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2,170 results for “clouds”
Dataset for "Comparison of climate response to ocean albedo modification and marine cloud brightening: A model study"
<p>Reproducible dataset for "Comparison of climate response to ocean albedo modification and marine cloud brightening: A model study"</p>
Cloud-hosted DBMS Performance, Scalability and Availability Evaluation Data
<p>These data sets contain evaluation data for the evaluation objectives performance, scalability and availability of different cloud-hosted, distributed DBMS. </p> <p>The data sets contain the raw performance metrics, monitoring data and evaluation metadata. For the higher-level evaluation objectives scalability and availability, supportive plots are provided.</p> <p><br> The performance and scalability evaluations have been carried out with the <a href="https://dl.acm.org/doi/10.1145/3297663.3310303">Mowgli framework</a> and the availability evaluation have been carried out with the <a href="https://dl.acm.org/doi/abs/10.1145/3341105.3373968">King Louie framework</a>.</p>
Clouds dataset for semantic segmentation
<p>This database contains images used for training a fully convolutional neural network for the semantic segmentation of clouds in images from the Sentinel-2 Level 2A Satellite.</p> <p>The images refer to the RGB composition (bands 4, 3, and 2).</p> <p>After that, each band was converted to a byte type (0-255).</p> <p>The images are still divided into three main sets: training, validation and testing:</p> <ol> <li><strong>Training dataset: </strong>it contains 1.160 GeoTIFF images with 512x512 pixels and associated PNG masks (clouds indicated in white and background in black color).</li> <li><strong>Validation dataset</strong>: it contains 100 GeoTIFF images with 512x512 pixels and associated PNG masks used for validation step.</li> <li><strong>Test dataset: </strong>it contains 100 GeoTIFF images 512x512 pixels for testing.</li> </ol>
Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition
<p>This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.<br> </p> <p><strong>Equipment</strong><br> The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand.</p> <p><em>Hyperspectral data</em></p> <p><br> Hyperspectral data acquisition was performed with Surface Optics SOC710 camera. This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range.</p> <p>The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel. </p> <p><em>3D point clouds</em></p> <p><br> The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights </p> <p><em>HD Images</em></p> <p><br> The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources. <br> </p> <p><strong>Data</strong></p> <p>The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles.</p> <p>Scene 1 (denoted <em>SceneEagle</em>) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand.<br> Scene 2 (<em>SceneFruit</em>) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image.<br> Scene 3 (<em>SceneFruit2</em>) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.</p> <p> </p> <p>The data are formatted as follows:<br> - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame.<br> - Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files.<br> - Matched Hyperspectral clouds are also provided as \text{*.obj} files<br> - HD photo files are provided in \text{*.jpg} files.<br> </p> <p><br> <strong>Acknowledgements</strong></p> <p>This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.</p> <p> </p>
Cloud radar, micro rain radar, parsivel and pluvio measurements at Ny-Ålesund for 7 Feb 2018, 16 March 2018 and 16 April 2018
<p>This data set contains netcdf files of the University of Cologne's cloud radar MiRAC-A, micro rain radar, parsivel and pluvio installed at the Arctic research site AWPIEV at Ny-Ålesund. Data are available for 3 days: 7 Feb 2018, 16 March 2018 and 16 April 2018.