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3,339 results for “heterogeneities”
Table S1: Publication bias and heterogeneity test results among studies in overall and subgroup analyses
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Leveraging Hardware Probes and Acceleration for Testing Heterogeneous Applications
<p>The part of data and code for HFuzz: Leveraging Hardware Probes and Acceleration for Testing Heterogeneous Applications</p>
Raw Data and Scripts used in Regulation of single-cell heterogeneity of capsular polysaccharide synthesis in a human gut symbiont
<p>Raw data used in this publication. Single-cell analysis of promoter inversions reveals differential inversion rates as a determinant of bacterial population heterogeneity.</p> <p> </p> <p>Libx.zip contains raw sequencing reads</p> <p>scripts.zip contains code for analysis of reads and growth curve data</p> <p>SequencingRawDataFilesIndex.xls contains a description of all the raw data in each libx.zip.</p>
Dataset of the paper "Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow"
<p>Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow</p>
Dataset from: Parapatric and sympatric adaptation of Setaria viridis populations in Japan to heterogeneous coastal habitats via trait divergence of plant form, salt spray tolerance and flowering time
<p>This study aimed to determine how coastal variants of<em> </em>plants arise from local populations under natural selection by studying variations in phenotypic variations and survival of <em>Setaria viridis</em> populations inhabiting mosaic environments of two seashores in Japan. <em>S.viridis</em> populations comprised five coastal variants showing significantly higher salt spray tolerance than the inland variant: ST, short and tolerant (common variant); TM, tall and mid-tolerant (Inland Sea); TT, tall and tolerant; PT, prostrate and tolerant; L, extremely late flowering; I, inland and susceptible variants. These variations imply that maritime plants first acquired salt spray tolerance for survival, after which compact plants evolved in habitats where strong winds caused damage from salt spray. Results indicate that diverse intensities of salt spray and winds as well as summer drought generated various coastal variations in parapatry and sympatry.</p>
SCM files for "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization"
<p>Supporting files for manuscript "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization" by N.P. Arnold.</p> <p>The data consists of source code modifications relative to v11.2.0 of the GEOS GCM, modified boundary conditions for running single column model experiments, and netcdf model output from those experiments.</p>
Underlying dataset for figures published in "A tool for evaluating heterogeneity in avidity of polyclonal antibodies"
<p>The dataset consists of all underlying data associated with Li et al. Front Immunol. 2023, 16;14:1049673. (doi: 10.3389/fimmu.2023.1049673.). Each folder corresponds to data shown in a single figure, with the figure number indicated in the folder names. Please refer to the ReadMe.pdf for information on the content of the figure-specific folders.</p>
TDMT solutions from catalog of "A Large Fault Partially Reactivated During Two Contiguous Seismic Sequences in Central Italy: The Role of Geometrical and Frictional Heterogeneities"
<p>Some new TDMT solutions from catalog at the link <a href="https://doi.org/10.5281/zenodo.10801577">https://doi.org/10.5281/zenodo.10801577</a>. The catalog contains events with M > 3.0, that occurred between January 2009 and April 2021, in Campotosto area, Italy. Moment tensor were calculated by applying the Time Domain Moment Tensor technique, originally proposed by Dreger and Helmberger (1993) and Pasyanos et al. (1996) and successively implemented at INGV by Scognamiglio et al. (2009).</p> <p>For every moment tensor the PDF file contains the event location, waveform fits, nodal planes, magnitude, double couple (DC) and compensated linear vector dipole (CLVD) values, variance reduction, six components of moment tensor and station coverage.</p>
Data from: Environmental heterogeneity influences liana community differentiation across a Neotropical rainforest landscape
<p>We examined the variation in the liana community composition and structure across geopedological land units to test the hypothesis that environmental heterogeneity is a driving force in the liana community assembly. At the Los Tuxtlas Tropical Biology Station, SE Mexico (640 ha of tropical rain forest), we sampled all lianas with basal diameter ≥ 1 cm in three 0.5-ha plots established in each of five land units (totaling 15 plots and 7.5 ha). We censused 6055 individuals and 110 species. Overall, the most speciose families were also the most abundant ones. The density and basal area (m<sup>2</sup>) of some dominant liana species differed among land units, and a permutational multivariate analysis of variance (PERMANOVA) and a non-metric multidimensional scaling ordination (NMDS) revealed differences in the presence, density, and basal area of liana species across the landscape. Liana composition and structure were highly heterogeneous among land units, suggesting that variations in soil water availability and relief are key drivers of liana community spatial differentiation. By showing that soil and topography play an important role at the landscape scale, we underscore the ecological relevance of environmental heterogeneity for liana community assembly. In the future, as our ability to assess the local environmental complexity increases, we will gain a better understanding of the liana community assembly process and its heterogeneous distribution in tropical forests.</p>
