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3,339 results for “heterogeneities”
Data from: Correlates of rate heterogeneity in avian ecomorphological traits
Heterogeneity in rates of trait evolution is widespread, but it remains unclear which processes drive fast and slow character divergence across global radiations. Here, we test multiple hypotheses for explaining rate variation in an ecomorphological trait (beak shape) across a globally distributed group (birds). We find low support that variation in evolutionary rates of species is correlated with life history, environmental mutagenic factors, range size, number of competitors, or living on islands. Indeed, after controlling for the negative effect of species' age, 80% of variation in species‐specific evolutionary rates remains unexplained. At the clade level, high evolutionary rates are associated with unusual phenotypes or high species richness. Taken together, these results imply that macroevolutionary rates of ecomorphological traits are governed by both ecological opportunity in distinct adaptive zones and niche differentiation among closely related species.
Data from: The genetics of phenotypic plasticity. XII. Temporal and spatial heterogeneity
In order to understand empirical patterns of phenotypic plasticity, we need to explore the complexities of environmental heterogeneity and how it interacts with cue reliability. I consider both temporal and spatial variation separately and in combination, the timing of temporal variation relative to development , the timing of movement relative to selection, and two different patterns of movement: stepping-stone and island. Among-generation temporal heterogeneity favors plasticity, while within-generation heterogeneity can result in cue unreliability. In general, spatial variation more strongly favors plasticity than temporal variation, and island migration more strongly favors plasticity than stepping-stone migration. Negative correlations among environments between the time of development and selection can result in seemingly maladaptive reaction norms. The effects of higher dispersal rates depends on the life history stage when dispersal occurs and the pattern of environmental heterogeneity. Thus, patterns of environmental heterogeneity can be complex and can interact in unforeseen ways to affect cue reliability. Proper interpretation of patterns of trait plasticity require consideration of the ecology and biology of the organism. More information on actual cue reliability and the ecological and developmental context of trait plasticity is needed.
Data from: Diversification in temporally heterogeneous environments: effect of the grain in experimental bacterial populations.
Although theory established the necessary conditions for diversification in temporally heterogeneous environments, empirical evidence remains controversial. One possible explanation is the difficulty of designing experiments including the relevant range of temporal grains and the appropriate environmental tradeoffs. Here, we experimentally explore the impact of the grain on the diversification of the bacterium Pseudomonas fluorescens SBW25 in a temporally fluctuating environment by including twenty different pairs of environments and four temporal grains. In general, higher levels of diversity were observed at intermediate temporal grains. This resulted in part from the enhanced capacity of disruptive selection to generate negative genotypic correlations in performance at intermediate grains. However, the evolution of reciprocal specialization was an uncommon outcome. Although temporal heterogeneity is in theory less powerful than spatial heterogeneity to generate and maintain diversity, our results show that diversification under temporal heterogeneity is possible provided appropriate environmental grains.
