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395 results for “tiling”
Experimental 3D convex hull tiling of a wild moss specimen ( Rhytidiadelphus or Eurhynchium ?) embedded in 1% agarose
<p>Experimental 3D convex hull tiling of a wild moss specimen ( Rhytidiadelphus or Eurhynchium ?) embedded in 1% agarose</p> <p>Raw data consists of 18 tiles of varying number of Z stacks, depending on where the 3D convex hull points were determined.</p> <p>Images acquired on a Zeiss Lightsheet Z1 using a W Plan-Apochromat 20x/1.0 UV-VIS objective simultaneous dual camera acquisition of autofluorescence under 488nm and 561nm excitation.</p> <p>Original voxel sizes are (x,y,z) = (0.326, 0.326, 2.0) microns</p> <p>Data provided here are tiles fused using BigStitcher, downsampled to the following voxel sizes</p> <p>- Moss_Fused_Downsampled.tif: (4, 4, 24) microns<br> - Moss_Fused_Downsampled_Anisotropy_2.tif: (12, 12, 24) microns</p> <p>Sample acquisition, sample preparation, imaging and stitching performed by Olivier Burri, at the Ecole Polytechnique Fédérale de Lausanne BioImaging and Optics Platform (EPFL - SV - PTECH - PTBIOP)</p>
Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"
<p>This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials” by M. Doškář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference.</p> <p>This repository contains:</p> <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> <p><strong>Numerics</strong></p> <p>All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Rokoš, O., Peerlings, R. H. J., Doškář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>.</p> <p>The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>.</p> <p>Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles.</p> <p>Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.)</p> <p><strong>Experimental data</strong></p> <p>This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts.</p> <p><strong>3D models</strong></p> <p>The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes:</p> <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> <p><strong>Robotics</strong></p> <p>The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand.</p> <p><strong>Acknowledgement</strong></p> <p>The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
Extract from the glazed tile heritage database of Covilhã-Portugal (12 elements)
<p>Extract from the tile heritage database of Covilhã - Portugal: geographic and alphanumeric data of 12 buildings with facades fully or partially covered with tiles, of heritage interest (registered in the SIPA), located in the municipality of Covilhã.</p> <p>The information is related to the buildings and tile compositions on its facades.</p> <p>Building data</p> <p>Building location: country, region, district, municipality, parish, street and house number, Lon WGS84, Lat WGS84, X ETRS89, Y ETRS89, Z elevation. SIPA: designation, identification number, link to SIPA data. Building identification: if it does not exist, an ID must be assigned. Protection and conditioning: world heritage, built heritage, benefits from a protection zone, in the process of classification, in study, non-existent. Public interest, national monument, municipal interest. Property type: public, private. Ownership: ownership information. Case number at municipal services: number ID. Typology: residence, industrial, commercial, religious, panel or other. Occupation: property occupation. Architectural style: dominant, secondary. Other decorative elements than tiles: if existing. General building condition (exterior): good, reasonable, bad, doom. Interventions chronology: if existing. Main moments of design, application, conservation, remodeling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p> <p>Glazed tile data</p> <p>Tile location: facade, inside building. Tile identification: ID number. Construction period: main construction periods. Local context: brief description of the glazed tile object surroundings, in its geophysical, historical, and socio-cultural aspects. Classification 1: coating, ceramic panel, toponymical plate or other. Classification 2: facade, patio, external staircase, balcony, wall, wall panel, small religious panel, caption. Classification 3: pattern, repeat