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5,155 results for “resolution”
GOIMAI DataSet-Phase-I of high-resolution macroscopic images for the identification of wood species
<p>GOIMAI Phase I is a wood species dataset obtained through magnifying lenses. The inputs needed in our laboratory were a mid-range mobile phone (e.g. Samsung Galaxy S7), a magnifying lens with a universal clamp attached to the lens of the mobile phone (in our case with 24x magnifier) and the wood sample itself.</p> <p>GO IMAI project - Identificación de madera por dispositivo móvil. <br>Convocatoria EI-Agri Programa Nacional de Desarrollo Rural 2014-2020 por el Ministerio de Agricultura, Pesca y Alimentación y el Fondo Europeo Agrícola de Desarrollo Rural (FEADER).</p>
Ship Plume Dispersion in Marseille Port: A High-Resolution CFD Visualization
<p><strong><u>Description</u></strong></p> <p>This video visualizes the results of a high-resolution Computational Fluid Dynamics (CFD) model applied to study ship plume dispersion in the passenger port of Marseille, France. The video focuses on a scenario where a small cruise/pleasure ship docked at the port turns on its main engines in preparation for departure, considering seaborn prevailing wind conditions.</p> <p>The visualization concentrates on two areas. The first is the area around La Major Cathedral, which is close to the waterfront. The second is a street canyon approximately 650 meters downwind of the ship's location. Due to the local urban layout, the plume becomes entrapped within this distant street canyon. Here, the spatial distribution of pollutants can vary even between neighboring floors of a single building. Finally, a rendered video of the plume dispersion is presented, where the urban landscape is visualized to make the dispersion more comprehensible for non-technical and non-scientific audiences.</p> <p>This video was previously published on YouTube at the following link: <a href="https://www.youtube.com/watch?v=HwLFTj1D4LU&ab_channel=SCIPPERProject" target="_blank" rel="noopener">YouTube</a>. </p> <p>All data used to create this video were made available through the EU Horizon 2020 project <a href="https://www.scipper-project.eu/">SCIPPER</a>.</p> <p>The SCIPPER project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement <a href="https://doi.org/10.3030/814893">Nr. 814893</a>. </p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
BreizhSR: multi-temporal cross-sensor super-resolution of satellite imagery
<h1>BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset </h1> <h2>1. Dataset motivation</h2> <p><strong>BreizhSR</strong> is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.</p> <h2>2. Dataset organization</h2> <p>The dataset folder follows the structure detailed below :</p> <p><code>BreizhSR</code><br><code>├── dataset_test.pkl</code><br><code>├── dataset_train.pkl</code><br><code>├── README.md</code><br><code>├── x</code><br><code>├── x_test</code><br><code>├── y</code><br><code>└── y_test</code></p> <p>The <code>README.md</code> file contains the same information as this description.</p> <p>Actual image patches are stored in the <code>x</code> and <code>x_test</code> folders for Sentinel-2 patches, and in the <code>y</code> and <code>y_test</code> folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. <code>8355</code>) that denote the series identifier. Therefore, for the S2 series <code>x/8355</code>, the corresponding SPOT patch is in subfolder <code>y/8355</code>.</p> <p>This organization and additional metadata are described in two <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">Pandas Dataframes</a> : <code>dataset_train.pkl</code> and <code>dataset_test.pkl</code>. These files are Dataframes serialized using the <a href="https://docs.python.org/3/library/pickle.html">pickle Python serialization protocol</a>. The columns available in these Dataframes are described in the table below.</p> <table> <tbody> <tr> <td>x</td> <td>y</td> <td>wkt</td> <td>spot6_name</td> <td>sen2_acquisitions</td> <td>dates_sen2</td> <td>dates_spot6</td> <td>split</td> </tr> <tr> <td>Latitude of the center point (expressed in Lambert 93 CRS)</td> <td>Longitude of the center point (expressed in Lambert 93 CRS)</td> <td>Area of interest geometry in well-known text format</td> <td>Path to the SPOT ground truth</td> <td>Paths to the Sentinel-2 input series</td> <td>Acquisition dates for the Sentinel-2 images</td> <td>Acquisition date for the SPOT ground truth</td> <td>`train` or `test`</td> </tr> </tbody> </table> <h2>3. Data collection and preprocessing</h2> <h3>Sentinel-2</h3> <p>Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.