September and December 2022</li></ul><p>Funding sources</p><ul><li>Funding from AID (Agence de l'Innovation de Défense), project REMODEL, contract number 2020 65 0057 ENSTA</li></ul><p>Data structure and information</p><ul><li>README.md: Contains the general information concerning this dataset</li></ul><p>Figures</p><ul><li>fig_1: description of the rotating beam</li><li>fig_2(a,b,c,d): Linear characteristics of the rotating cantilever beam</li><li>fig_3(a,b): FRC of the rotating cantilever beam around 1F mode</li><li>fig_4: Convergence of the non-autonomous part of DPIM for the 1F mode</li><li>fig_5(a,b,c,d,e,f): Interpolation of the coefficients of the autonomous ROM</li><li>fig_6(a,c): Hardening/softening behaviour of the rotating beam; fig 6b is a zoom on fig 6a</li><li>fig_7(a,b,c): Comparisons of FRCs obtained from interpolated ROMs with FOM solution</li><li>fig_8a: FRC of the rotating cantilever beam around 2F mode; fig 8b is a zoom of fig 8a</li><li>fig_9(a,b,c,d): fig 9 a-b-c : geometry of the blade and some modes and static displacements; fig 9d : Campbell diagram of the blade</li><li>fig_10: FRC of the twisted plate</li><li>fig_11(a,b,c): Computing time and convergence analysis with respect to mesh refinement for the fan blade</li></ul><p>fig_12(a,b,c,d): FRC of interpolated ROMs with increasing degrees compared to reference solution</p><p>fig_A_1: Campbell diagram of the beam : impact of Coriolis effects</p><ul><li>fig_C_3(a,b,c,d,e,f,g,h,i): Comparison of the results on the beam studied between DPIM and article from Thomas for 1F and 2F modes</li><li>fig_C_2(a, b): Comparison of the results on the beam studied between : DPIM, article from Thomas and results from Debeurre</li></ul>
ScienceDex guides
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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.