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1,254 results for “PANs”
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2001): BSF, minNDTI, and NOS
<h2><strong>Data information</strong></h2> <p>This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2001. They are calculated as follows:</p> <ul> <li><strong>minNDTI (minimum NDTI)</strong> is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li><strong>BSF (bare soil fraction)</strong> is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li><strong>NOS (number of seasons)</strong> indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2003): BSF, minNDTI, and NOS
<h2><strong>Data information</strong></h2> <p>This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2003. They are calculated as follows:</p> <ul> <li><strong>minNDTI (minimum NDTI)</strong> is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li><strong>BSF (bare soil fraction)</strong> is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li><strong>NOS (number of seasons)</strong> indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2015): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2015. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Pan-genomic analysis highlights genes associated with agronomic traits and enhances genomics-assisted breeding in alfalfa
Open the record for dataset details and reuse information.
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p025, 2020–2022</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2016–2020</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Bonneville Salt Flats saline pan ground and surface water fluctuations, geochemical, and well construction data
<p>Data archive of geochemical, water depth, and brine flux data for the Bonneville Salt Flats.</p> <p> </p>
FIGURE. Flemingia vestita Benth. ex Baker A & B from B. Liu 550 (two sheets in HITBC), showing the persistent stipels of leaves. C. Pod enclosed by calyx. D. Pod. E. Seed. A & B by Bo Pan, C, D & E by Kai-Wen Jiang from B. Pan s. n. (NPH), a seed specimen collected from Yunnan, China. in Legume additions to the flora of China
FIGURE. Flemingia vestita Benth. ex Baker A & B from B. Liu 550 (two sheets in HITBC), showing the persistent stipels of leaves. C. Pod enclosed by calyx. D. Pod. E. Seed. A & B by Bo Pan, C, D & E by Kai-Wen Jiang from B. Pan s. n. (NPH), a seed specimen collected from Yunnan, China.
FIGURE. Desmodium uncinatum (Jacq.) DC. A. Habit. B. Mature leaf. C. Young leaf. D. Abaxial surface of leaflets. E. Inflorescence. F. Loments. Photographs by Pan Li. in Legume additions to the flora of China
FIGURE. Desmodium uncinatum (Jacq.) DC. A. Habit. B. Mature leaf. C. Young leaf. D. Abaxial surface of leaflets. E. Inflorescence. F. Loments. Photographs by Pan Li.
The effect of methodological considerations on the construction of gene-based plant pan-genomes
<p>Pan-genomics is an emerging approach for studying the genetic diversity within plant populations. In contrast to common resequencing studies that compare whole genome sequencing data to a single reference genome, the construction of a pan-genome involves the direct comparison of multiple genomes to one another, thereby enabling the detection of genomic sequences and genes not present in the reference, as well as the analysis of gene content diversity. While multiple studies describing pan-genomes of various plant species have been published in recent years, our understanding regarding the effect of the computational procedures used for pan-genome construction is still limited.</p> <p>Here we examine the effect of several key methodological factors on the obtained gene pool and on gene presence-absence detections by constructing and comparing multiple pan-genomes of Arabidopsis thaliana and cultivated soybean, as well as conducting a meta-analysis on published pan-genomes. These factors include the construction method, the sequencing depth, and the extent of input data used for gene annotation. We observe substantial differences between pan-genomes constructed using three common procedures (De novo assembly and annotation, Map-to-pan, and Iterative assembly), and that results are dependent on the extent of the input data. Specifically, we report low agreement between the gene content inferred using different procedures and input data. Our results should increase the awareness of the community to the consequences of methodological decisions made during the process of pan-genome construction and emphasize the need for further investigation of commonly applied methodologies.</p>
Supporting files for: "So volcanoes created the dinosaurs? A quantitative characterization of the early evolution of terrestrial Pan-Aves"
<p>Supplementary files for the manuscript "So volcanoes created the dinosaurs? A quantitative characterization of the early evolution of terrestrial Pan-Aves", published in Frontiers in Earth Science. The zip folder contains 3 subfolders, with datasets, scripts and results of analyses. </p>
Data from: The fossil record and phylogeny of the auklets (Pan-Alcidae, Aethiini)
The auklets Aethia and Ptychoramphus comprise the smallest known Alcidae (Aves, Charadriiformes) and have a fossil record that extends into the Miocene. The evolution of auklets is poorly understood because systematic hypotheses of relationships among extant auklets are largely incongruent, the morphology of auklet fossils has not been evaluated in detail, and extinct species of auklets have not been previously included in a phylogenetic analysis. Previously described auklet fossil remains are reviewed and two new species of auklet, Aethia barnesi and Aethia storeri, are described from the Miocene and Pliocene of southern California, USA. Previously described auklet fossil remains, the two newly described extinct species of auklet, and extant species of auklets and other alcids are included in combined phylogenetic analyses of morphological and molecular sequence data. Based on the results of the phylogenetic analyses, the taxonomy of fossils referred to Aethiini is revised and the evolution of the clade is evaluated in a phylogenetic context. The osteological morphology of extinct auklets appears to be little-changed from their extant relatives, suggesting that the ecological attributes of these small wing-propelled divers may also be relatively unchanged since the Miocene.
