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221 results for “multi-scale”
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.
Single-cell multi-scale footprinting reveals the modular organization of DNA regulatory elements
GEO Series GSE216464. Mus musculus; Homo sapiens. 41 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Multi-scale simulation of flow through a wind farm during a frontal passage event
<p>Animation from a multi-scale simulation of flow through a wind farm showing hub-height wind speed during a frontal passage event. The simulation was performed using the Weather Research and Forecasting (WRF) model using an embedded generalized actuator disk (GAD) wind turbine model. This animation is included as supplementary material for the manuscript "Multi-scale simulation of wind farm performance during a frontal passage" published in<em> <a href="https://www.mdpi.com/2073-4433/11/3/245#">Atmosphere</a></em><a href="https://www.mdpi.com/2073-4433/11/3/245#"> 2020 11(3), 245</a>, Special Issue "Modeling of Atmospheric Boundary Layers at Turbulence-Resolving Grid Spacings." This work was prepared by LLNL under Contract DE-AC52-07NA27344.</p>
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: Strong effects of coral species on the diversity and structure of reef fish communities: a multi-scale analysis
While there is increasing evidence for habitat specialization in coral reef fishes, the extent to which different corals support different fish communities is not well understood. Here we quantitatively assess the relative importance of different coral species in structuring fish communities and evaluate whether sampling scale and coral colony size affect the perceived strength of fish-habitat relationships. Fish communities present on colonies of 8 coral species (Porites cylindrica, Echinopora horrida, Hydnophora rigida, Stylophora pistillata, Seriatopora hystrix, Acropora formosa, A. tenuis and A. millepora) were examined in the Lizard Island lagoon. Additionally, the differences in fish communities supported by 3 coral species (P. cylindrica, E. horrida, H. rigida) were investigated at 3 spatial scales of sampling (2x2 m, 1x1 m, 0.5x0.5 m). Substantial differences in fish communities were observed across the different coral species, with E. horrida and H. rigida supporting the most fish species and individuals. Coral species explained more of the variability in fish species richness (20.9-53.6%), than in fish abundance (0-15%). Most coral species supported distinctive fish communities, with dissimilarities ranging from 50 to 90%. For 3 focal coral species, a greater amount of total variation in fish species richness and fish abundance was evident at a larger scale of sampling. Together, these results indicate that the structure of reef fish communities is finely tuned to coral species. Loss of preferred coral species could have profound effects on reef fish biodiversity, more so than would be predicted on the basis of declining coral cover alone.
Identification of beneficial and detrimental bacteria that impact sorghum responses to drought using multi-scale and multi-system microbiome comparisons
<p><strong>Background: </strong>Drought is a major abiotic stress that limits agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere and may lead to enrichment of specific groups of microbes, such as <em>Actinobacteria</em>. How these changes ultimately affect plant health is not well understood. In parallel, model systems have been used to tease apart the specific interactions between plants and single, or small groups of microbes. However, translating this work into crop species and achieving increased crop yields within noisy field settings remains a challenge. Thus, the next scientific leap forward in microbiome research must cross the great lab-to-field divide. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key beneficial and detrimental, root associated microbes that robustly affect drought stressed plant phenotypes.</p> <p> </p> <p><strong>Results: </strong>Fifty-three bacterial strains, originally characterized for association with <em>Arabidopsis</em>, were applied to sorghum seeds and their effect on root growth was monitored for seven days. Two <em>Arthrobacter </em>strains, members of the <em>Actinobacteria </em>phylum, caused root growth inhibition (RGI) in <em>Arabidopsis</em> and sorghum. In the context of synthetic communities, strains of <em>Variovorax</em> were able to protect both <em>Arabidopsis </em>and sorghum from the RGI caused by <em>Arthrobacter</em>. As a transitional system, we tested the synthetic communities through a 24-day high-throughput sorghum phenotyping assay and found that during drought stress, plants colonized by <em>Arthrobacter</em> were significantly smaller and had reduced leaf water content as compared to control plants. However, plants colonized by both <em>Arthrobacter</em> and <em>Variovorax</em> performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across well-watered and drought conditions. Drought responsive microbes were identified, including an enrichment in <em>Actinobacteria</em>, consistent with previous findings. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a newly developed method and were then able to observe that the abundance of <em>Arthrobacter</em> strains negatively correlated with plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of high confidence bacterial taxa that positively associated with plant growth under drought stress.</p> <p> </p> <p><strong>Conclusions: </strong>A three-tiered experimental system connected reductionist and ecological approaches and identified beneficial and deleterious bacterial strains for sorghum under drought stress.</p>