</p> <p>Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.</p> <h3>SPOT-6/7</h3> <p>Orthorectified SPOT data under the Licence Ouverte is collected from the <a href="https://openspot-dinamis.data-terra.org/">DINAMIS</a> platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm <a href="https://www.orfeo-toolbox.org/CookBook/Applications/app_BundleToPerfectSensor.html">Orfeo ToolBox</a>, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.</p> <h2>4. License</h2> <p>SPOT images and the Sentinel-2 Theia L2A products are released under the <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">Licence Ouverte 2.0</a> from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).</p> <h3>Acknowledgements</h3> <p>We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).</p>
High-resolution figures of Amaral et al. 2024
<p>High-resolution figures of Amaral et al. 2024 "Expanding the Mesozoic record of early brachyceran fly larvae, including new larval forms with chimera-type morphologies" in Insects (MDPI).</p>
A regional high-resolution (1/12 degree) coupled ocean-ecosystem model (INCOIS-BIO-ROMS) output for Indian Ocean
<p>The dataset contains sea-surface temperature, sea-surface salinity, sea-surface dissolved inorganic carbon, sea-surface total alkalinity, sea-surface pH, and sea-surface partial pressure of CO2 for the Indian Ocean region. The data is available from 1980 to 2019 on a monthly time scale. Each of these data has a spatial resolution of 1/12º.</p>
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)
<p>This is the experiment data.</p> <p>The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately. </p>
Molecular dynamics trajectories of pYEEI:SH2 recognition, unbiased, at all-atom resolution.
<div> </div> <p>Set of 772 all-atom trajectories simulated from an unbound (apo) configuration of the human p56 -lck tyrosine kinase SH2 domain with its high-specificity phosphopeptide recognition substrate pYEEI (initial structure based on PDB:<a href="https://www.rcsb.org/structure/1LKK">1LKK</a> ). Approximately 24 trajectories spontaneously reach a bound state with ligand RMSD < 2 Â from the crystal. System building and run details are described in [1].</p> <p>A preliminary version of this dataset have been analyzed and discussed in [1] (approx 200 ns per trajectory were available and used in [1]). </p> <p>The trajectories provided here are extended to ~800 ns each, for a total of ~640 μs sampled time. The full dataset is analyzed in [2] with a SOM-based technique.</p> <div> <h2>Notes</h2> </div> <div> <ul> <li>These are all-atom simulations (with TIP3P water). Water molecules have been stripped off from these files (filtered).</li> <li>Not all trajectories have the same length. Some are cut short due to the distributed computing setup.</li> <li>Frame-to-frame interval is 1 ns.</li> </ul> </div> <h2>Acknowledgments</h2> <p>We thank the volunteers of the GPUGRID.net project for donating computing time.</p> <p> </p> <h2>References</h2> <p>[1] T. Giorgino, I. Buch, and G. De Fabritiis. <a href="https://pubs.acs.org/doi/10.1021/ct300003f">Visualizing the Induced Binding of SH2-Phosphopeptide</a>, J. Chem. Theory Comput. 2012, 8, 4, 1171-1175. doi:10.1021/ct300003f</p> <p>[2] Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta. Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics. J. Chem. Phys, 2024. https://doi.org/10.1063/5.0225183 </p> <p> </p> <div> </div> <div> <p> </p> </div>
Issues of the Eclipse Platform Repository with Resolution set to FIXED
<p>In this dataset we can find all the issues of the Eclipse Platform repository where the Resolution attribute is set to FIXED. <br>The dataset contains information about the ID, Title, Description, StartDate( Date when the issue was asigned to its resolutor) and EndDate (Date when the issue was set to FIXED) of the issues in this repository.<br><br>This file does not include issues with a Duration ( EndDate-StartDate) is less than 5 minutes or issues that do not have its title or description set.</p>
Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model
<p>This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.</p>
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</p>
Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities
<p>Dataset associated to the paper "Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities"</p>
Advances in volumetric super-resolution microscopy and single-particle tracking (associated codes and datasets)