composition, figurative, ornamental, or loose figure. Iconography: religion, biblical, magic, mysticism, nature, human being – humanity, society - civilization – culture, history, ancient history, literature, classical mythology, abstract ideas and concepts, abstract - non-figurative art. Monochrome or polychromatic: monochrome, polychromatic. Objective and succinct tile description: From general to particular: building, space, glazed tile, and section. Material (ceramic product): faience, stone dust, stoneware, porcelain, terracotta. Tile decoration technique: information on tile manufacture, divided into conformation, decoration and application. Composition dimensions: height x width (meters). Tile dimensions: height x width (centimetres). Tile condition: good, reasonable, bad. Authorship: company, artist, painter or other. Authorship nationality: country. Interventions chronology: if existing. Main moments of design, application, conservation, remodelling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p>
Bar graphs of DEMIX database tile classifications
<p>The DEMIX database (Guth, 2023a) contains statistics from 6 test 1 arc second DEMs (ALOS, ASTER, CopDEM, FABDEM, NASADEM, and SRTM) compared to high resolution reference DEMs. The database contains 236 DEMIX tiles (Guth and others, 2023) and forms the basis for the ranking of global DEMs in Bielski and others (2023).</p> <p>A K-means clustering of the database using MICRODEM (Guth, 2023b, 2023c), and an additional set of 4 land cover and landform classifications (Table 1) for the 236 DEMIX tiles computed the percentage of each DEMIX tile in each classification category. Guth (2023d) has the raw data for the percentages of each category for each of the 236 tiles along with the K-means cluster assignments.</p> <p>This data set contains 3 figures for each of the 5 classification databases in Guth (2023d):</p> <ul> <li>Bar graph of the category percentages for each of the test areas. A composite version of these graphs is in Bielski and others (2023).</li> <li>Bar graph of the category percentages for each of the 236 test tiles. These graphs are too large to include on a single page with readable legends.</li> <li>Legend for the classification</li> </ul> <p> </p> <p>References:</p> <p>Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; and the TMSG DEMIX Working Group, 2023. DEMIX Method Ranks COPDEM, and FABDEM as Top 1” Global DEMs: <a href="https://arxiv.org/abs/2302.08425v3">https://arxiv.org/abs/2302.08425v3</a></p> <p>Guth, P. L., 2023a. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8062008">https://doi.org/10.5281/zenodo.8062008</a></p> <p>Guth, P.L., 2023b. GIT-MICRODEM [Delphi source code, archived installation versions]. URL: https://github.com/prof-pguth/git_microdem </p> <p>Guth, P.L., 2023c, MICRODEM: Open-source GIS with a focus on Geomorphometry [download latest Win64 executable and CHM help file] URL: <a href="https://microdem.org/">https://microdem.org/</a></p> <p>Guth, P.L., 2023d, K-means clustering of the DEMIX data set (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8283791</p> <p>Guth, Peter L., Peter Strobl, Kevin Gross, & Serge Riazanoff. (2023). DEMIX 10k Tile Data Set (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7504791">https://doi.org/10.5281/zenodo.7504791</a></p>
Phenological metrics for Protected Area "GranParadiso", MODIS terra tile h18v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Phenological metrics for Protected Area "HighTatra", MODIS terra tile h19v04
Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;
Complex k-Uniform Tilings by a Simple Bitopic Precursor Self-Assembled on Ag(001)_experimental dataset
<p>This experimental dataset contains the raw underlying data for the article "Complex k-Uniform Tilings by a Simple Bitopic Precursor Self-Assembled on Ag(001) Surface" by Lukáš Kormoš, Pavel Procházka, Anton O. Makoveev, and Jan Čechal. </p>
Braid Tiling Experiments
<p>This set of images presents a set of experiments which were done to test the capabilities of the developed braiding pattern generation methods. Each figure shows a different kind of tiling problem, from regular to completely random patterns.</p>
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g.