Microstructural heterogeneity in rate-dependent plasticity of multiphase titanium alloys
<p>This datasheet contains original data for figures 8 and 9 in the paper. If readers need further information, please feel free to contact: zhen.zhang@imperial.ac.uk</p>
Genetic Heterogeneity of Early-Onset Colorectal Cancer in Asian Populations: A Comprehensive Systematic Review
<p>Genetic Heterogeneity of Early-Onset Colorectal Cancer in Asian Populations: A Comprehensive Systematic Review</p>
Individual heterogeneity in ixodid tick infestation and prevalence of Borrelia burgdorferi sensu lato in a northern community of small mammalian hosts
<p>Dataset and scripts for our paper "Individual heterogeneity in ixodid tick infestation and prevalence of <i>Borrelia burgdorferi</i> sensu lato in a northern community of small mammalian hosts" in Oecologia</p>
Scripts for the publication "HONO production via the heterogeneous reaction between NO2 and diesel soot: role of environmentally persistent free radicals"
<p>Access only for reviewers until manuscript acceptance.</p>
Supporting data—Heterogeneous basal thermal conditions underpinning the Adélie-George V Coast, East Antarctica
<p>Processed data and logistic regression analysis results from manuscript: Dawson et al., 2024, Heterogeneous basal thermal conditions underpinning the Adélie-George V Coast, East Antarctica, GRL. Refer to the README for more information on each file.</p>
A human liver cell atlas reveals heterogeneity and epithelial progenitors
<p>Contains data that was used for the shiny app.</p> <ul> <li>pairwise_comparisons.zip:</li> </ul> <p>Contains all pairwise comparisons of clusters using the RaceID function diffexpnb.</p> <p> </p> <ul> <li>shiny__sc_ndata__pairwise_comparisons_of_all_clusters_Human_Liver_Atlas_Aizarani_et_al_Nature_2019.csv.gz:</li> </ul> <p>Contains the ndata slot from the sc object (see RaceID package) that was used for the shiny application.</p>
Developmental origins of cell heterogeneity in the human lung
<p>The images correspond to the raw data of a modified version of the manuscript "Developmental origins of cell heterogeneity in the human lung" with doi: https://doi.org/10.1101/2022.01.11.475631. File names indicate the corresponding figures.</p>
Flow channelling variability in transit times and tortuosity in a fractured rock model with small scale heterogeneity
<p>Description for file "simulation-particle-data-all-cases".</p> <p>First row is header information, as follows: </p> <p>Column 1, label "avf"<br> The variance of fracture aperture fields (m2)</p> <p>Column 2, label "sdx"<br> The correlation length factor of fracture aperture fields (-)</p> <p>Column 3, label "t"<br> Particle transit time (yr)</p> <p>Column 4, label "log_t"<br> Base-10 logarithm of transit time </p> <p>Column 5, label "l"<br> Particle trajectory length (m)</p> <p>Column 6, label "tort"<br> Particle trajectory tortuosity (-)</p>
The presence of nanoparticles in aqueous droplets containing plant-derived biopolymers play a role in heterogeneous ice nucleation
<p>Dataset for the paper titled "The presence of nanoparticles in aqueous droplets containing<br>plant-derived biopolymers play a role in heterogeneous ice nucleation".</p> <p>Dataset contains freezing data (freezing temperatures recorded with FINC), normalized freezing data (n_p, n_s and n_m), and particle size information (NTA data).</p>
Reactive Transport in Heterogeneous Porous Media Under Different Peclet Numbers
<p>In our recent paper “Reactive Transport in Heterogeneous Porous Media Under Different Peclet Numbers“, we study the synergistic effects of the Peclet number and the length scale of medium heterogeneity on the evolution of bimolecular reactive transport between mobile and immobile species. We performed a suite of numerical simulations at the Darcy scale that quantify the instantaneous, irreversible bimolecular reaction , under various transport conditions (Peclet numbers) and porous media configurations (correlation lengths).