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5,155 results for “resolution”
Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
<p>Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.</p>
Frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery
<p>Results of high-resolution (pixel size 10–15 m) visible satellite imagery analysis, described in Bradtke and Herman 2023 "Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery" (https://tc.copernicus.org/articles/17/2073/2023/).</p> <p>The source data for analysis came from three satellite sensors: ALI (Advanced Land Imager), OLI (Operational Land Imager), and MSI (Multispectral Instrument). </p> <p>The dataset includes:</p> <ol> <li>results of frazil streaks detection in polynya, ice_water (ice =1, water=2, NoData=0)</li> <li>maps of spatially averaged characteristics of ice in polynya (NoData = -100): <ul> <li>ice concentration, Cfs (-),</li> <li>frazil streaks orientation, Thetafs (degrees clockwise from the north)</li> <li>width of frazil streaks, Wfs (m)</li> </ul> </li> <li>maps of spatially averaged wind-wave characteristics obtained from the Fourier analysis (NoData = -100):<br> <ul> <li>peak wave length, Lpeak (m)</li> <li>peak wave direction, Thetapeak (degrees clockwise from the north)</li> </ul> </li> </ol> <p>Data are provided in WGS 1984 / UTM Zone 58S projection (EPSG:32758), in raster grid with 10m resolution, in GeoTIFFformat.</p> <p>File name convention is: sensor_YYYYMMDD_variable.tif</p> <p> </p>
Integrating data from different taxonomic resolutions to better estimate community alpha diversity
<p>Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species-level dataset of presence-absence records with a co-located dataset of group-level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM.</p>
High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"
<p>High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"</p>
Evaluation of Mesoscale Convective Systems in High Resolution E3SMv2
<p>This is the open data resource for the paper "Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations" that submitted to the Journal of Geophysical Research: Atmospheres. </p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
Geodiversity data for Europe at 1-km and 10-km resolutions
<p>The European geodiversity data provides a novel perspective on the diversity of non-living nature over large spatial extents. These data describe geological, pedological, geomorphological, and hydrological diversity, including 78 different geofeatures. Geofeatures refer to individual features that each component of geodiversity (geology, pedology, geomorphology, and hydrology) consists of, such as soil types in the case of pedology. This standardized and accessible geodiversity dataset facilitates comparability for geodiversity research across Europe and can be used for multiple purposes, from studying geodiversity patterns to geodiversity–biodiversity relationship and more. Moreover, the methodology (described in <a href="https://dx.doi.org/10.1098/rsta.2023.0173" target="_blank" rel="noopener">Toivanen et al. 2024</a>) establishes a grid-based approach for quantifying geodiversity, which is suitable for large extents and can be applied in other regions worldwide.</p> <p>This grid-based geodiversity dataset, available at resolutions of 1-km and 10-km, includes ready-to-use georichness variables (as GEOtiff files), and provides information on the presence and coverage of individual geofeatures that can be used to calculate different measures of geodiversity (as csv files). The data on georichness can be utilized in its entirety, representing the overall geodiversity, or with selected geodiversity components as individual richness layers describing geological, pedological, geomorphological or hydrological feature richness. One key objective of this geodiversity data is to provide complimentary environmental variables for biodiversity modelling and conservation studies. However, the choice of geodiversity data (richness or other index), the scale of analysis (1-km or 10-km), and the specific variables (overall geodiversity or individual components) should be determined by the research question and context.</p> <p>This is a dataset from: Toivanen, M. et al. (2024). Geodiversity data for Europe. Philosophical Transactions of the Royal Society A. <a href="https://dx.doi.org/10.1098/rsta.2023.0173">https://dx.doi.org/10.1098/rsta.2023.0173</a> (accepted for publication 2023-10-19)</p>
High-resolution mapping of soil carbon stocks in the western Amazon
<h2>Dear Researchers and Interested Parties,</h2> <p> It is with great enthusiasm that we share our page on Zenodo, where we provide <strong>detailed maps</strong> (30 m resolution) of <strong>soil carbon stocks in Rondônia, Brazil</strong>. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region.</p> <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> <p>We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha<sup>-1</sup>), quantiles (Mg ha<sup>-1</sup>) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>).</p> <p>The entire process was conducted in open source (R Language). The codes and database used <strong>can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a></strong>, and more information about the methodology is available in the following publication:</p> <p>Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. <em>Geoderma Regional</em>, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a></p> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> <p>We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change.</p> <h3>Enhance environmental understanding:</h3> <p>By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies.</p> <h3>Stimulating innovation:</h3> <p>We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon.</p> <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