Dataset: Tile Shop Holdings, Inc. (TTSH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Interface, Inc. (TILE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Grid tiles for identifying EU-SoilHydroGrids tiles
<p>Polygon tiles in Shapefile and GeoPackage for navigating/selecting EU-SoilHydroGrids 250m GeoTiff tile folders.</p>
Effect of biological colonization on ceramic roofing tiles by lichens and a combined laser and biocide procedure for its removal
<p>Biodeterioration damage is an important issue in conservation and restoration of built heritage, especially when ceramic materials are used. Biological colonization of ceramic roofing tiles by lichens is a common phenomenon. However, there are no reports to date of lichens removal from unglazed roofing tiles for conservation purposes. This paper for the first time reveals the results of a combined procedure undertaken to assess the removal of lichens on different kinds of unglazed ceramic roofing tiles by treatments based on both dual sequential laser irradiation and treatment using Acticide<sup>® </sup>CF biocide. Three species of lichens were identified: <em>Verrucaria nigrescens</em>, <em>Calogaya decipiens</em> and <em>Pyrenodesmia teicholyta</em>. The chemical and mineralogical composition of roofing tiles were characterized by X-ray fluorescence (XRF) spectrometry, optical polarized petrographic microscopy, and X-ray diffraction (XRD). Laser irradiation was accomplished by applying sequences of nanosecond laser pulses at two wavelengths (1064 and 266 nm). After dual sequential laser irradiation a biocide was applied. To assess the combined effect of both treatments several techniques were used, including stereo and fluorescence (FM) microscopies, scanning (SEM) and transmission (TEM) electron microscopies, and FT-Raman spectroscopy. Chemical composition of the analyzed roofing tiles was shown as a relevant factor regarding the degree of interaction between the biological colonization and the substrate, and hence, the bioweathering effect. The combined procedure has proved to be very effective in damaging and mostly collapsing the lichen thalli without altering the substrate.</p>
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017]. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017].
Linked collectors and determiners for: The mosaic tiled harvestmen-taxonomic review of Gonyleptellus Roewer, 1930 (Opiliones: Gonyleptidae: Gonyleptinae).
Natural history specimen data linked to collectors and determiners held within, "The mosaic tiled harvestmen-taxonomic review of Gonyleptellus Roewer, 1930 (Opiliones: Gonyleptidae: Gonyleptinae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d3e2e8f0-829f-4e34-aa82-14c0409aa379">https://bionomia.net/dataset/d3e2e8f0-829f-4e34-aa82-14c0409aa379</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d3e2e8f0-829f-4e34-aa82-14c0409aa379">https://gbif.org/dataset/d3e2e8f0-829f-4e34-aa82-14c0409aa379</a>. Formatted as a Frictionless Data package.
tatianeolivatto/ACR-Street-View: Original and tiled
<p>This project comprise a labelled image dataset developed to provide an object detector for ACRs (accessibility curb ramps) located in sidewalks covering Brazilian Cities (specifically São Paulo State).<br> There are two panorama image datasets included, being one original and one tiled. The second one resulted in the best detections.<br> Also, there are two Yolov4 object detector included, one for each dataset.</p>
DEMIX 10k Tile Data Set
<p>This dataset of tiles, nominally 10x10 km in size on a geographic grid, supports the work of the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group (Strobl and others, 2021; Guth and others, 2021; ; Bielski and others, work in progress). This data set provides a technical description, naming convention, and GIS data sets in shapefile and geopackage formats. The tiles have a constant latitudinal spacing of 6 arc minutes, and increase the longitudinal spacing toward the poles to approximate a constant area for the tiles. The DEMIX tile system provides a global tessellation at a scale fine enough to produce locally significant results, coarse enough to keep the total number of tiles manageable, and equal enough in size to allow for statistical aggregation and comparative analysis.</p> <p> </p> <p>To understand the use of the database, see the preprint (Bielski and others, 2023).</p> <p><strong>References:</strong></p> <p>Bielski, C.; López-Vázquez, C.; Guth. P.L.; Grohmann, C.H. and the TMSG DEMIX Working Group, 2023. DEMIX Wine Contest Method Ranks ALOS AW3D30, COPDEM, and FABDEM as Top 1” Global DEMs: <a href="https://arxiv.org/pdf/2302.08425.pdf"> https://arxiv.org/pdf/2302.08425.pdf</a></p> <p>Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. <a href="https://doi.org/10.3390/rs13183581">https://doi.org/10.3390/rs13183581</a></p> <p>Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. <a href="https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021">https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021</a></p>