</p> <p> </p> <p><strong>Numerical Methods</strong></p> <p>We used the following steps to obtain the results:</p> <p>1. Generation of the hydraulic conductivity fields – we used a widely tested sequential Gaussian simulator (Gómez-Hernández & Journel, 1993). The software is written in C and available to download via <a href="https://wiki.52north.org/AI_GEOSTATS/SWGCOSIM3D">https://wiki.52north.org/AI_GEOSTATS/SWGCOSIM3D</a>. In the file ‘real20.mat’, we show an example of the generated 20 realizations, from the software, for <span class="math-tex">\(\ell\)</span>=1 ,<span class="math-tex">\(\sigma^2\)</span>=1. </p> <p>2. Determination of the flow field – we solve the Darcy equation (for each of the hydraulic conductivity fields), using an open-source code MRST (Lie, 2016). The software is available to download via <a href="https://www.sintef.no/projectweb/mrst/">https://www.sintef.no/projectweb/mrst/</a>. In the file ‘real20_solutions.mat’, we show the flow field solutions (for the different realizations from ‘real20.mat’) under different Peclet numbers. For each realization and Peclet, we store the solution of the velocity components (first column), the velocity magnitude (second column) and the hydraulic conductivity field (third column).</p> <p>3. Chemical transport – was modeled through the Langevin equation, where movement by advection and diffusion were taken into account. Chemical reaction between the different chemical species was modeled via the reaction-radius approach (Edery, Porta, Guadagnini, Scher, & Berkowitz, 2016).</p> <p><br> <strong>Results</strong></p> <p>The data of the published figures can be download from the following files:</p> <p><strong>Fig2.txt</strong> -- processed simulation results shown in Figure 2.</p> <p><strong>Fig3.txt</strong> -- processed simulation results shown in Figure 3a,b.</p> <p><strong>Fig4.txt </strong>-- processed simulation results shown in Figure 4.</p> <p><strong>Fig5.txt </strong>-- processed simulation results shown in Figure 5.</p> <p><strong>FigS1.png </strong>-- First and second statistical moments of the generated conductivity fields, for different correlation lengths. The left axis displays the first moment of the generated fields (ln(K)), and the right axis shows the second moment (ln)). The small circles represent individual realizations, and the large circles indicate the mean value among realizations.</p> <p><strong>FigS2.png </strong>-- Sensitivity analysis of the power law parameters (a) and (b) (see equation (1) in the main text), among realizations; mean values are represented by gray circles, and the standard deviations by the vertical black lines. (c) and (d) show the standard deviations of (a) and (b), respectively, in percentage. Note that among the different realizations, the standard deviation does not exceed 3%. </p> <p> </p> <p><strong>References</strong></p> <p>[1] Edery, Y., Porta, G. M., Guadagnini, A., Scher, H., & Berkowitz, B. (2016). Characterization of Bimolecular Reactive Transport in Heterogeneous Porous Media. Transport in Porous Media. https://doi.org/10.1007/s11242-016-0684-0</p> <p>[2] Gómez-Hernández, J. J., & Journel, A. G. (1993). Joint Sequential Simulation of MultiGaussian Fields. https://doi.org/10.1007/978-94-011-1739-5_8</p> <p>[3] Lie, K.-A. (2016). User Guide for the MATLAB Reservoir Simulation Toolbox (MRST). In A. Soares (Ed.), An Introduction to Reservoir Simulation Using MATLAB. Oslo, Norway: SINTEF ICT, Department of Applied Mathematics. </p> <p> </p> <p> </p>
Joint analysis of single-cell RNA sequencing and bulk transcriptome reveals the heterogeneity of the urea cycle of astrocytes in glioblastoma
Open the record for dataset details and reuse information.
Replication package for Real Effects of Financial Distress: The Role of Heterogeneity
<p>The package contains synthetic data and codes to replicate the figures and tables in the paper and appendix. Please refer to the readme file for further details.</p>
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
<p>Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.</p>
Raw data of the heterogeneous Hegselmann-Krause model on network ensembles