</p> <p>The MiRAC-A hourly files (“mirac-a_nya_compact_*_P01_ZEN.nc” include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:long_name = "Equivalent radar reflectivity factor Ze" ;</p> <p>Ze:units = "mm^6/m^3" ;</p> <p>float vm(time, range) ;</p> <p>vm:long_name = "Mean Doppler velocity" ;</p> <p>vm:units = "m/s" ;</p> <p>vm:comment = "negative values indicate falling particles towards the radar" ;</p> <p>float sigma(time, range) ;</p> <p>sigma:long_name = "Spectral width of Doppler velocity spectrum" ;</p> <p>sigma:units = "m/s" ;</p> <p> </p> <p>The Micro Rain Radar daily files (“*_nya_mrr_improtoo_0-101.nc”) include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:description = "reflectivity of the most significant peak" ;</p> <p>Ze:units = "dBz" ;</p> <p> </p> <p>The parsivel daily files (“sups_nya_dm00_l1_any_v00_*.nc”) include (among other variables):</p> <p>float N(dclasses, time) ;</p> <p>N:fill_value = NaN ;</p> <p>N:units = "log10(m-3 mm-1)" ;</p> <p>N:long_name = "particle concentration per diameter class" ;</p> <p>float dclasses(dclasses) ;</p> <p>dclasses:units = "mm" ;</p> <p>dclasses:long_name = "volume equivalent diameter class center" ;</p> <p> </p> <p>The pluvio daily files (“pluvio_nya_*.nc”) include (among other variables):</p> <p>r_accum_NRT(dim) ;</p> <p>r_accum_NRT:description = "accumulated precipitation NRT" ;</p> <p>r_accum_NRT:units = "mm" ;</p> <p> </p> <p>All available variables are listed and described in the header of the corresponding netcdf files.</p>
Clouds and Radiation in a mock-Walker Circulation model output
<p>Data used in the creation of figures for Silvers and Robinson, Clouds and Radiation in a mock-Walker Circulation. 2020, JAMES. </p> <p>These experiments are described in detail in the paper. They use the AM4.0 physics package on a doubly periodic domain with a warm patch of SST that is fixed in time at the center of the domain. Grid-Spacings of 1km, 2km, 25km, and 100km are explored on different domain sizes. The data set focuses on analysis of the atmospheric circulation and cloud response to this SST warm region.</p> <p>For questions please contact Levi Silvers at levi.silvers@stonybrook.edu, thomas.robinson@noaa.gov</p> <p>Directory contents are described below:</p> <p>#=========================================================================================<br> # data for the P100, P100L, P25, P25L, E25 experiments are in the following directories <br> #=========================================================================================<br> # P100<br> c8x40L33_am4p0_100km_wlkr_ent0p9<br> c8x40L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P100L<br> c8x160L33_am4p0_100km_wlkr_ent0p9<br> c8x160L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P25<br> c8x160L33_am4p0_25km_wlkr_ent0p9<br> c8x160L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # P25L<br> c8x640L33_am4p0_25km_wlkr_ent0p9<br> c8x640L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # E25<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv_lwoff</p> <p># within the directories for each of these experiments: P100, P100L, P25, P25L, E25<br> # there are two files: 1979th1983_daily.nc and 1980th1983.atmos_month_tmn.nc</p> <p># within each of these files are the following variables: <br> 1979th1983_daily.nc<br> 'prec_conv'<br> 'prec_ls'<br> 'precip'</p> <p>1980th1983.atmos_month_tmn.nc<br> 'ps'<br> 'temp'<br> 'ucomp'<br> 'w'<br> 'sphum'<br> 'cld_amt'<br> 'z_full'<br> 'precip'<br> 'prec_conv'<br> 'prec_ls'<br> 'rh'<br> 'tot_liq_amt'<br> 'tot_ice_amt'<br> 'tdt_lw'<br> 'evap'<br> 'shflx'</p> <p><br> #=========================================================================================<br> # data for the E1 and E2 experiments are in the following directories <br> #=========================================================================================<br> # E1 <br> c10x4000L33_am4p0_1km_wlkr_4K<br> c10x4000L33_am4p0_1km_wlkr_4K_lwoff<br> # E2 <br> c50x2000L33_am4p0_2km_wlkr_4K<br> c50x2000L33_am4p0_2km_wlkr_4K_lwoff</p> <p># within each of these directories are two files: 1979_6mn.atmos_month.nc and 1979.6mn.atmos_daily_selvars.nc<br> # these files contain the following variables: </p> <p># 1979_6mn.atmos_month.nc<br> tdt_lw<br> t_surf<br> evap<br> shflx<br> tot_ice_amt<br> tot_liq_amt<br> rh<br> precip<br> z_full<br> cld_amt<br> sphum<br> ucomp<br> temp <br> w<br> ps<br> shflx</p> <p># 1979.6mn.atmos_daily_selvars.nc<br> t_surf<br> ps<br> precip</p> <p>Acknowledgment: Aparna Radhakrishnan supported the provision of this Zenodo DOI. <br> </p>
Data from: A taxonomic and molecular survey of the pteridophytes of the Nectandra Cloud Forest Reserve, Costa Rica
