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2,214 results for “Walls”
Fragment of the wall of a vase - PG.2000.1526
Fragment of the wall of a vase. Argaric culture. Early-Middle Bronze Age (2300-1600 BC). Inventory number: PG.2000.1526 Find this object in the museum's online catalog [Carmentis](https://www.carmentis.be:443/eMP/eMuseumPlus?service=ExternalInterface&module=collection&objectId=198589&viewType=detailView) Source: Objaverse 1.0 / Sketchfab
Vase with straight wall - PG.2000.1267
Vase with straight wall. Argaric culture. Early-Middle Bronze Age (2300-1600 BC). Inventory number: PG.2000.1267 Find this object in the museum's online catalog [Carmentis](https://www.carmentis.be:443/eMP/eMuseumPlus?service=ExternalInterface&module=collection&objectId=197277&viewType=detailView) Source: Objaverse 1.0 / Sketchfab
Deir el-Bahari, North Wall, Middle Portico
Damaged reliefs on the north wall of the hypostyle hall of the Anubis shrine on the Middle portico of the Temple of Hatshehp at Deir el-Bahari, Luxor Egypt. These relief carvings show deliberate destrution which took place during the reign of Thutmose. The names and figures of Hatshepsut have been chiseled off the walls, while those of Thutmose III are preservered and untouched. Created from 200 photos (Canon EOS Rebel T5i, 18MP) using Metashape Professional 1.6.1. It was photographed in January 2020. Model is scaled 1:1 with units in meters. Source: Objaverse 1.0 / Sketchfab
Fort wall
Uploaded with 3dScannerApp.com Source: Objaverse 1.0 / Sketchfab
Wall Substance Painter test
Just me leraning about Substance Painter. Source: Objaverse 1.0 / Sketchfab
Wall Detail 01
detail wall including 10 million high model Source: Objaverse 1.0 / Sketchfab
A soviet construction wall.
A soviet construction wall. Советский строительный забор. Source: Objaverse 1.0 / Sketchfab
Castle Wall
First Upload photoscanned model of Castle Wall. Source: Objaverse 1.0 / Sketchfab
Wall fragment of a vase with buttons
Wall fragment of a vase with buttons. Argaric culture. Early-Middle Bronze Age (2300-1600 BC). Inventory number: PG.41.1.810.3 Find this object in the museum's online catalog [Carmentis](https://www.carmentis.be:443/eMP/eMuseumPlus?service=ExternalInterface&module=collection&objectId=197317&viewType=detailView) Source: Objaverse 1.0 / Sketchfab
Small vase with straight wall and round base
Small vase with straight wall and round base. Argaric culture. Early-Middle Bronze Age (2300-1600 BC). Inventory number: PG.2000.201 Find this object in the museum's online catalog [Carmentis](https://www.carmentis.be:443/eMP/eMuseumPlus?service=ExternalInterface&module=collection&objectId=197312&viewType=detailView) Source: Objaverse 1.0 / Sketchfab
China's Wall
Source: Objaverse 1.0 / Sketchfab
Channakeshwara Temple Wall 3D Scan
3D Scanned Wall of Chennakeshwara Temple @ Aralaguppe, Karnataka Source: Objaverse 1.0 / Sketchfab
Fragment of a pot with straight wall
Fragment of a pot with straight wall. Argaric culture. Early-Middle Bronze Age (2300-1600 BC). Inventory number: PG.2000.1504 Find this object in the museum's online catalog Carmentis Source: Objaverse 1.0 / Sketchfab
Mosaic Wall Fountain
3D Scan from the Metropolitan Museum of Art in New York. Source: Objaverse 1.0 / Sketchfab
Fig. 1 in Reactive oxygen species in cell wall metabolism and development in plants
Fig. 1. Schematic representation of the enzymes at the plasma membrane and in the cell wall that are able to form reactive oxygen species (ROS: superoxide anion radical · — hydrogen peroxide hydroxyl radical. into the apoplast. · — is dismutated either enzymatically catalyzed by superoxide dismutase, (O 2 ), (H2O2), ( OH)) O 2 to H2O2 or nonenzymatically in the acidic pH that is typical in the cell wall. ROS play important roles both in cell wall loosening during cell elongation and cross-link formation involved in cell growth restriction..OH is considered as a cell wall-loosening agent formed from H O either non-enzymatically by Fenton reaction involving a transition metal such as 2 2 Fe2+ or Cu +, or enzymatically by peroxidases (not depicted). Di- and oligoferulate bridges, bonds between tyrosine residues abundant in cell wall structural proteins, lignin formation, or cross-linkages between ferulates and lignin are possible cross-links involved in cell growth restriction. For the cross-link formation, oxidative enzymes (peroxidase in a peroxidative cycle using H2O2 as an oxidant, or laccase using O2 as an oxidant) catalyze the oxidation of the phenolic residues after which they make a crosslink. In addition to cell wall modifications, ROS are important signalling components in various biological processes. The left cell shows the enzymes producing ROS, and the right cell shows the enzymes consuming ROS during cell wall cross-linking. Note that all enzymes and the cross-link types mentioned may not be present in the same cell, or in all species. In case of quinone reductases, further studies are needed to find out whether enough quinones are present in the plasma membranes to be able to mediate electron transport from the cytoplasmic reductant to apoplastic molecular oxygen.
High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023
<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>
FIGURE 1 in Report of stray sightings of Dendrelaphis proarchos (Wall, 1909) (Serpentes: Colubridae) in Surat, Gujarat, western India
FIGURE 1. Maximum likelihood phylogenic tree of Dendrelaphis based on a partial fragment of 16S with bootstrap support shown at nodes (values <60 not shown). The Surat individual BNHS 3553 (in red) nested within the native Myanmar and Northeast Indian populations of D. proarchos.
FIGURE 2. BNHS 3553 in Report of stray sightings of Dendrelaphis proarchos (Wall, 1909) (Serpentes: Colubridae) in Surat, Gujarat, western India
FIGURE 2. BNHS 3553 in life Dendrelaphis proarchos from Surat, Gujarat, India. (a) Full body, dorsal view; (b) Head, left side view with black postocular stripe; (c) Head, right side view with black postocular stripe; (d) Head, frontal view, showing its red tongue; (e) Body, lateral view, showing black ventrolateral stripes along body; (f) undivided cloacal scale (profiles of diagnostic features). Photographs by Dikansh S. Parmar.
FIGURE 4 in Report of stray sightings of Dendrelaphis proarchos (Wall, 1909) (Serpentes: Colubridae) in Surat, Gujarat, western India
FIGURE 4. Live individual here identified as Dendrelaphis cf. proarchos (see Appendix 3) recently rescued by Nature Care NGO from Anand Nursery, Jaipur, Rajasthan, western India. (a) Full body, dorsal view; (b) Anterior and ventrolateral aspects; (c) Head, dorsal and lateral view. Photographs by Rakesh Prajapat.
FIGURE 3 in Report of stray sightings of Dendrelaphis proarchos (Wall, 1909) (Serpentes: Colubridae) in Surat, Gujarat, western India
FIGURE 3. Physical map of Indian subcontinent showing Surat in coastal Gujarat, western India from where we present this report of Dendrelaphis proarchos, and localities (black rectangles) in the natural range of the species in Northeast India and neighboring countries, including its type locality (black star).
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.