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Dataset results
277 results for “regional scale”
Type II topoisomerases shape multi-scale 3D chromatin folding in regions of positive supercoils (LaminB1 CnR).
GEO Series GSE255734. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Type II topoisomerases shape multi-scale 3D chromatin folding in regions of positive supercoils (Ttchem-seq).
GEO Series GSE255737. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
GEO Series GSE71279. Homo sapiens; Escherichia coli. 26 samples. Type: Expression profiling by high throughput sequencing; Other.
Type II topoisomerases shape multi-scale 3D chromatin folding in regions of positive supercoils
GEO Series GSE255739. Homo sapiens. 42 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other; Expression profiling by high throughput sequencing.
Multi-scale coupling during magnetopause reconnection: interface between the electron and ion diffusion regions
<p>Output in IDL-save format from four frames of the two particle-in-cell simulations used in the manuscript. See manuscript (Appendix C) for simulation set-up. Unless otherwise specified, each of the following variables are given as NxM matrices, where N is the number of grid cells per row (X axis) and M is the number per column (Z axis). Among other variables, the data files named "frame#.sav" contain the following items, used in the paper:</p> <ul> <li>E[i]: the i (X, Y, or Z) component of the electric field vector</li> <li>B[i]: the i (X, Y, or Z) component of the magnetic field vector</li> <li>Ay: the Y component of the magnetic vector potential</li> <li>V[s][i]: the i (X, Y, or Z) component of the bulk velocity of species s (ions or electrons)</li> <li>den[s]: the number density of species s</li> <li>xx: N-element vector of the x location of each grid cell</li> <li>zz: M-element vector of the z location of each grid cell</li> </ul> <p>One other file, named "frame38_P.sav", gives each of the 6 unique elements of the ion and electron pressure tensor with the variables named as P[s][i][j], where i and j are X, Y, or Z and s is either i for ions or e for electrons. </p>
Data from: Assembly mechanisms determining high species turnover in aquatic communities over regional and continental scales
Niche and neutral processes drive community assembly and metacommunity dynamics, but their relative importance might vary with the spatial scale. The contribution of niche processes is generally expected to increase with increasing spatial extent at a higher rate than that of neutral processes. However, the extent to what community composition is limited by dispersal (usually considered a neutral process) over increasing spatial scales might depend on the dispersal capacity of composing species. To investigate the mechanisms underlying the distribution and diversity of species known to have great powers of dispersal (hundreds of kilometres), we analysed the relative importance of niche processes and dispersal limitation in determining beta-diversity patterns of aquatic plants and cladocerans over regional (up to 300 km) and continental (up to 3300 km) scales. Both taxonomic groups were surveyed in five different European regions and presented extremely high levels of beta-diversity, both within and among regions. High beta-diversity was primarily explained by species replacement (turnover) rather than differences in species richness (i.e. nestedness). Abiotic and biotic variables were the main drivers of community composition. Within some regions, small-scale connectivity and the spatial configuration of sampled communities explained a significant, though smaller, fraction of compositional variation, particularly for aquatic plants. At continental scale (among regions), a significant fraction of compositional variation was explained by a combination of spatial effects (exclusive contribution of regions) and regionally-structured environmental variables. Our results suggest that, although dispersal limitation might affect species composition in some regions, aquatic plant and cladoceran communities are not generally limited by dispersal at the regional scale (up to 300 km). Species sorting mediated by environmental variation might explain the high species turnover of aquatic plants and cladocerans at regional scale, while biogeographic processes enhanced by dispersal limitation among regions might determine the composition of regional biotas.
Multi-scale harmonisation Across Physical and Socio-Economic Characteristics of a City region (MAPSECC): London, UK
<p>A new methodology and comprehensive database (<strong>M</strong>ulti-scale harmonisation <strong>A</strong>cross <strong>P</strong>hysical and <strong>S</strong>ocio-<strong>E</strong>conomic<strong> C</strong>haracteristics of a<strong> C</strong>ity region, <strong>MAPSECC</strong>) is developed that connects physical characteristics of a city (building morphology and materials, land-surface cover) with socio-economic aspects (building function, microenvironments of activity, urban transport infrastructure, residential and workplace populations, human activities), and is demonstrated for London, UK (<strong>MAPSECC: London</strong>). The database fulfils input requirements for dynamic and multi-scale urban modelling approaches. Dataset components combine and harmonise information from primary sources (often government agencies) through novel downscaling and aggregation methods to give a traceable, repeatable methodology. Further details about the processing and methodology can be found here:</p> <ul> <li><span>Hertwig, D., McGrory, M., Paskin, M., Liu, Y., Piano, S.L., Llanwarne, H., Smith, S.T. and Grimmond, S. (2025), Connecting Physical and Socio-Economic Spaces for Multi-Scale Urban Modelling: A Dataset for London. Geoscience Data Journal 12, e289. </span><a href="https://doi.org/10.1002/gdj3.289">https://doi.org/10.1002/gdj3.289 </a></li> </ul> <p><strong><em>Cite the article above together with the dataset DOI in any publications using MAPSECC: London data.