<div class="page"> <div class="layoutArea"> <div class="column"> <p><span>Floristic surveys are crucial to the conservation of biodiversity, but the vast majority of such surveys are limited to listing species names, and few take into account the evolutionary history of species. Here, we combine classical taxonomic and molecular phylogenetic (DNA barcoding) approaches to catalog the biodiversity of pteridophytes (ferns and lycophytes) of the Nectandra Cloud Forest Reserve, Costa Rica. Surveys were carried out over three field seasons (2008, 2011, and 2013), resulting in 176 species representing 69 genera and 22 families of pteridophytes. Our literature survey of protected areas in Costa Rica shows that Nectandra has an exceptionally diverse pteridophyte flora for its size. Plastid </span><span>rbcL </span><span>was selected as a DNA barcode marker and obtained for >95% of pteridophyte taxa at this site. Combined molecular and morphological analyses revealed two previously undescribed taxa that appear to be of hybrid origin. The utility of </span><span>rbcL </span><span>for species identification was assessed by calculating minimum interspecific distances and found to have a failure rate of 18%. Finally we compared the distribution of minimum interspecific </span><span>rbcL </span><span>distances with two other areas that have been the focus of pteridophyte molecular surveys: Japan and Tahiti. The comparison shows that Nectandra is more similar to Japan than Tahiti, which may reflect the biogeographic history of these floras.</span></p> </div> </div> </div>
The asymmetric diurnal latent heat flux in Chi-Lan montane cloud-fog forest: CLM simulations and sap flow observations
<p>Chilan_30min_sap_flow_V_2020JJA.csv recorded the data of sap flow velocity during JJA 2020.</p> <p>CL_CTR.*.nc is the analyzed CTR simulations which consider fog interception as a source of canopy water.</p> <p>CL_EXP.*.nc is the analyzed EXP simulations that do not allow the canopy to hold the water.</p>
Southern Ocean Cloud and Aerosol data set: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage
<p>Due to its remote location and extreme weather conditions, atmospheric in situ measurements are rare in the Southern Ocean. As a result, aerosol-cloud interactions in this region are poorly understood and remain a major source of uncertainty in climate models. This, in turn, contributes substantially to persistent biases in climate model simulations, numerical weather prediction models and reanalyses. It has been shown in previous studies that in situ and ground-based remote sensing measurements across the Southern Ocean are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements which are typically obscured by near-continuous overlying cloud cover observed in this region. Here we provide a comprehensive set of ship-based aerosol and meteorological observations collected on the TAN1802 voyage of R/V Tangaroa across the Southern Ocean, from Wellington, New Zealand, to the Ross Sea, Antarctica. The voyage was carried out from 8 February to 21 March, 2018. The compiled data set provides here includes measurements from a range of instruments, such as (i) meteorological conditions at the sea surface and profile measurements; (ii) the size and concentration of particles; (iii) trace gases dissolved in the ocean surface such as dimethyl sulfide and carbonyl sulfide; (iv) and remotely sensed observations of low clouds. We encourage the scientific community to use these measurements for further analysis and model evaluation studies, in particular, for studies of Southern Ocean clouds, aerosol and their interaction.</p>
Data for GMD article: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4"
<p>Dataset for the publication by Nina Črnivec and Bernhard Mayer: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4" submitted to Geoscientific Model Development in 2020.</p> <p>The repository contains data for stratocumulus, cirrus and cumulonimbus cloud case studies. It also contains MYSTIC benchmark radiation data (including atmosphering heating rate and net surface flux) for the aforementioned cloud cases. See README for additional information and description of data files.</p>
A 5% measurement of the gravitational constant in the Large Magellanic Cloud
<p><strong>Reproduction package for the paper "A 5% measurement of the gravitational constant in the Large Magellanic Cloud".</strong></p> <p><strong>The package contains inlist and run_star_extra.f files for MESA that can be used to simulate Cepheid variable stars under different values of the gravitational constant. Also included is a Python MCMC script for sampling MESA Cepheid models and comparing with measurements from Pilecki et al., ApJ 862 43 (2018).</strong></p>
