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1,254 results for “PANs”
Figure 3 in Surveys of the bee (Hymenoptera: Apiformes) community in a Neotropical savanna using pan traps
Figure 3. Bee abundance in relation to the habitat complexity (A) and to each habitat score (varying 0 to 3): tree canopy cover (B), shrub canopy cover (C), ground herb cover (D), amount of logs, rocks and debris (E), and soil moisture (F).
Cherokee Plain Pan (2176p13)
**Cherokee plain pan** Location: Qualla Boundary, Swain County, North Carolina. Period: Historic (1880s). Material: ceramic. Dimensions: height, 8.4 cm; diameter, 24.3 cm. Notes: Catalog no. 2176p13. Ethnographic specimen. North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
Cherokee Check-Stamped Pan (2176p10)
**Cherokee check-stamped pan** Location: Qualla Boundary, Swain County, North Carolina. Period: Historic (1880s). Material: ceramic. Dimensions: height, 6.8 cm; diameter, 25.2 cm. Notes: Catalog no. 2176p10. Ethnographic specimen. North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
Dionysos and Pan
Dionysos and Pan and a panther, from Pozzuoli (Naples) 2nd C. A.D., Roman copy from Greek original, Marble, Nye Carlsberg Glyptotek (Copenhagen, Denmark). Made with Memento Beta. In Greek religion and mythology, Dionysus is the god of the grape harvest, winemaking and wine, of ritual madness, fertility, theatre and religious ecstasy in Greek mythology. Also known as Bacchus, the name adopted by the Romans. Wine played an important role in Greek culture with the cult of Dionysus the main religious focus for unrestrained consumption. Pan is the god of the wild, shepherds and flocks, nature of mountain wilds and rustic music, and companion of the nymphs.His name originates within the Ancient Greek language, from the word paein, meaning "to pasture"; the modern word "panic" is derived from the name. He has the hindquarters, legs, and horns of a goat, in the same manner as a faun or satyr. Source: Objaverse 1.0 / Sketchfab
Cherokee Complicated Stamped Pan (2176p14)
**Cherokee complicated stamped pan** Location: Qualla Boundary, Swain County, North Carolina. Period: Historic (1880s). Material: ceramic. Dimensions: height, 8.8 cm; diameter, 20.5 cm. Notes: Catalog no. 2176p14. Ethnographic specimen. North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
Pan and a Satyr
Pan and a Satyr (A352), Thorvaldsen museum (Copenhagen, Denmark). Made with Memento Beta (now ReMake) from AutoDesk. Relief in marble, after the original model from 1831. Pan is half-human and half-animal and so can be recognised by his goat legs, horns, and beard. He is the god of shepherds, and here is teaching a small satyr how to play a pan flute. For more updates, please follow @GeoffreyMarchal on Twitter. Source: Objaverse 1.0 / Sketchfab
Pan-cancer analyses refine the single-cell portrait of tumor-infiltrating dendritic cells
<p>This is the dataset for "Pan-cancer Analyses Refine the Single-Cell Portrait of Tumor-Infiltrating Dendritic Cells".</p> <p>File "panDC_all_h5ad.gz" contains processed expression .h5ad data.</p> <p>File "panDC_metadata.csv" contains the meta data for this study.</p>
Data from: Pan-genome analysis highlights the role of structural variation in the evolution and environmental adaptation of Asian honeybees
<p>The <em>Asian honeybee</em>, <em>Apis cerana</em>, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population-level health, histories, and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger-scale genomic variation. We employed long-read sequencing technologies to generate a chromosome-scale reference genome for the ancestral group of<em> A. cerana</em>. Integrating this with 525 resequencing datasets, we constructed the first pan-genome of <em>A. cerana</em>, encompassing almost the entire gene content. We found that 31.32% of genes in the pan-genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions, however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel <em>A. cerana</em> ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of <em>A. cerana</em> by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan-genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of <em>A. cerana</em>.</p>