<h2>Overview</h2> <p>This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by <a href="https://www.ch.cam.ac.uk/person/sgd46">Sam G. Daly</a> (Yusuf Hamied Department of Chemistry, University of Cambridge).</p> <p>Managed/updated versions my be avalible at <a href="https://github.com/TheLeeLab">https://github.com/TheLeeLab</a>.</p> <p>The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM.</p> <h2>Available Datasets</h2> <h3>Chapter 4</h3> <ol> <li>Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames.</li> <li>Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames.</li> </ol> <h3>Chapter 5</h3> <ol> <li>B cell receptor imaging on a fixed B cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>SPT of the B cell receptor on a live B cell (PALM); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>PD-1 imaging on a fixed T cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed T cell (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> </ol> <h3>Chapter 6</h3> <ol> <li>SPT of ACBD3 in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> <li>SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> </ol> <h2>Available Code</h2> <ol> <li><strong>Autofocus (BeanShell):</strong> Counteracts axial drift in SMLFM experiments.</li> <li><strong>Calibration (BeanShell):</strong> Controls the piezo scanner for axial calibrations in 3D-SMLM.</li> <li><strong>3D Reconstruction (Matlab):</strong> Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub.</li> <li><strong>Fiducial correction (Matlab):</strong> Removes focal drift artifacts from 3D localisation data.</li> <li><strong>Temporal grouping (Python):</strong> Removes multiple single-molecule blinking events.</li> <li><strong>3D tracking (Matlab):</strong> Converts 3D localisations into tracks and calculates diffusion quantities.</li> <li><strong>Matching (Matlab):</strong> Determines PPV, sensitivity, and Jaccard index from localisation data.</li> <li><strong>Membrane curvature (Python):</strong> Determines the frequency of 3D localisations at a given membrane curvature.</li> </ol> <h3><em>Supported by The Royal Society (RGF\EA\181021) </em></h3>
Issues of the Eclipse Platform Repository with Resolution set to FIXED
<p>In this dataset we can find all the issues of the Eclipse Platform repository where the Resolution attribute is set to FIXED. <br>The dataset contains information about the ID, Title, Description, StartDate( Date when the issue was asigned to its resolutor) and EndDate (Date when the issue was set to FIXED) of the issues in this repository.<br><br>This file does not include issues with a Duration ( EndDate-StartDate) is less than 5 minutes or issues that do not have its title or description set.</p>
High-resolution harvester data for estimating rolling resistance and forest trafficability
<p>This study describes an extensive, high-resolution data on rolling resistance coefficient collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50~km of harvester routes and used in the following study:</p> <p>Salmivaara, A., Holmström, E., Kulju, S., Ala-Ilomäki, J., Virjonen, P., Nevalainen, P., Heikkonen, J., and Launiainen, S. 2024.High-resolution harvester data for estimating rolling resistance and forest trafficability. European Journal of Forest Reseach. doi: 10.1007/s10342-024-01717-6</p> <p>The coordinates are in metric system, and the relative position between sites are maintained. However, they have been converted and do not refer to actual locations.</p> <p>Two datasets collected by a Ponsse Ergo harvester are available: </p> <p>1) All non-frost period of 2021, with 8m*8m grid means of rolling resistance ('grid mean rr'), along with grid mean longitudinal inclination of the machine ('grid mean pitch'), time, topographic wetness index ('twi') and binary indication whether a road is within 4m radius from the grid centerpoint ('roads'). </p> <p>2) Data including all 11 test sites with all first pass harvester machine data ('time', 'E_conv', 'N_conv', 'dtorq', 'drpm', 'peng', 'cooling', 'phydr', 'pwheels', 'v_ms', 'fmotive', 'slopeforce', 'normalforce', 'pitch', 'rr') and grid means of spatial data ('grid', 'grid mean rr', 'grid mean pitch', 'roads', 'site', 'slope_rad_terrain', 'bmroot_all'. 'c_bmroot_ratio', 'broadlv_rootbm_ratio', 'p_bmroot_ratio','s_bmroot_ratio', 'age', 'diameter', 'standheight', 'b_vol', 'b_pulp_vol', 'b_timber_vol', 's_vol', 's_pulp_vol', 's_timber_vol', 'p_vol', 'p_pulp_vol', 'p_timber_vol', 'broadlv_vol','broadlv_pulp_vol', 'broadlv_timber_vol', 'canopy_cov', 'broadlv_canopy_cov', 'ba', 'volume', 'dem', 'devMeanElev16', 'devMeanElev8', 'diffMeanElev8', 'erosion risk', 'tri', 'roughness_mag', 'roughness_scale', 'dtw00_5ha', 'dtw_01_0ha', 'dtw_04_0ha', 'dtw_10_0ha', 'twi', 'sat_deficit_doy', vol_moisture_doy, 'bare_rock_areas', 'lakes', 'streams', 'peat', 'topsoil', 'aspect', 'aspect_sin', 'aspect_cos', 'main_class_fertility_class_comb')</p> <p> </p>