</em></strong></p> <h3>Files in this archive</h3> <ul> <li>Documentation <ul> <li>MAPSECC_London_documentation.pdf</li> </ul> </li> <li>Processing grid <ul> <li>Main dataset: London_500m_grid.zip</li> <li>Code: London_500m_grid_code.zip</li> </ul> </li> <li>Land-cover fractions <ul> <li>Main dataset: London_landcover.zip</li> <li>Auxiliary data: London_landcover_auxiliary.zip</li> <li>Code: London_landcover_code.zip</li> </ul> </li> <li>Building typologies (with population statistics) <ul> <li>Main dataset: Building_typologies.zip</li> <li>Auxiliary data: Building_typologies_auxiliary.zip</li> <li>Code: Building_typologies_code.zip</li> </ul> </li> <li>Building material parameters <ul> <li>Main dataset: Materials_layer_info.zip, Materials_layer_processed.zip, Materials_parameters.zip</li> <li>Code: Materials_code.zip</li> </ul> </li> <li>Human activity profiles <ul> <li>Main dataset: UK_TUS2014-15_activity_profiles.zip</li> <li>Auxiliary data: Activity_profiles_auxiliary.zip</li> <li>Code: Activity_profiles_code.zip</li> </ul> </li> <li>Transport database <ul> <li>Main dataset: London_transport_database.zip</li> <li>Code: London_transport_code.zip</li> </ul> </li> <li>Road lengths by type <ul> <li>Main dataset: London_roads_by_type.zip</li> <li>Auxiliary data: London_roads_auxiliary.zip</li> <li>Code: London_roads_code.zip</li> </ul> </li> <li>Spatial attractors <ul> <li>Main dataset: London_attractors.zip</li> <li>Auxiliary data: London_attractors_auxiliary.zip</li> <li>Code: London_attractors_code.zip</li> </ul> </li> <li>Disclaimer notice <ul> <li>disclaimer_note.txt</li> </ul> </li> </ul>
Dataset for manuscript "Regional variability of aerosol impacts on clouds and radiation in global kilometer-scale simulations"
<p>This dataset includes two Jupyterlab book python script and a folder of compressed data. (latest version includes updated script to fix mising variable)</p> <p>Due to the size of the dataset we are unable to include the raw simulation output but have instead inluded all datasets required to plot the figures.</p> <p>The python script "original_analysis_script.ipynb" contains the code that was used to perform the analysis on the outputs from the simulations. This is not intended for public use.</p> <p>The python script "figure_plotting_scripts.ipynb" is a cut-down verison of the original code that can be used to plot the figures as presented in the manuscript. All neccessary data to achieve this can be found in the compressed folder "data_arrays". </p> <p>The scripts will run on Jupyterlab book. Providing the user has the neccessary python modules available the plotting script can be run by amending the paths for the figure outout directory and the uncompressed "data_arrays" directory. These paths can be found at the top of the script.</p> <p> </p>
Development and Validation of a Regional Multi-scale System for the Prediction of the Patient Flow in the Emergencies and the Need for Hospitalization
ClinicalTrials.gov study NCT03051737. IPD Sharing: NO. Countries: 1. Publications: 0.
Emergency Department Triage in a Resource Constrained Setting: Application of the World Health Organization Triage Scale in Regional Papua New Guinea
ClinicalTrials.gov study NCT04098705. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Assembly mechanisms determining high species turnover in aquatic communities over regional and continental scales
Open the record for dataset details and reuse information.
Type II topoisomerases shape multi-scale 3D chromatin folding in regions of positive supercoils (GapRUN).
GEO Series GSE255730. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Type II topoisomerases shape multi-scale 3D chromatin folding in regions of positive supercoils (HiC).
GEO Series GSE255731. Homo sapiens. 12 samples. Type: Other.
Type II topoisomerases shape multi-scale 3D chromatin folding in regions of positive supercoils (microC).
GEO Series GSE255736. Homo sapiens. 16 samples. Type: Other.
FIGURE. Phylogenetic tree derived from Bayesian analysis, based on nrITS1-5.8S-ITS2 region data. Posterior probability (PP> 0.95) values from the Bayesian analysis are added at the nodes. The scale bar represents the number of nucleotide changes per site. (T) indicates the type specimen for this species. The new species are in bold. in Four new species of Entoloma (Entolomataceae, Agaricomycetes) subgenera Cyanula and Claudopus from Vietnam and their phylogenetic position
FIGURE. Phylogenetic tree derived from Bayesian analysis, based on nrITS1-5.8S-ITS2 region data. Posterior probability (PP> 0.95) values from the Bayesian analysis are added at the nodes. The scale bar represents the number of nucleotide changes per site. (T) indicates the type specimen for this species. The new species are in bold.
Integrating a genome-wide association study with a large-scale transcriptome analysis to predict genetic regions influencing the glycemic index and texture in rice
GEO Series GSE123616. Oryza sativa Indica Group. 21 samples. Type: Expression profiling by array.
Scale-dependent effects of urbanization on avian diversity in a Neotropical region
<p>RDA_birds script: This is the script that contains the Redundancy Analysis (RDA), whose database is named: "Rda_birds.txt". RDA is a multivariate statistical technique that combines elements of Principal Component Analysis (PCA) and Multiple Regression to explore the relationships between sets of variables. This technique is often used to analyze data where there are multiple responses (dependent variables) and multiple predictors (independent variables). In this case, we used this statistical technique to explore multivariate relationships between sets of variables, where we explored how local environmental and landscape characteristics (independent variables) can explain patterns in bird species communities (dependent variables).</p> <p>MOBr_birds script: This is the script that contains the Biodiversity Measurement Analysis (Mob), whose database is named: "Mobr_birds". Mob is a multi-scale approach to dissecting the roles of species abundance distribution (SAD), total community abundance and spatial aggregation within species along continuous environmental gradients.</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.