3D Multi-fluid MHD Simulation of the Early Time Behavior of an Artificial Plasma Cloud in the Bottom Side Ionosphere
<p>This repository contains the necessary files for reproducing each of the figures in Ober et. al (2021) - currently a manuscript. Each file is named after the associated figure number and stored in the HDF5 format - with maximum compression. In general all chemical species physical variables are stored with four indexed dimensions: (species, z-axis, y-axis, x-axis).</p> <p> </p> <p>The details for chemical species are based in corresponding index values with string array ‘species’:<br> </p> <p>0 -> Ba</p> <p>1 -> O</p> <p>2 -> O+</p> <p>3 -> Ba+</p> <p>4 -> e-</p> <p> </p> <p>Axis values are in units of meters (m) from spatial origin </p> <p>X-axis (1, 300)</p> <p>Y-axis (1, 300)</p> <p>Z-axis (1, 888)</p> <p> </p> <p>Chemical species number densities in units of meters cubed (m^3)</p> <p>number_density ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species Temperature in units of Kelvin (K)</p> <p>temperature ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species velocity component values - meters per second (m/s)</p> <p>velocity_x ( 5, 888, 300, 300 ) </p> <p>velocity_y ( 5, 888, 300, 300 ) </p> <p>velocity_z ( 5, 888, 300, 300 ) </p> <p> </p> <p>Magnetic field in units of Teslas (T)</p> <p>magnetic_x ( 1, 888, 300, 300 ) </p> <p>magnetic_y ( 1, 888, 300, 300 ) </p> <p>magnetic_z ( 1, 888, 300, 300 ) </p> <p> </p> <p>NOTE: To help meet the data set limit, magnetic field variables are excluded from file “_figure-1.h5”. For "__figure-7b.h5" and "__figure-7c.h5" only magnetic field components are included.</p>
Annotated Pseudo-Transitive Relations of the LOD Cloud
<p>This resource consists of four folders:</p> <p>The <strong>gold_standard</strong> folder provides the files consisting of manually evaluated triples. <br> The files were exported from ANNit with the column:<br> LEFT* for the source URI of the triple.<br> RIGHT* for the target URI of the triple.<br> UserChioce for the choice of user when manually evaluated<br> Decision* for the actual decision made by annotator. It can only be unknown, remove, remain.<br> Comment, if any.</p> <p>The only three columns that matter for the evaluation of removed triples in this project are those labelled with *.</p> <p>The folder graph_file includes the unweighted graphs, as well as the two sets of weighted graphs: the graphs with counted weights and the graphs with inferred weights (in the subdirectory of <strong>counted_weights</strong> and <strong>inferred_weights</strong> subdirectory respectively). <br> The files are compressed in the format of *.gz. Each file consists of two columns of integers as the source and the target. The integers corresponds to the URIs. The corresponding mapping files are in the directory <strong>mapping</strong>.</p> <p>Finally, the corresponding files (of unweighted graphs) in WebGraph format are provided. These files were used when evaluating our algorithm against exiting web-scale feedback-arc-set algorithm.</p> <p> </p> <p>Should there be any problem with these datasets, please feel free to report to us at the following email address: <a href="mailto:shuai.wang@vu.nl">shuai.wang@vu.nl</a>.</p> <p> </p>
Enhancement of cloud-to-ground lightning activity caused by the urban effect: a case study in the Beijing metropolitan area
<p>The dataset here includes land-surface temperature, wind field, precipitation, UV aerosol index, and lightning information analyzed in the paper "<strong>Enhancement of cloud-to-ground lightning activity </strong><strong>caused by the urban effect: a case study in</strong> <strong>the Beijing metropolitan area</strong>".</p>
A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of TRMM PR and VIRS
<p>This new merged gridded dataset (M-1B01-2A25-GD) contains precipitation, clouds and atmospheric parameters with 0.25° spatial resolution.</p> <p>It is produced by merging TRMM PR and VIRS measurements with the ERA5 reanalysis dataset at the same spatiotemporal resolution between 40° S and 40° N. The near-surface rain rate, profiles of rain rate and precipitation reflectivity factor, visible and infrared signals and atmospheric parameters (temperature, pressure, geopotential height, specific humidity, divergence, vertical velocity and so on) can be obtained in the dataset. The statistical results indicate that the merging and gridding will not dramatically distort the original data and the new dataset can be used to study the characteristics and distribution of the precipitation and clouds systems.</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>