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
Inputs for 2D HEC-RAS model for sensitivity analysis of DEM and mesh grid resolutions over part of the Virginia Tech StREAM Lab
<p>Inputs into a 2D HEC-RAS model used in the article written by Elizabeth M. Prior, Nathan Michaelson, Jonathan A. Czuba, Thomas J. Pingel, Valerie A. Thomas, and W. Cully Hession titled "Lidar DEM and computational mesh grid resolutions modify roughness in 2D hydrodynamic models".</p>
Data from: Phylogenomic loci define the generic boundaries of Gochnatieae and improve resolution at the species level in Moquiniastrum (Compositae)
<p>Understanding the evolution of the tribe Gochnatieae (Compositae) has been the subject of considerable effort in the past decade. This is due to the key position of this tribe in the phylogeny of the sunflower family and the corresponding implications for biogeographic and morphological evolution of Compositae. Previous studies have confirmed the monophyly of this tribe as well as most of the genera that belong to it. However, phylogenetic resolution of Gochnatieae at both the genus- and species-level has remained poor. A subset of new phylogenomic loci used in this study has proven effective and has improved phylogenetic resolution in this group. The results of this work demonstrate Gochnatieae is a well-supported clade comprised of nine genera (<em>Anastraphia</em>, <em>Cnicothamnus</em>, <em>Cyclolepis</em>, <em>Gochnatia</em>, <em>Moquiniastrum</em>, <em>Nahuatlea</em>, <em>Pentaphorus</em>, <em>Richterago</em>, <em>Tehuasca</em>). One recently described genus, <em>Vickia</em>, was not included in this study; but its placement in Gochnatieae as a tenth genus in the tribe is well-justified. The monospecific <em>Cyclolepis</em>, which had been circumscribed within the tribe since its inception but was subsequently removed and designated as <em>incertae sedis</em> since 2014, is also shown to belong to Gochnatieae. We confirmed the monophyletic <em>Moquiniastrum</em> with two well-supported subclades. Ancestral area reconstruction analyses show that Gochnatieae originated in Eastern South America about 53 my. Apparently, except for <em>Cyclolepis</em> and <em>Richterago</em>, the ancestors of the other genera of Gochnatieae originated about 44 my from an area that now corresponds to the central Andes. The presence of the genera in the Chaco phytogeographic province, central Chile, and Mexico-United States-Caribbean is a result of dispersal from the central Andes. The ancestral distribution of <em>Moquiniastrum</em> corresponds to a large area comprising Eastern South America and the current central Andes, about 32 my. Ancestral character state reconstruction that included four characters indicates several states associated with complex plant reproductive biology such as gynodioecy, gynomonoecy, and polygamodioecy are derived in Gochnatieae as are heterogamous capitula (in <em>Moquiniastrum</em> and <em>Richterago</em>), dimorphic and subdimorphic corollas (in <em>Cnicothamnus</em>, <em>Moquiniastrum</em>, and <em>Richterago</em>), and the presence of marginal female corollas (in <em>Moquiniastrum</em> and <em>Richterago</em>). Within <em>Moquiniastrum</em>, two subclades (Densicephalum and Polymorphum) exhibit divergent patterns of trait evolution associated with these reproductive characters which suggests this genus can serve as a model to understand the sexual system evolution in plants.</p>
Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data
<p><span>We</span> have developed a tree-based approach to create spatially continuous forest stand mean height maps across China through integrating high-<span>point</span> density, high-precision close-range LiDAR data and multisource remote sensing data. The accuracy analysis of the arithmetic mean height (Ha) and the weighted mean height (Hw) demonstrates the feasibility of the proposed method. A practical framework for forestry investigation based on close-range LiDAR was proposed. The mean values of Ha and Hw are 13.3 ± 3.3 m 11.3 ± 2.9 m on pixel level, respectively. Validation based on LiDAR and field sample data shows that the RMSE values, range from 2.6 to 4.1 m for Ha and 2.9 to 4.3 m for Hw, respectively, indicating that our approach outperforms existing forest canopy height maps derived from area-based approaches. Hopefully, our methods and maps will serve as a foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. </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.