Mass cytometry data for "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma"
<p>Raw suspension mass cytometry data supporting the publication "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma".</p>
Capturing urban heterogeneity enhances tropical cyclones simulation in Houston (dataset)
<p>WRF simulation output for the paper titled "Capturing urban heterogeneity enhances tropical cyclones simulation in Houston". </p>
Temporal and spatial heterogeneity of atmospheric environmental and meteorological field in the urban street canyon——dataset
<p>Here are the datasets related to the article, "Temporal and spatial heterogeneity of atmospheric environmental and meteorological field in the urban street canyon". The datasets contain two part:</p> <ol> <li>The observation data and the analysis processing scripts (Folder observation).</li> <li>The data used for figures (Folder Fig_data).</li> </ol> <p>The CFD simulation configuration files can be found at https://doi.org/10.5281/zenodo.10841653.</p>
Temporal and spatial heterogeneity of atmospheric environmental and meteorological field in the urban street canyon——CFD configuration
<p>This dataset is about the configuration files for simulations conducted in OpenFOAM-v8 in multiple scenarios, categorized into the "wd" folder for simulating the wind field and the "conc" folder for the concentration field.</p>
Dataset related to "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes"
<p>This is an accompanying dataset to the article with the title "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes" (DOI: <a href="https://doi.org/10.1101/2024.03.15.585196">10.1101/2024.03.15.585196</a>). It contains structural and activity data related to the morphologically detailed model of the rat somatosensory cortex (<a href="https://doi.org/10.1016/j.cell.2015.09.029" target="_blank" rel="noopener">Markram et al., 2015</a>), refered to as "BBP" data in the article. Specificaly, following data items are included:</p> <p><strong>Simulation data:</strong> <code>simulation.xz</code></p> <p><em>"Reliability" protocol:</em></p> <p>Separate folders <code>BlobStimReliability_O1v5-SONATA_<circuit-name></code> with simulation data using the baseline and all manipulated connectomes respectively (see Technical info below), each of which containing:</p> <ul> <li><code>working_dir/connectome.h5</code>: Connectivity matrix in <code>ConnectivityMatrix</code> format, which can be loaded using <a href="https://github.com/BlueBrain/ConnectomeUtilities" target="_blank" rel="noopener">ConnectomeUtilities</a>.</li> <li><code>working_dir/raw_spikes_exc_<sim>.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column). One file for each of the 10 simulations with different simulator seeds, i.e., <sim> is 0..9.</li> <li><code>working_dir/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>working_dir/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> <li><code>working_dir/processed_data_store.h5</code>: Data store in HDF5 format with preprocessed spike signals (e.g., required for <em>Gaussian kernel reliability</em> computations), which contains... <ul> <li><code>spike_signals_exc</code>: Group of simulations datasets "sim_0" to "sim_9", each of which is an array of size <#gids x #t_bins> and contains binned spike signals filtered with a Gaussian kernel.</li> <li><code>sigma</code>: Sigma in ms of Gaussian kernel used for smoothing.</li> <li><code>gids</code>: List of excitatory neuron GIDs.</li> <li><code>t_bins</code>: List of time bins in ms.</li> <li><code>firing_rates</code>: Average firing rates per simulation (0..9; rows) and (excitatory) neuron GID (columns); average firing rates were computed as the inverse of the mean inter-spike interval per neuron.</li> </ul> </li> </ul> <p><em>"Classification" protocol:</em></p> <p>Single folder <code>Toposample_O1v5-SONATA</code> with simulation data using the baseline connectome, stored in a format compatible with the <a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> (<a href="https://doi.org/10.1162/netn_a_00228">Conceição et al., 2022</a>) and <a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> (<a href="https://doi.org/10.1371/journal.pone.0261702">Reimann et al., 2022</a>) pipelines, containing:</p> <ul> <li><code>toposample_input/connectivity.npz</code>: Sparse connectivity matrix in Compressed Sparse Column format , which can be loaded using <code>scipy.sparse.load_npz</code>.</li> <li><code>toposample_input/neuron_info.pickle</code>: Pandas dataframe in pickle format, which can be loaded using <code>pandas.read_pickle</code>, containing additional information about each neuron.</li> <li><code>toposample_input/raw_spikes_exc.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column).