Dataset: Thermal tides in the upper cloud layer of Venus as deduced from the emission angle dependence of the brightness temperature by Akatsuki/LIR
<p>The LIR images (Level 2c) used in the figures in the paper "Thermal tides in the upper cloud layer of Venus as deduced from the emission angle dependence of the brightness temperature by Akatsuki/LIR" by Akiba et al. (J. Geophys. Res)</p> <p>The description of Akatsuki/LIR and level definition are in <a href="https://darts.isas.jaxa.jp/planet/project/akatsuki/lir.html.en">https://darts.isas.jaxa.jp/planet/project/akatsuki/lir.html.en</a></p>
Vertical distribution of epiphytic lichens on Quercus laurina Humb. & Bonpl. in a remnant of cloud forest in the state of Veracruz, México
<p>It is considered that in the tropics, lichen richness and cover tend to increase from the trunk base to the top of the crown of trees. In this study we calculate total beta diversity of the lichen community along a vertical gradient on Quercus laurina. By comparing the richness and cover of the lichens by zone, we will be able to prove that the foliose and fruticose forms will be the minor component of the total lichen species richness; but with the highest cover with respect to the crustose lichens. Five zones were identified in each phorophyte (n=15) with a diameter at breast height > 40 cm. A total of 92 species were identified, of these, 38% were found only in a single zone, 51% were shared between the different zones, 11% occurred across all zones. Species richness and cover increased from the lowest to the highest zones of phorophytes. Dissimilarity in species composition between the zones, can be explained by species replacement. The Indicator Species Analysis revealed that only a few species such as Hypotrachyna vexans, H. cf. sublaevigata and Ramalina cf. sinaloensis preferred a particular zone. The results obtained to date show that the lichen community associated with Quercus laurina phorophytes is highly diverse. There is a high replacement of species across the different zones. Our results suggest that species richness and cover of the corticolous lichen community are related to the zone and the diverse growth forms.</p>
Point Cloud - Plaza Mayor de Almagro, Spain
This is a point cloud of the Plaza Mayor de Almagro, located in Almagro Spain. The data were collected during the night in March 2017 using three FARO Focus3D laser scanners. Almagro's main square is in the historical and cultural center of the city, in the heart of the Military Order of Calatrava. It was built in the 16th century as a large public space for a market, celebrations, important or solemn events and leisure space. Its size allowed, the holding of bullfights, before the exempt bullrings were created. The plaza has a rectangular shape and is delimited by two large galleries formed by beautiful stone columns. Originally the upper galleries of the square were not closed as they served to contemplate the public shows organized in the square. At one end the Town Hall stands and at the other end the Church of San Bartolomé, now disappeared. In addition, on one of its sides it is still possible to visit the corral de comedias (theater) built in the late 16th century or early 17th century. Source: Objaverse 1.0 / Sketchfab
12th C CE Water-Moon Guanyin - point cloud
Twelfth-century Water-Moon Guanyin, now in the collection of the Minneapolis Institute of Art. From the sculpture's description on artsmia.org: "This form of the bodhisattva presides over his own paradise, Potolaka, which is described in scripture as a rugged seaside cave from which Avalokiteshvara could admire the reflection of the moon in the water. Appropriately, he appears meditative and relaxed and is seated informally in the "royal-ease" posture. Ornately dressed, with silk robes, fluttering sashes, jewelry, and an elaborate hairstyle, he also wears a headpiece that features an image of Amitābha, the buddha to whom Avalokiteshvara attends. Believers might look to a sculpture like this as a guide for their own journeys toward enlightenment." The sculpture on Mia's website: https://collections.artsmia.org/art/32122/seated-guanyin-china The mesh version of the object is here: https://skfb.ly/68PAA Source: Objaverse 1.0 / Sketchfab
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.