Data for "Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought"
<p>This dataset contains data described in '<em>Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought</em>' (<a href="doi.org/10.1038/s43247-024-01448-x">publication link</a>). These data are a subset of those collected across the 50,000 ha region of interest (designation: GIL) across and beyond Gilé National Park, Mozambique. In particular, these data concern a ~350 ha subsection (designation: GIL04). See the Materials and Methods section of the paper for a description of how these data were collected. Additionally, data used for creating the graphs and charts of the manuscript are included. This dataset comprises:</p> <ul> <li><strong>Conventional inventory measurements:</strong> <ul> <li>Digitised forest inventory of plot GIL04-01, including automatic correction of taxonomic information, and attribution of basic wood density (.csv)</li> </ul> </li> <li><strong>Terrestrial laser scanning measurements and derived products:</strong> <ul> <li>0007MZ_GIL04-01_4587*_81616*.laz: 4 x 10 m tiled TLS point clouds</li> <li>0007MZ_GIL04-01_t974.laz: point cloud of an individual tree segmented from the above tiles</li> <li>0007MZ_GIL04-01_t974.mat: quantitative structural model constructed from the above individual tree point cloud</li> <li>0007MZ_GIL04-01_L4_AGB_10m.tiff: gridded estimate (10 m resolution) of TLS-derived aboveground biomass across GIL04-01</li> </ul> </li> <li><strong>Unoccupied aerial vehicle laser scanning measurements and derived products:</strong> <ul> <li>0007MZ_GIL04_4587*_81616*.laz: 4 x 50 m tiled UAV-LS point clouds overlapping the above TLS point clouds</li> <li>0007MZ_GIL04_L3*.tiff: UAV-LS-derived metrics of forest structure across GIL04 (10 m resolution unless stated otherwise)</li> <li>CHM: canopy height and digital elevation model (1 m resolution)</li> <li>RH: 100 relative height metrics in 1% intervals</li> <li>structure_metrics: 14 structural metrics (see paper for details)</li> <li>voxelvolumes: 3D voxel occupancy rates in 1 m bands from 0 to 50 m above terrain</li> <li>0007MZ_GIL04_L4_AGB.tiff: gridded estimate (10 m resolution) of predicted above-ground biomass across GIL04 (10 m resolution)</li> </ul> </li> <li><strong>Aerial laser scanning measurements and derived products:</strong> <ul> <li>0007MZ_GIL_458700_8161600.laz: 1 x 100m tiled ALS point cloud coincident with above UAV-LS point clouds</li> <li>0007MZ_GIL_L3*_clipped.tif: ALS-derived metrics of forest structure across GIL04, in the same format as above UAV-LS metrics</li> <li>0007MZ_GIL_L4_AGB_clipped.tif: gridded estimate (10 m resolution) of predicted above-ground biomass across GIL04 (10 m resolution)</li> </ul> </li> <li><strong>Data for manuscript graphs:</strong> <ul> <li>.csv files containing the data to reproduce Fig.3-5</li> </ul> </li> </ul>
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>
A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres
<p>XRD-CT data presented in the manuscript: "A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres" by Clement Jacquot et al. The h5 files contained the integrated and reshaped diffraction datas as well as a native 2theta x axis.</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.
Multi-scale Spatio-Temporal Analysis of Research Data
ClinicalTrials.gov study NCT07189312. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Multi-scale Modeling for Predictive Characterization of Ligaments and Grafts Behavior in ACL Reconstruction
ClinicalTrials.gov study NCT06058494. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Image-based Multi-scale Modeling Framework of the Cardiopulmonary System: Longitudinal Calibration and Assessment of Therapies in Pediatric Pulmonary Hypertension
ClinicalTrials.gov study NCT03564522. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multi-scale Analysis of Physiological Brain Networks
ClinicalTrials.gov study NCT03912155. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multi-Scale Analysis of Phenotypes in Heart Failure (MAP-HEART)
ClinicalTrials.gov study NCT06280820. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Strong effects of coral species on the diversity and structure of reef fish communities: a multi-scale analysis
Open the record for dataset details and reuse information.
Data from: Biodiversity conservation in agriculture requires a multi-scale approach
Open the record for dataset details and reuse information.
Multi-scale chromatin footprinting reveals wide-spread alterations to the structure of DNA regulatory elements [SHARE-Seq]
GEO Series GSE216404. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Aerobicity stimulon in Escherichia coli revealed using multi-scale computational systems biology of adapted respiratory variants
GEO Series GSE291717. Escherichia coli str. K-12 substr. MG1655. 40 samples. Type: Expression profiling by high throughput sequencing.
Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution
GEO Series GSE59377. Escherichia coli str. K-12 substr. MG1655. 10 samples. Type: Expression profiling by high throughput sequencing.
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.