</li> <li><code>toposample_input/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>toposample_input/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> </ul> <p> </p> <p><strong>Classification data:</strong> <code>classification.xz</code></p> <p>Selected neighborhoods and classification results for the "PCA" method (<a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> pipeline) as well as the "network_based" method using active subnetworks (<a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> pipeline, using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a> wrapper), stored as:</p> <p><em>"PCA" method:</em></p> <ul> <li><code>PCA/community_database_PCA.pkl</code>: Pandas dataframe in pickle format, containing the binary selection of 50 neighborhood centers (neuron GIDs; rows) for the different selection parameters (columns).</li> <li><code>PCA/classification_results_PCA.pkl</code>: Pandas dataframe in pickle format, containing the classification accuracies for all selection parameters (rows) and 6 cross-validation folds plus mean (columns).</li> </ul> <p><em>"network_based" method:</em></p> <ul> <li><code>network_based/selections_reliability.pkl</code>: Pandas dataframe in pickle format, containing different combinations of first/second selection parameters for the <em>double selection procedure</em> (rows) together with neuron indices (w.r.t. the EXC subcircuit) of the corresponding 50 neighborhood centers (chief0..49; columns).</li> <li><code>network_based/partition_reliability.npy</code>: Partition indices in numpy .npy format required to launch the pipeline using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a>, which is an array of 50 neuron indices (w.r.t. the full circuit!) of the neighborhood centers (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> <li><code>network_based/results/...</code>: Subfolder containing a list of pickle files with the classification results using different featurization parameters, as indicated by the filename. Each file contains a Pandas dataframe with the classification accuracies and numbers of (non-zero) features (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> </ul> <p> </p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Data and Codes of A Deep Learning-Based Consistency Test for Earth System Models on Heterogeneous Many-Core Systems
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Data Sets For: Heterogeneous Structure, Mechanisms of Counterion Exchange, and the Spacer Salt Effect in Complex Molten Salt Mixtures Including LaCl3
<p>Data Sets For: Heterogeneous Structure, Mechanisms of Counterion Exchange, and the Spacer Salt Effect in Complex Molten Salt Mixtures Including LaCl3</p>
Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data
<p>This is the data repository for Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data.</p> <p>Catalog:</p> <ol> <li>Intermediate data used in plotting: CANVAS_source_data.zip <ol> <li>Single cell monocyte data: monocyte.h5ad</li> <li>qPCR table: qPCR_1013.csv</li> <li>NanoString GeoMx cell composition: fig6b.csv</li> </ol> </li> <li>Pretrained CANVAS model: checkpoint-1999.pth</li> </ol> <p>The source IMC data is avaiable at: https://zenodo.org/records/7760826</p>
Crustal heterogeneity onshore central Spitsbergen: insights from new gravity and vintage geophysical data (digital appendix)
<p>This is a digital appendix with data sets related to a scientific paper in G-cubed. It contains gravity, GPR and positioning data from Svalbard.</p> <p> </p> <p><span>Crustal heterogeneity onshore central Spitsbergen: insights from new gravity and vintage geophysical data </span></p> <p><span>Kim Senger<sup>1,2*, </sup>Fenna Ammerlaan<sup>1,3</sup>, Peter Betlem<sup>1,</sup> <sup>†</sup>, Marco Brönner<sup>4</sup>, Marie-Andrée Dumais<sup>4</sup>, Jomar Gellein<sup>4</sup>, Tormod Henningsen<sup>5</sup>, Julian Janocha<sup>1,6</sup>, Erik P. Johannessen<sup>7</sup>, Jonas Liebsch<sup>1,8</sup>, Jakob Machleidt<sup>9,1</sup>, Tereza Mosočiová<sup>1,10,11</sup>, Snorre Olaussen<sup>1</sup>, Bo Olofsson<sup>12</sup>, Nil Rodes<sup>1</sup>, Sofia Rylander<sup>12,1</sup>,<span> </span>Grace E. Shephard<sup>13,14</sup>, Aleksandra Smyrak-Sikora<sup>15</sup>, Juan D. Solano-Acosta<sup>16,1 </sup>and Anna Sterley<sup>11,1</sup></span></p>
Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics: fastq & bam files - Sample S5_Rec
<p>You can find here the fastq and bam files related to the datasets used in the publication: </p> <p>In this particular upload, you can find the fastq (version1) and bam (version2) files of the two replicates of sample S5_Rec (A121573)</p> <p><strong>Valdeolivas, A., Amberg, B., Giroud, N. <em>et al.</em> Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. <em>npj Precis. Onc.</em> 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong></p> <p> </p> <p> </p>
Cataloged fresh craters of "Subsurface heterogeneity of light plains formed by the Orientale basin"
<p>This dataset includes all the fresh impact craters observed in the work. The craters in each region are stored as a separate shapefile. In the attribute table, the "Rock" field is marked as "1" for observed excavated boulders and "0" for no observed boulders. The "Diameter" field represents the crater diameter in meters, and the "Max_EXdepth" field indicates the excavation depth of the crater, also in meters.</p>
ScienceDex guides
Understand access before you commit
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.