Data from: Pan-evolutionary and regulatory genome architecture delineated by integrated macro- and microsynteny approach
<p>Based on the published algorithms or tools developed by our and other groups, we introduce a detailed protocol for the most comprehensive and up-to-date genome synteny pipeline (called PanSyn) and provides step-by-step instructions as well as application examples for demonstrating how to use it. PanSyn pipeline includes three major modules (microsynteny analysis, macrosynteny analysis, and integrated micro & macro analysis). PanSyn not only fills a gap of lacking a user-friendly, highly-customized tool for genome macrosynteny analysis but also allows for integrated pan-evolutionary and regulatory analysis of genome microsyntenty and macrosynteny which are not yet available in any public synteny software or tools. PanSyn has been tested under Linux system. PanSyn has multiple subroutines. Users only need to simply modify the configuration file and corresponding command parameters to execute them. Outputs include vector diagrams that are suitable for custom modification. <br> </p>
Frying Pan avalanche data
<p>The data here are large wood data collected in the Summer of 2022 in the Frying Pan River Basin in the Sawatch Mountains of Central Colorado. There are six datasets included or referenced here. The first is general geomorphic and watershed characteristics of the stream reaches surveyed. The second is data from field reports to the CAIC. The third is topographic data for the studied avalanche pathways. The fourth is summary data of the wood volumes within each surveyed reach. The fifth is the unprocessed raw data for all wood jams and individual pieces surveyed. The sixth is a table of literature-derived annual recruitment rates for mechanisms common to mountain streams. Data may also be accessed via the Dryad data repository as linked in the data accessibility statement.</p> <p>Raw data relate several wood jam and individual piece properties, including length, width, and depth of the former, and length and diameter of the latter. Data also indicate several other wood characteristics, such as piece orientation, stability and decay class, and the presence of a rootwad. Finally, data include information about the geomorphic impact of each surveyed piece and jam. Data were collected to examine research questions related to in-stream wood load volumes supplied by snow avalanches and the resultant geomorphic impacts. </p>
Catawba Earthenware Pan (2499p1404/1)
**Catawba earthenware pan** Location: Old Town (RLA-SoC 634), Lancaster County, South Carolina. Period: Historic (1780-1800). Material: ceramic. Dimensions: height, 13.1 cm; diameter, 35.8 cm. Notes: Catalog no. 2499p1404/1. Vessel 31. North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
Pan de Muerto
Una pieza de Pan de Muerto, se consume en Noviembre por el festejo del Día de los Muertos #FoodChallenge Source: Objaverse 1.0 / Sketchfab
Pan yunquerano
Modelo 3D a partir de fotografías de un auténtico pan de horno de la panadería de Nicolás de Yunquera (Málaga) Source: Objaverse 1.0 / Sketchfab
A pan-cetacean MHC amplicon sequencing panel developed and evaluated in combination with genome assemblies
<p>The major histocompatibility complex (MHC) is a highly polymorphic gene family that is crucial in immunity, and its diversity can be effectively used as a fitness marker for populations. Despite this, MHC remains poorly characterised in non-model species (e.g., cetaceans: whales, dolphins and porpoises) as high gene copy number variation, especially in the fast-evolving class I region, makes analyses of genomic sequences difficult. To date, only small sections of class I and IIa genes have been used to assess functional diversity in cetacean populations. Here, we undertook a systematic characterisation of the MHC class I and IIa regions in available cetacean genomes. We extracted full-length gene sequences to design pan-cetacean primers that amplified the complete exon2 from MHC class I and IIa genes in one combined sequencing panel. We validated this panel in 19 cetacean species and described 354 alleles for both classes. Furthermore, we identified likely assembly artefacts for many MHC class I assemblies based on the presence of class I genes in the amplicon data compared to missing genes from genomes. Finally, we investigated MHC diversity using the panel in 25 humpback and 30 southern right whales, including four paternity trios for humpback whales. This revealed copy-number variable class I haplotypes in humpback whales, which is likely a common phenomenon across cetaceans. These MHC alleles will form the basis for a cetacean branch of the Immuno-Polymorphism Database (IPD-MHC), a curated resource intended to aid in the systematic compilation of MHC alleles across several species, to support conservation initiatives.</p>
