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9,882 results for “Scale”
msd-errors - Data for scaling figures
Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks). <br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/6c81f0e8e5ee08bb2eaebd520ceb59213c5026f5">this GitHub repo</a>.
A catalogue of meter-scale craters in Chang'e-5 landing area
<p>Contents of this file :</p> <p> </p> <p>1. <strong>Craters mapped from the 0.5 m/pixel and 0.05 m/pixel DOM in CE-5 landing area.</strong> (File with the name 'Information about all craters in CE-5 landing area.xls')</p> <p> </p> <p>2. <strong>Depth of craters measured from the 0.1 m/pixel DEM.</strong> (File with the name 'Information about depth-measured craters.xls')</p>
Spatial scale evaluation of forecast flood inundation maps
<p>Spatial scale evaluation of forecast flood inundation maps, data and code</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for a new scale-selective approach to forecast flood map evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- JBA Consulting Flood Foresight forecast flood maps used in the study. </p> <p>Helen Hooker. (2022). Spatial scale evaluation of forecast flood inundation maps (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6011882</p> <p>Related publications:</p> <p>Spatial scale evaluation of forecast flood inundation maps; 2022; Journal of Hydrology (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
Strain specific Genome scale metabolic models for 1011 Saccharomyces cerevisiae
<p>This is generated strain specific genome scale metabolic models for 1011 S. cerevisiae strains. This is linked with paper: Lu, H. et al. <em>A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.</em> Nature Communications 10, 3586 (2019). <a href="https://doi.org/10.1038/s41467-019-11581-3">doi:10.1038/s41467-019-11581-3</a></p> <p> </p>
Large-scale comparison of machine learning algorithms for target prediction of natural products
<p>Supplement Materials of the article named "Large-scale comparison of machine learning algorithms for target prediction of natural products".</p>
Data from: Assessing canalisation of intraspecific variation on a macroevolutionary scale: the case of crinoid arms through the Phanerozoic
[No abstract entered]
J1/LM Scaling MD Traj a
<p>J1/LM Scaling MD Traj a</p> <p>With AMBER topology file and NetCDF trajectories. Scaling factor is labelled in the name.</p>
Scale model
*Caio Mario* crying on the ruins of Carthage Author: *Michele Tripisciano* Location: private collection - Caltanissetta From a project by: Anna Tiziana Amato Cotogno, Vincenzo Duminuco, Giovanni Gruttadauria Source: Objaverse 1.0 / Sketchfab
Figure 1 from: Dasari S (2016) Studying the effect of Ruthenium on High Temperature Mechanical Properties of Nickel Based Superalloys and Determining the Universal Behavior of Ruthenium at Atomic Scale with respect to alloying elements, Stress and Temperature. Research Ideas and Outcomes 2: e10714. https://doi.org/10.3897/rio.2.e10714
Figure 1 - Chemical composition of TMS 138 alloy.
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>
Coexistence mechanisms of carnivores revealed at a fine behavioral scale within the mountains of southwest China
<p><span>Understanding mechanisms of species coexistence is critical in biodiversity research, with behavior playing a key role as species respond to internal and external environments, revealing crucial insights into species interactions and coexistence mechanisms. Carnivore communities are integral components of ecosystems, regulating ecosystem structure and function. However, there is a lack of research from a behavioral ecology perspective on coexistence mechanisms within carnivore communities, leading to an incomplete understanding of their complex interspecies interactions. This study investigates behavioral strategy differences between top and secondary predators in the carnivore community of Xinlong, China. From March 2022 to June 2023, we surveyed behavioral changes of five carnivore species—leopard (<em>Panthera pardus</em>), wolf (<em>Canis lupus</em>), yellow-throated marten (<em>Mustela sibirica</em>), red fox (<em>Vulpes vulpes</em>), and leopard cat (<em>Prionailurus bengalensis</em>)—using infrared cameras. We analyzed habitat impacts on behavioral hierarchy using linear mixed-effects models.</span></p>
Data from: Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India
<p>This dataset contains the filtered and processed data files used in the analyses for the paper titled "Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India". These are <code>.Rdata</code> files that can be readily imported into the R environment (all analyses were performed in the R environment using RStudio).</p> <p>It is important to note that these data files were derived from publicly available datasets, and that the entire analytical workflow is reproducible (see <a href="https://github.com/rikudoukarthik/covid-ebirding/blob/main/README.md">the Github repository</a> for more details). Below is a summary of the datasets used:</p> <h2>Data</h2> <p>The analysis is centred around eBird data from India, which is publicly available but is a very large file.