Tiles of airborne laser scanning point clouds of Vienna, Austria (2016)
<p>Airborne laser scanning of the city of Vienna, which was organized by the survey department of the City Administration of Vienna (MA41). The data acquisition was performed in eight flight missions in November 2015.</p> <p>After strip adjustment relative accuracy of the point cloud was in the order of 2cm. The absolute accuracy measured as RMSE is better than 5cm in planimetry and better than 4cm in elevation. The dataset was cleaned and does not involve obvious gross errors, e.g. points high up in the air or points far below ground level. The point density is measured as by last echoes per unit area and is more than 15 points/m<sup>2</sup> for 97% of the area. The average point density is 33 points/m<sup>2</sup>.</p> <p>The LiDAR dataset of the city is organized in a number of 1270m×1020m tiles (including a 10m overlap of all neighboring tiles).</p> <p>Reference labels were generated semi-automatically: a rough filtering of main objects was firstly conducted by the software “Terrasolid”, and the final classification was refined by manual labelling. For the purpose of Vienna city administration, five classes are considered, namely ground, buildings, vegetation, others, water and bridges. All common street objects are categorized as others, such as (e.g.) streetlights, benches, shrubs, cars, construction sites and garbage bins. The different classes are defined by the following numeric integer codes: 2: Ground, 5: Vegetation, 6: Buildings, 8: Others, 9: Water and 17: Bridges.</p> <p>The quality of the labelling was manually checked. In 20 sites of 100m×100m the classification was manually verified, and the average accuracy of reference labels is 95%.</p> <p>Total 9 tiles are published, in which 4 tiles were used for the training and 5 tiles for the evaluation in the paper “A Comparison of Deep Learning Methods for Airborne LiDAR Point Clouds Classification”. Their locations in the city of Vienna can be found in the file of metadata, which also provides WGS84/GRS80 latitude and longitude coordinates of the extent corners of each tile (EPSG: 31256). This can be used to access images of the tiles from Google Maps or other public map service.</p> <p> </p>
Image tiles of TCGA-CRC-DX histological whole slide images, non-normalized, tumor only
<p>These are image tiles of tumor tissue of N=604 colorectal cancer (CRC) histological whole slide images in the TCGA database. The tumor tissue was manually outlined and cut into non-overlapping tiles of 512x512 px at 0.5 µm/px. No further preprocessing was applied. No color normalization was applied. Tiles are compressed with JPEG. Each ZIP file corresponds to one whole slide image (original format: SVS).</p> <p>Please cite our previous publication related to this dataset: https://www.nature.com/articles/s41591-019-0462-y</p> <p>Please observe the original TCGA licenses if you use the data https://portal.gdc.cancer.gov/</p> <p>Original image credit to TCGA: https://portal.gdc.cancer.gov/</p> <p>Image tiles were created with QuPath v0.1.2 https://qupath.github.io</p> <p>Genetic data matching these images are available at https://cbioportal.org</p> <p>More information on the procedures: https://zenodo.org/record/3694994</p>
Pierhead Building dragon tile
This is a scan of one of the red clay double Welsh Dragon tiles around the entrance to the Pierhead Building, in Cardiff Bay, South Wales. According to Wikipedia the Pierhead Building was built in 1897 so the tiles are a nice example of Victorian architectural embellishment. And they're also a nice bit of Welsh history to have hanging on your wall. If you like this model please read more about it on the blog page below: http://parth3d.co.uk/pierhead-building-dragon-tile Please note that this scan is provided without any license for commercial use. It is intended simply as a model you can have the fun of printing yourself. And of course it is intended to be a motivation for you to visit Cardiff Bay yourself. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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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.