High-resolution environmental and host-related factors impacting questing Ixodes scapularis at their northern range edge
<p>The geographic range of tick populations has expanded in Canada due to climate warming and the associated poleward range shifts of their vertebrate hosts. Abiotic factors, such as temperature, precipitation, and snow, are known to directly affect tick abundance. Yet, biotic factors, such as the abundance and diversity of mammal hosts, may also alter tick abundance and consequent tick-borne disease risk. Here, we incorporated host surveillance data with high-resolution environmental data to evaluate the combined impact of abiotic and biotic factors on questing <em>Ixodes scapularis </em>abundance in Ontario and Quebec, Canada. High-resolution abiotic factors were derived from remote sensing satellites and meteorological towers, while biotic factors related to mammal hosts were derived from active surveillance data that we collected in the field. Generalized additive models were used to determine the relative importance of abiotic and biotic factors on questing <em>I. scapularis</em> abundance. Combinations of abiotic and biotic factors were identified as important drivers of abundances of questing <em>I. scapularis</em>. Positive and negative linear relationships were found for questing <em>I. scapularis </em>abundance with precipitation and accumulated snow, but no effect was found for the relative abundance of white-footed mice. Positive relationships were also identified between questing <em>I. scapularis </em>abundance with monthly mean precipitation and mammal species richness. Therefore, future studies that assess <em>I. scapularis </em>should incorporate host surveillance data with high-resolution environmental factors to determine the key drivers impacting the<em> </em>abundance and geographic spread of tick populations and tick-borne pathogens.</p>
Supplemental data files for: A potential mode of the lithospheric growth and delamination of orogenic belts: Evidence from high-resolution magnetotelluric data
<p>Data files for a 2-D electrical resistivity model in the Greater Xing'an Range, including the observed MT data, the data fitting, the preferred resistivity model, and the raw data in impedance format.</p>
Roll-to-roll, high-resolution 3D printing of shape-specific particles
<p>Particle fabrication has attracted recent attention due to its diverse applications in bioengineering, drug and vaccine delivery, microfluidics, granular systems, self-assembly, microelectronics, and abrasives. Herein we introduce a scalable, high-resolution, 3D printing technique for the fabrication of shape-specific particles based on roll-to-roll continuous liquid interface production (r2rCLIP). We demonstrate r2rCLIP using single-digit, micron-resolution optics in combination with a continuous roll of film (in lieu of a static platform), enabling the rapidly permutable fabrication and harvesting of shape-specific particles from a variety of materials and complex geometries, including geometries not possible to achieve with advanced mould-based techniques. We demonstrate r2rCLIP production of mouldable and non-mouldable shapes with voxel sizes as small as 2.0 × 2.0 μm<sup>2</sup> in the print plane and 1.1 ± 0.3 μm unsupported thickness, at speeds of up to 1,000,000 particles per day. Such microscopic particles with permutable, intricate designs enable direct integration within biomedical, analytical and advanced materials applications.</p>
Dataset of 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations'
<p>Dataset of the article 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations', recently published in Open Research Europe (DOI 10.12688/openreseurope.17388.1). The code which implements the physics-informed neural network can be found at https://github.com/AlvaroMS90/PINNs-for-high-resolution-weather-reconstruction-from-sparse-weather-stations.</p> <p>Funded by the European Union under action HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships, call HORIZON-MSCA-2021-PF-01 (project number 101059984 with acronym PERSEVERE). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <pre> </pre>
Low resolution local L-sigma calibration dataset
<p>Measurements of the H-alpha luminosity and intrinsic velocity dispersion of 347 H II regions across 9 spiral galaxies. These properties were measured from integral field unit (IFU) spectroscopy taken by the Mulit-Object Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT). The L-sigma relation calibrated from these data is valid for low spectral (R ~ 3000) and spatial (~500 pc) resolution observations of giant H II regions. </p>
Stellar chromospheric activity database of solar-like stars based on the Ca II H and K lines of LAMOST Low-Resolution Spectroscopic Survey: the bolometric and photospheric calibration