Pan-Cancer-Nuclei-Seg-DICOM: DICOM converted Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pan-Cancer-Nuclei-Seg-DICOM" target="_blank" rel="noopener">Pan-Cancer-Nuclei-Seg-DICOM</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div>This collection contains automatic nucleus segmentation data of 5,060 whole slide tissue images of 10 cancer types earlier published in [2] (<a href="https://doi.org/10.7937/TCIA.2019.4A4DKP9U">https://doi.org/10.7937/TCIA.2019.4A4DKP9U</a>) stored in DICOM Bulk Annotation and DICOM Segmentation formats.</div> <div> </div> <div>DICOM Bulk Annotation nuclei annotations are stored as closed polygons along with the area of each nuclei. DICOM Segmentation version contains binary segmentations obtained by rasterizing the polygon contours. </div> <div> </div> <div>The annotations correspond to digital pathology images from the TCGA-BLCA,TCGA-BRCA,TCGA-CESC,TCGA-COAD,TCGA-GBM,TCGA-LUAD,TCGA-LUSC,TCGA-PAAD,TCGA-PRAD,TCGA-READ,TCGA-SKCM,TCGA-STAD,TCGA-UCEC,TCGA-UVM collections available in NCI Imaging Data Commons.</div> <div> </div> <div>To learn how these files are organized and how to access the content programmatically, see this documentation page: <a href="https://highdicom.readthedocs.io/en/latest/ann.html">https://highdicom.readthedocs.io/en/latest/ann.html</a>.</div> <div> </div> <div>Conversion of the nuclei segmentations from the original format into DICOM ANN and SEG representations was done using the code available in <a href="https://doi.org/10.5281/zenodo.13871765">10.5281/zenodo.10632181</a>.</div> <div> </div> <div>Annotations corresponding to this container ID in the source failed to convert due to the pixel matrix being too large to store: <code>TCGA-OL-A66K-01Z-00-DX1</code></div> <div> </div> <div>The following container IDs from the source annotations have failed due to inability to find the annotated images using the container IDs:</div> <div> <pre><code>TCGA-CU-A3QU-01Z-00-DX1 TCGA-A2-A0D1-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-BH-A0B2-01Z-00-DX1 TCGA-E2-A15E-01Z-00-DX1 TCGA-E2-A1IP-01Z-00-DX1 TCGA-F4-6857-01Z-00-DX1 TCGA-12-0773-01Z-00-DX4 TCGA-35-3621-01Z-00-DX1 TCGA-49-4486-01Z-00-DX1 TCGA-33-4587-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX2 TCGA-D9-A4Z6-01Z-00-DX1 TCGA-EE-A17Y-01Z-00-DX1 TCGA-EE-A29R-01Z-00-DX1 TCGA-EE-A2A0-01Z-00-DX1 TCGA-EE-A2MS-01Z-00-DX1 TCGA-ER-A199-01Z-00-DX1 TCGA-ER-A1A1-01Z-00-DX1 TCGA-ER-A2NC-01Z-00-DX1 TCGA-FS-A1Z7-06Z-00-DX10 TCGA-FS-A1Z7-06Z-00-DX11 TCGA-FS-A1Z7-06Z-00-DX12 TCGA-FS-A1Z7-06Z-00-DX13 TCGA-FS-A1ZN-01Z-00-DX10 TCGA-FS-A1ZN-01Z-00-DX11 TCGA-FS-A1ZW-06Z-00-DX10 TCGA-FS-A1ZW-06Z-00-DX11 TCGA-GN-A261-01Z-00-DX1 TCGA-GN-A266-01Z-00-DX1 TCGA-GN-A268-01Z-00-DX1 TCGA-GN-A26A-01Z-00-DX1 TCGA-XV-AB01-01Z-00-DX1 TCGA-AJ-A23O-01Z-00-DX1 TCGA-AP-A056-01Z-00-DX1 TCGA-BK-A139-01Z-00-DX1 TCGA-E6-A1M0-01Z-00-DX1</code></pre> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> </div> </div> <div>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</div> <div> </div> <div>[2] Hou, L., Gupta, R., Van Arnam, J. S., Zhang, Y., Sivalenka, K., Samaras, D., Kurc, T., & Saltz, J. H. (2019). Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.4A4DKP9U</div>
CryoGridLite: Model output of pan-Arctic simulations at 1° resolution from 1700 to 2020