<p># Raw data of the heterogeneous Hegselmann-Krause model on network ensembles<br> This is the raw data underlying the results of the article *«On the effects of over-compromising: heterogeneity and network effects on a bounded confidence opinion dynamics model.»*.</p> <p>For each measured combination of the parameters, there is one gzipped file. The parameters are:</p> <p> - Lower and upper bounds of the confidence interval, [ε_l, ε_u].<br> - Topology: the different types of networks and the average degree with which the networks are generated.<br> - System size<br> - Number of realizations for the parameter combination<br> <br> The single files follow a naming scheme of `data_HK_uni[{eps_l},{eps_u}]_topo={topology}_N={N}_trajrecord=0_{m}real.dat.gz`, where:</p> <p> - `{eps_l},{eps_u}` are the values of the lower and upper bounds of the confidence interval.<br> - `{topology}` contains the type of network and the average degree. The possibilities are `BA_k=10`, `ER_c=10`, `sl1`, `sl2`, and `sl3`.<br> - `N` is the system size. The sizes are powers of two.<br> - `trajrecord=0` signals the fact that file contains only the final state.<br> - `{m}` is the number of realizations.</p> <p># Data format<br> Each file contains the final state of each realization back to back. Each final state is encoded as three lines:</p> <p> - The convergence time is a single integer with a line prefix '\# iterations:'<br> - The positions of all clusters in opinion space with a line prefix '\# ' (unsorted)<br> - The number of agents in each of the clusters without a line prefix</p> <p># Folders structure<br> The files are organized as follows:</p> <p> - **`phase_plots.tar`**: contains the data for the different phase plots (full exploration of the [ε_l, ε_u] space) with `N=16384` and `m=100` realizations.<br> - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10)<br> - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10)<br> - **`SL`** contains the data for Square lattice with first, second and third nearest neighbors (k=4, 8, 12)<br> - **`swipes.tar`** contains the data for the finite size effects study at fixed ε_l with `m=1000` realizations.<br> - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10) with ε_l = 0.05<br> - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10) with ε_l = 0.05<br> - **`SL`** contains the data for Square lattice with third nearest neighbors (k=12) with ε_l =0.03<br> - the different videos referenced in the main text and the SM follow various naming schemes:<br> - **`scatter3D_el_eu_Smax_uni_{topology}_N=16384.mp4`**: 360° rotation of the 3D visualisation of the data leading the average phase plots.<br> - **`scatter2D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: evolution of the scatter plot leading the finite size study as a function of N.<br> - **`scatter3D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: same as before, but in 3D where the Z-axis is the extremism.<br> - **`scatter_x0_xt_{topology}_N={N}_{realization_type}.mp4`**: time evolution of the scatter plot of the opinion at time `t` versus initial opinion, color-coded with the extremism. {realization_type} can be mild, skewed or U-turn.<br> - **`traj_2D_SL_k=12_N=16384_{realization_type}.mp4`**: because of the spatial embedding, the time evolution of those realizations on the Square Lattice can be visualized in 2D.</p> <p># Python example for reading the format<br> An example script, which visualizes <S\> vs ε_u graph for the largest size of the ER case, with a function to read this format is given in `example.py`.</p>
BACKGROUND FACTORS AFFECTING ALPHA HETEROGENITY ON THE REVISED LIFE ORIENTATION TEST (LOT-R)
<p>Data and syntax for reliability generalization cronbach alpha Life Orientation Test (LOT-R) </p>
Minimum dataset for FUSCC HER2-low breast cacer: https://doi.org/10.5281/zenodo.8103633 (Dai et.al., Molecular features and clinical implications of the heterogeneity in Chinese patients with HER2-low breast cancer. Nature Communications, 2023.)
<p>Minimum dataset for FUSCC HER2-low breast cacer: https://doi.org/10.5281/zenodo.8103633 (Dai et.al., Molecular features and clinical implications of the heterogeneity in Chinese patients with HER2-low breast cancer. Nature Communications, 2023. https://doi.org/10.1038/s41467-023-40715-x)</p>
Korpus der Staatsexamensarbeit "Diskursive Konstruktionen der Verwendung von geschlechterinklusiver Sprache in sozialen Medien: Identifizierung von Mustern der Ablehnung durch heterogene Gruppierungen sprachlicher Akteure und Akteurinnen."
<p>Der für die vorliegende Diskursanalyse relevante Datensatz setzt sich aus den digitalen Korpora der Telegram-Kanäle @Gender-Wahn, @Stoppt den Gender-Wahnsinn und @GenderWelten zusammen. Die Datenerhebung erfolgt anhand der Export-Funktion auf Telegram, die erlaubt, den gesamten Chatverlauf ab der Erstellung des jeweiligen Kanals nachzuvollziehen. Die Daten wurden anhand der Anwendung SketchEngine ausgewertet. </p>
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.