<p>This database contains the stellar chromospheric activity parameters of solar-like stars based on the Ca II H and K lines from the spectra of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). We extract 1,122,495 LRS spectra of solar-like stars from the LAMOST Data Release 8 v2.0 and provide the chromospheric activity parameters $S_{\rm tri}$, $S_{\rm MWO}$, $R_{\rm HK}$ and $R'_{\rm HK}$, as well as their uncertainties in the database. $R_{\rm HK}$ and $R'_{\rm HK}$ are derived from the method in the classic literature (denoted with classic) and the method based on the PHOENIX model (denoted with PHOENIX). These stellar chromospheric activity indexes and their uncertainties, along with the relevant spectroscopic parameters from the LAMOST catalogs, are saved in a CSV-format file (name: CaIIHK_Activity_Indexes_LAMOST_DR8_LRS.csv).</p> <p> </p> <p>Columns in the catalog of the database:</p> <p>obsid - Unique observation identifier of LAMOST LRS spectrum</p> <p>obsdate-Spectral observation date</p> <p>fitsname - FITS file name of LAMOST LRS spectrum</p> <p>snrg - Signal-to-noise ratio in the $g$ band ($\mathrm{S/N}_g$) of LAMOST LRS spectrum</p> <p>snrr - Signal-to-noise ratio in the $r$ band ($\mathrm{S/N}_r$) of LAMOST LRS spectrum</p> <p>teff - Effective temperature ($T_\mathrm{eff}$) derived from the LASP (unit: K)</p> <p>teff_err - Uncertainty of $T_\mathrm{eff}$ derived from the LASP (unit: K)</p> <p>logg - Surface gravity ($\log\,g$) derived from the LASP (unit: dex)</p> <p>logg_err - Uncertainty of $\log\,g$ derived from the LASP (unit: dex)</p> <p>feh - Metallicity ([Fe/H]) derived from the LASP (unit: dex)</p> <p>feh_err - Uncertainty of [Fe/H] derived from the LASP (unit: dex)</p> <p>rv - Radial velocity ($V_r$) derived from the LASP (unit: km/s)</p> <p>rv_err - Uncertainty of $V_r$ derived from the LASP (unit: km/s)</p> <p>ra_obs - Right ascension (RA) of fiber pointing (unit: degree)</p> <p>dec_obs - Declination (DEC) of fiber pointing (unit: degree) </p> <p>gaia_source_id - Source identifier in Gaia DR3 catalog</p> <p>gaia_g_mean_mag - $G$ magnitude in Gaia DR3 catalog</p> <p>S_tri* - $S_{\rm tri}$ index of LAMOST LRS </p> <p>S_tri_err* - Uncertainty of $S_{\rm tri}$ </p> <p>S_MWO* - $S_{\rm MWO}$ </p> <p>S_MWO_err* - Uncertainty of $S_{\rm MWO}$ </p> <p>log_R_HK_classic* - $\log\,R_{\rm HK,classic}$ </p> <p>log_R_HK_classic_err* - Uncertainty of $\log\,R_{\rm HK,classic}$ </p> <p>log_R_p_HK_classic* - $\log\,R'_{\rm HK,classic}$ </p> <p>log_R_p_HK_classic_err* - Uncertainty of $\log\,R'_{\rm HK,classic}$ </p> <p>log_R_HK_PHOENIX* - $\log\,R_{\rm HK,PHOENIX}$ </p> <p>log_R_HK_PHOENIX_err* - Uncertainty of $\log\,R_{\rm HK,PHOENIX}$ </p> <p>log_R_p_HK_PHOENIX* - $\log\,R'_{\rm HK,PHOENIX}$ </p> <p>log_R_p_HK_PHOENIX_err* - Uncertainty of $\log\,R'_{\rm HK,PHOENIX}$ </p> <p>(Note: The columns denoted by an asterisk represent the parameters provided by this database, and the remaining columns are collected from the data release of LAMOST DR8 V2.0. If the values are not available, they will be filled with '-9999' in this database.)</p>
Surface drifters and high resolution global simulations mapping of internal tide surface energy
<p>File " gdp_energy.nc " contains surface semidiurnal internal tides binned-averaged energy levels estimated from the Global Drifter Program dataset. </p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides binned-averaged kinetic energy levels estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>For all files semidiurnal signal is obatined from band-pass filtering.</p>
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
<p>A 10 m resolution land cover map of the Tibetan Plateau with 12 vegetation types and 3 non-vegetation types for the year 2022 (TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and Random Forest model using Google Earth Engine platform.</p>
Probing structural superlubricity of two-dimensional water transport with atomic resolution.
<p>Here lies the tabulated data used to create the Figures for the Science manuscript ado1544 titled "Probing structural superlubricity of two-dimensional water transport with atomic resolution".</p>
High-Resolution Beach Profile Time Series: Morecambe Bay, 2007-2022
<p><strong>Description:</strong> This dataset provides a comprehensive collection of beach profile measurements for Morecambe Bay, covering a 16-year period from 2007 to 2022. The data was collected and shared by Sefton Council (Sefton MBC), UK, as part of the coastal monitoring programme. Captured biannually during spring and autumn, the data offers valuable insights into the bay's coastal morphology and dynamics.</p> <p><strong>Data Description:</strong></p> <ul> <li><strong>Beach Transects:</strong> Detailed profiles are provided for various locations along the Morecambe Bay coastline. Station numbers are used to identify each measurement point on a dedicated map (included within the data).</li> <li><strong>Temporal Coverage:</strong> The dataset encompasses measurements conducted biannually, capturing seasonal variations in beach profiles.</li> </ul>
FIGURE 1 in Comparative performance of a multi-locus barcoding approach to enhance taxonomic resolution of New Zealand mosquitoes (Diptera: Culicidae)
FIGURE 1 (Continued)
Presentation in HEARING: High-resolution structural and functional EAR imaging 2023: Three-dimensional vibration of the human tympanic membrane using a scanning laser doppler vibrometer
<p>This is for Bastian Baselt's poster presentation in HEARING: High-resolution structural and functional EAR imaging, Ascona, Switzerland in 2023, and the related data.</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.