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3,339
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ShareScore release 0.9.0
Dataset results
3,339 results for “heterogeneities”
Single-cell RNA sequencing of lymph node stromal cells reveals niche-associated heterogeneity
GEO Series GSE112903. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Heterogeneous Cardiac- and Neural Crest-Derived Aortic Smooth Muscle Cells have Similar Transcriptional Changes after TGFβ Signaling Disruption [scRNA-seq-of-human-aortic-media-vascular-cells]
GEO Series GSE253934. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
High-throughput, Microscope-based Sorting to Dissect Cellular Heterogeneity
GEO Series GSE141030. Homo sapiens. 31 samples. Type: Expression profiling by high throughput sequencing; Other.
Clonal tracing reveals the contribution of both cancer-intrinsic and -extrinsic mechanisms to the heterogeneity of responses to immune checkpoint blockade
GEO Series GSE139476. Mus musculus. 26 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Single-cell transcriptome sequencing reveals the cellular heterogeneity of small intestine tissue in celiac disease
GEO Series GSE282570. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade
GEO Series GSE234159. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Single-Cell Transcriptome Profiling Reveals Intratumoral Heterogeneity and Molecular Features in Breast Cancer
GEO Series GSE195861. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Single Cell Dissection of Transcriptional Heterogeneity in HPV Positive and Negative Cervical Cancer
GEO Series GSE171894. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Single cell analysis reveals mechanisms of heterogeneity and plasticity of leukemia initiating cells [inDrop]
GEO Series GSE147860. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Structural Analysis of the Ash2L/Dpy-30 Complex Reveals a Heterogeneity in H3K4 Methylation
GEO Series GSE112061. Saccharomyces cerevisiae. 17 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles
GEO Series GSE156138. Homo sapiens. 162 samples. Type: Expression profiling by high throughput sequencing.
Tractable mouse TNBC models capture the heterogeneous tumor immune microenvironment and adaptation to PD-L1 blockade
GEO Series GSE292146. Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing.
Decoding the heterogeneity of human undifferentiated spermatogonia reveals RAS-dependent regulation of stem cell fate
GEO Series GSE297876. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Single cell RNA-sequencing reveals cellular heterogeneity and trajectories of lineage specification during murine embryonic limb development
GEO Series GSE142425. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Catalogs and stress history for "stress dependent b value variations in a heterogeneous rate-and-state fault model"
<p>Catalogs of synthetic seismicity and stress history used in the manuscrit "stress dependent b value variations in a heterogeneous rate-and-state fault model".</p>
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>
Temporal Sampling Schemes and Role of Flux Measurements for Subsurface Heterogeneous Characterization in Groundwater Basins Using Hydraulic Tomography
<p>This file contains the data for this manuscript, including the animations of transient groundwater flow fields for scenarios A and B, the summary of transient HT inversion results, and the comparison between head inversion and head & flux inversion results.</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>
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.