</p> <ul> <li><a href="https://ebird.org/data/download/ebd" rel="nofollow"><em>eBird Basic Dataset (EBD)</em></a><em>, relMay-2022</em>: The current version of this dataset is much more recent and contains data after May 2022, but filtering it should produce a fairly similar dataset to the ones used in this study, with only minor changes. (The processed version of this EBD ready for analyses is <code>data0_MY_d_slice.RData</code>).</li> <li><em>Shapefiles</em>: <code>maps_sf.RData</code> contains admin unit polygons, and <code>grids_st_sf.RData</code> contains country-wide grids of multiple resolutions.</li> <li><a href="https://lpdaac.usgs.gov/products/mcd12q1v006/" rel="nofollow"><em>MODIS Land Cover Type Product MCD12Q1</em></a>: The processed data is available as <code>rast_UNU.RData</code>, but deleting this before starting the analysis will force fresh LULC data files from MODIS to be produced from the script.</li> <li><em>Timeline of COVID classification</em>: Available <code>covid_classification.csv</code>. Classification of the timeline of interest into various COVID categories.</li> </ul> <h2>Abstract</h2> <p>Many parts of the world lack the large and coordinated volunteer networks required for systematic monitoring of bird populations. In these regions, citizen science (CS) programmes offer an alternative with their semi-structured data, but the utility of these data is contingent on how, where, and how comparably birdwatchers watch birds, year on year. Trends inferred from the data can be confounded during years when birdwatchers may behave differently, such as during the COVID-19 pandemic. We wanted to ascertain how the data uploaded from India to one such CS platform, eBird, were impacted by this deadly global pandemic. To understand whether eBird data from the pandemic years in India are comparable to data from adjacent years, we explored several characteristics of the data, such as how often people watched birds in groups or at public locations, at multiple spatial and temporal scales. We found that the volume of data generated increased during the pandemic years 2020--21 compared to 2019. Data characteristics changed largely only during the peak pandemic months (April--May 2020 and April--May 2021) associated with high fatality rates and strict lockdowns. These changes in data characteristics (e.g., greater site fidelity and less group birding) were possibly due to the decreased human mobility and social interaction in these periods. The data from the remainder of these restrictive years remained similar to those of the adjacent years, thereby reducing the impact of the aberrant peak months on any annual inference. Our findings show that birdwatchers in India as contributors to CS rapidly returned to their pre-pandemic behaviour, and that the effects of the pandemic on birdwatching effort and birdwatcher behaviour are scale- and context-dependent. In summary, eBird data from the pandemic years in India remain useful for abundance trend estimation and similar large-scale applications, but will benefit from preliminary data quality checks when utilised at a fine scale.</p>
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>
Artistanal and Small-scale Mining (ASM) sites in the Eastern DRC
<p>This is the ground truth dataset created while working on my MSc thesis 'Using high-resolution satellite imagery and deep learning to map artisanal mining spatial extent in the DRC'. The tiles with ASM sites are present in this dataset.</p>
Flow estimates example for k = 50 - Information-Theoretic Sensor Placement for Large-Scale Sewer Networks
<p>This dataset is used to generate Figure 7 without running the simulations in the paper Information-Theoretic Sensor Placement for Large-Scale Sewer Networks.</p>
Flow channelling variability in transit times and tortuosity in a fractured rock model with small scale heterogeneity
<p>Description for file "simulation-particle-data-all-cases".</p> <p>First row is header information, as follows: </p> <p>Column 1, label "avf"<br> The variance of fracture aperture fields (m2)</p> <p>Column 2, label "sdx"<br> The correlation length factor of fracture aperture fields (-)</p> <p>Column 3, label "t"<br> Particle transit time (yr)</p> <p>Column 4, label "log_t"<br> Base-10 logarithm of transit time </p> <p>Column 5, label "l"<br> Particle trajectory length (m)</p> <p>Column 6, label "tort"<br> Particle trajectory tortuosity (-)</p>
Supplemental Data Files for Advances in Organ-Specific Dosimetry Models for Radionuclides and Radiopharmaceuticals at Their Macro And Micro Scales
<p>Data sets for Aims 1 and 2 of my dissertation. Both aims contain three excel spreadsheets for a total of 6 files with the computed specific absorbed fractions and S-values.</p>
Geometries for "Reduced Scaling Correlated Natural Transition Orbitals for Multilevel Coupled Cluster Calculations"
<p>Formaldehyde, formaldehyde-in-water, and azobenzene dye geometries used in "Reduced Scaling Correlated Natural Transition Orbitals for Multilevel Coupled Cluster Calculations" in xyz-format.</p>
Figure 2 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 2 Adult of Prionotropis rhodanica.
Figure 7 from: Hardy NB, Williams DJ (2018) Doubling the known endemic species diversity of New Caledonian armored scale insects (Hemiptera, Diaspididae). ZooKeys 782: 11-47. https://doi.org/10.3897/zookeys.782.27938
Figure 7 a Adult female of Greeniellacasuarinae sp. n. with b enlargement of pygidium.
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.