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750 results for “heterogeneous data”
Periodic and heterogeneous solid and velocity data used to train and validate CNN models
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mRNA data - Unique miRNome and Transcriptome Profiles Underlie Microvascular Heterogeneity in Mouse Kidney
GEO Series GSE220985. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system
GEO Series GSE181543. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing; Other.
Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities
GEO Series GSE150084. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]
GEO Series GSE150025. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Data for 'The Helmholtz Equation in Heterogeneous and Random Media: Analysis and Numerics'
<p>Part of the computational output in the PhD thesis: Owen Rhys Pembery, 'The Helmholtz Equation in Heterogeneous and Random Media: Analysis and Numerics', University of Bath, 2020.</p>
Data for: Tissue evolution: Mechanical interplay of adhesion, pressure, and heterogeneity
<p>Tissue evolution: mechanical interplay of adhesion, pressure, and heterogeneity</p> <p>Full Data for corresponding publication.</p> <p> </p> <p>All folders are named by the actual simulation values used, not by the rescaled values shown in the paper<br> All folders contain data files, trajectory files are converted and zipped, as well as the executable and the starting configuration file.<br> Start simulations with ./cell_dpd starting_configuration.sconf</p> <p>The simulation for Fig.1 can be found in folder /free_evolution/<br> folder structure: /free_evolution/LXxLYxLZ/GB_F1/DeltaG_alpha/ with LX, LY, LZ simulation box lengths in x,y,z direction, GB and F1 the simulation parameters of the host tissue, DeltaG difference in G of neighbouring species and alpha the relative change of f1 to G of neighbouring species.</p> <p>The simulation for Fig.2 and Fig.S3 can be found in folder /heterogeneity/sharp_treshold/<br> folder structure: /heterogeneity/sharp_treshold/pm/tradeoff/ with mutation probability pm and tradeoff parameter between changes in G and F1.</p> <p>The simulation for Fig.S1 can be found in folder /heterogeneity/division_rate/tradeoff/ with tradeoff parameter between changes in G and F1.</p> <p>The simulation for the division rate simulations of Fig.S2 can be found in folder /pair_competition/12x12x12/40_6.0/division_rate/</p> <p>The simulation for Figs. 4, 5 and S4 can be found in folder /pair_competition/12x12x12/40_6.0/sharp_treshold/<br> folder structure: /pair_competition/LXxLYxLZ/GW_F1W/sharp_treshold/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M).</p> <p>The simulations for Fig.6 can be found in folder /mutationrate/<br> folder structure: /mutationrate/LXxLYxLZ/GW_F1W/pm/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M) and mutation probability pm.</p> <p>The simulations for Fig.7 can be found in folder /survival/<br> folder structure: /survival/LXxLYxLZ/GW_F1W/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M).</p> <p>The simulation for Fig.S5 can be found in folder /unstable/<br> folder structure: /unstable/LXxLYxLZ/GW_F1W/GM/F1M/phi0/sim_number with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M), initial number fraction phi0 and simulation number sim_number</p> <p>Folder /scripts/ contains all scripts to analyze the simulation results, described in the following:</p> <p>get_phi.py : Outputs file with cell number fractions from input file containing absolute cell numbers.<br> Called by : python get_phi.py input output<br> input: Name input file (in all simulations numcells.dat)<br> output: Name output file</p> <p>minimize_uid: Outputs minimized trajectory file as input.min<br> Called by: ./minimize_uid input<br> input: name trajectory file (in all simulations traj.dat)</p> <p>convert2xyz_spec: Outputs trajectory in xyz format as input.xyz for pair competitions with n=2<br> Called by : ./convert2xyz_spec input<br> input: name trajectory file (minimized file from minimize_uid)</p> <p>convert2xyz_new: Outputs trajectory in xyz format as input.xyz for heterogeneity simulations for n=21 species<br> Called by : ./convert2xyz_new input<br> input: name trajectory file (minimized file from minimize_uid)</p> <p>block_average.py : Outputs file with block averaged value from input file.<br> Called by : python block_average.py input output column n_or_t t_n_start t_n_end<br> input: Name input file<br> output: Output file name<br> column: column to be averaged<br> n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> t_n_start: time/line number to start average<br> t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end</p> <p>cluster_analysis.py : Number of clusters of each species, cluster analysis by DB-SCAN algorithm with minimal points=1 and potential cut-off distance as size treshold<br> Called by: python cluster_analysis.py input output<br> input: trajectory file in xyz format<br> output: name of output file</p> <p>neighbour_analysis.py : Analysis of the cell species average cell species of the cells in interaction range at each frame of the trajectory,<br> second column gives average of total neighbours per cell, following two columns for species 0 average number of identical <br> and different cell species, vice versa for species 1 in the next two last columns<br> Called by: python neighbour_analysis.py input output<br> input: trajectory file in xyz format<br> output: name of output file</p> <p>get_average_cluster.py : averages number of clusters over given time/frame number<br> Called by : python get_average_cluster.py input output n_or_t t_n_start t_n_end<br> input: Name input file<br> output: Output file name<br> n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> t_n_start: time/line number to start average<br> t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end<br> get_average_neighbour.py : averages number of clusters over given time/frame number<br> Called by : python get_average_neighbours.py input output n_or_t t_n_start t_n_end<br> input: Name input file<br> output: Output file name<br> n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> t_n_start: time/line number to start average<br> t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end</p> <p><br> Folder /notebooks/ contains all jupyter notebooks to create the plots, starting from that directory as root directory</p> <p>Folder /src/ contains the source code for the individual simulation setups</p> <p>Folder /plots/ contains all plots created with the jupyter notebooks</p>
Supplementary data : Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period
<p>BAM files from exome sequencing of 7 U2OS clonal cells used in the publication titled: Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period.</p> <p>Details of samples names are in 'sample details.txt'</p>
Data related to manuscript titled "Heterogeneous dynamics in partially disordered proteins"
<p>Data related to manuscript titled "Heterogeneous dynamics in partially disordered proteins" by Virtanen et al.</p>
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.
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>
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>
Data set. Quantitative soil characterization using frequency domain electromagnetic induction method in heterogeneous fields
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Identification of Neuroinflammation and Neuroimaging Biomarkers Through Data Driven Artificial Intelligence Techniques for Unraveling the Heterogeneity of Aged Subjects at Risk of Dementia and to Bett
ClinicalTrials.gov study NCT06746909. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
A Study on the Heterogeneity of Efficacy of Interventional Therapy Combined With Systemic Therapy for Liver Cancer Based on Multi-center Real-world Data
ClinicalTrials.gov study NCT07156617. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Secondary Data to Evaluate Sex-based Heterogeneity of GLP-1 Agonists and SGLT2 Inhibitors on Cardiovascular-Kidney-Metabolic Health (CKMH) Outcomes in Real-world Settings (DASH-CKMH)
ClinicalTrials.gov study NCT07188545. IPD Sharing: NO. Countries: 1. Publications: 0.
Meta-analysis of Single-cell RNA-seq Data Reveals the Formation of Tertiary Lymphoid Organ and Mechanism Heterogeneity in Vascular Disease
GEO Series GSE234651. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
smallRNA data - Unique miRNome and Transcriptome Profiles Underlie Microvascular Heterogeneity in Mouse Kidney
GEO Series GSE220986. Mus musculus. 20 samples. Type: Non-coding RNA profiling by high throughput sequencing.
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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.