<p><strong>CryoGridLite</strong> is a lightweight version of the more complex and process-rich <strong>CryoGrid Community model</strong> (<a href="https://gmd.copernicus.org/preprints/gmd-2022-127/">Westermann et al. 2022</a>).</p> <p>This archive contains the following output datasets which were produced by the model.</p> <p><code>./SETUP.zip</code> The parameters varied in the ensemble simulations.<br> <code>./RESULTS.zip</code> The output variables. An overview of the output variables is provided below and in the README.md file of the archived code.</p> <p>The model code can be found here: <a href="https://zenodo.org/record/6619537">10.5281/zenodo.6619537</a></p> <p>Input data required for pan-Arctic simulations can be found here: <a href="https://zenodo.org/record/6619212">10.5281/zenodo.6619212</a></p> <table> <thead> <tr> <th>Variable</th> <th>Coordinates</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><strong>H_lat_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total latent heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_sen_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total sensible heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_tot_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_lat_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total latent heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>H_sen_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total sensible heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>H_tot_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>ThawedGround_10m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Maximum vertically integrated depth of thawed ground (T>0°C) in the uppermost 10m of the ground column [m]</td> </tr> <tr> <td><strong>T_av_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Mean annual ground temperature [°C]</td> </tr> <tr> <td><strong>T_min_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Minimum annual ground temperature [°C]</td> </tr> <tr> <td><strong>T_max_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Maximum annual ground temperature [°C]</td> </tr> <tr> <td><strong>LandArea</strong></td> <td><em>(latitude, longitude)</em></td> <td>Total area covered by land within the model grid cell [km²]</td> </tr> </tbody> </table>
Genome assemblies of 118 Pisum accessions used for Pisum pan-genome analysis
<p>This repository stores genome assemblies of 118 <em>Pisum</em> accessions used for <em>Pisum</em> pan-genome anaylsis.</p> <p>Please refer to the supplementary data in publication and NCBI BioSamples for details.</p> <p>Associated NCBI BioProject : <strong>PRJNA730094</strong></p> <p>Correspondance : gaoshh@im.ac.cn</p>
The ScaleMaster: The decompostion of Pan-Scalar, Interactive Map (OSM,Google Maps,IGN scan)
<p>The ScaleMaster diagram of Brewer and Buttenfield, "where the scaleLine replaces the timeLine", is a formal tool (Excel sheets) designed to formalize the rules for manual map design and "emphasize changes to the map display" . Inspired by Brewer and Buttenfield, we use ScaleMaster to standardize and formalize changes while zooming and exploring each of pan-scalar map (OSM,Google Maps,Scan IGN). In our methodology, however, we go a step further. The timeline of exploration is also examined in addition to the scaleline of zooming. We focus on map design practices that account for pan-scalar map exploration. For example, we account for generalization changes between scales based on empirically or theoretically justifiable reasons.</p> <p>we use ScaleMaster to analyze particular and common geographic entities in the maps (including rivers, urban areas, bus stations, and administrative borders) representing but a fraction of all map ontologies (e.g., water, roads, transportation networks, relief, points-of-interest, vegetation, administrative districts). We constructed a ScaleMaster for each of the three pan-scalar maps (OSM, Google Map, Scan IGN). </p> <p>Our hope is that this first analysis, and the resulting categories below, will lead to critique, comment, and iterative improvement in the future. In other words, our initial findings are just that – outcomes that further exploration on pan-scalar maps can add to, revise, and improve upon. </p>
Clay pan / bowl
High poly pan/bowl with clay PBR texture. Feel free to use for your renders. Source: Objaverse 1.0 / Sketchfab
Frying pan
Inventory number: MT-E/3523 District Museum in Tarnów https://muzea.malopolska.pl/en/objects-list/2342 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.