Pan troglodytes (modern) (2501.1rp72)
***Pan troglodytes*** Location: Africa. Age: modern. Material: epoxy resin cast. Dimensions: length, 105 mm; width, 117 mm; height, 263 mm. Notes: RLA catalog no. 2501.1rp72 (cast). Male chimpanzee right innominate. Cast made by the Wenner-Gren Foundation Casting Program at the University Museum of the University of Pennsylvania. From the teaching collection of the Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Joy Mersmann. Source: Objaverse 1.0 / Sketchfab
Pan troglodytes (modern) (2501.1rp4-1)
***Pan troglodytes*** Location: Africa. Age: modern. Material: epoxy resin cast. Dimensions: length, 195 mm; width, 130 mm; height, 105 mm. Notes: RLA catalog no. 2501.1rp4-1 (cast). Male chimpanzee cranium. Cast manufacturer unknown. From the teaching collection of the Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Jordyn Gray. Source: Objaverse 1.0 / Sketchfab
Pan
Bust of Pan in the Hirschstetten flower gardens in Vienna's 22nd district. The god of forests and meadows is in the midst of trees in the pinales garden. Source: Objaverse 1.0 / Sketchfab
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Pan-cancer networks for TREE, including six homogeneous and two heterogeneous networks
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Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability
<p>"Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability", F. Raynaud, M. Mina, D. Tavernari and G. Ciriello</p> <p>These files are necessary to generate the supplementary table containing:</p> <p>#Sample_name #Cancer_type #Cancer_subtype #Mean_reads_per_mutations #Number_of_mutations #Number_of_altered_segments #Number_of_clones #TreeScore #Number_of_mutations_first_clone #Number_of_mutations_other_clones</p> <p>REQUIREMENTS</p> <p>Phylogenies generated by PhyloWGS (PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors](<a href="http://genomebiology.com/2015/16/1/35">http://genomebiology.com/2015/16/1/35</a>), Deshwar et al.)</p> <p>Output files from PhyloWGS for each sample:</p> <ul> <li> <p>top_k_trees which contains the best phylogenies (50 by default) with the label of each clone, the population frequency of each clone, the number of children of each clone, number of mutations in the clone, the labels of the mutations</p> </li> <li> <p>top_k_trees1, top_k_trees2, ... , top_k_treesN output file for the best k trees (50 by default)</p> </li> </ul> <p>FILES</p> <p>*Molecular data for the tumor types: CESC, UCEC, UVM, THCA, KICH, BRCA, SKCM, ACC, CRC, STAD, BLCA, LUAD, KIRP, PRAD, LIHC; has been collected in July 2015 *Molecular data for the tumor types: SARC, PAAD, MESO, LGG, GBM, DLBC, UCS, THYM, TGCT, PCPG, OV, LUSC, LAML, KIRC, HNSC, ESCA, CHOL; has been collected in 2018</p> <p>from the FireHose (<a href="https://gdac.broadinstitute.org/">https://gdac.broadinstitute.org/</a>) and cBioPortal (Cerami et al., 2012) (<a href="http://www.cbioportal.org/">http://www.cbioportal.org/</a>) data repositories for The Cancer Genome Atlas (TCGA). Only TCGA datasets publicly available at that time were used in our study.</p> <p>-Mutation files (MAF format): combined_2015_2018_MAF.maf.bz2</p> <p>-Copy number segmentation files: combined_seg_2015_2018.seg.bz2</p> <p>-analyze_public.py: Python file to generate the Supplementary Table run: python2.7 analyze_public.py</p> <p>- All input data and results from PhyloWGS: PhyloWGS_input_output.tar.bz2 </p>
Pan-cancer atlas of endothelial cells
<p>Datasets used to generate a pan-cancer atlas of endothelial cells and additional datasets generated with sorted endothelial cells used for validation.</p>
40-year monthly mean AVHRR GAC Land Surface Temperature data for the Pan-Arctic region (Pan-Arctic AVHRR LST)
<p><em>This data collection contains 40 years of monthly mean daytime AVHRR Global Area Coverage (GAC) land surface temperature (LST) data. This dataset covers the 1981-2020 perdiod and covers the whole globe above 50° latitude. The spatial extent of the dataset is the following : (-180°, 50°N) ; (180°, 90°N)</em></p> <p><strong>Dataset description:</strong></p> <p>The LST monthly mean composites are computed from daily daytime LST files, that were generated from the EUMETSAT AVHRR PyGAC FDR (https://navigator.eumetsat.int/product/EO:EUM:DAT:0862) as described in Dupuis et al. (2024). These daily LST files contain only cloud-free pixels and pixels with sufficient quality regarding satellite zenith angle and error margin from the radiative transfer modelling. The probabilistic cloud mask from the CLARA-A3 (https://navigator.eumetsat.int/product/EO:EUM:DAT:0874) dataset has been used. The LST monthly means do not contain any water masks, as potential users might have different requirements regarding water masks. The dataset has been validated against in situ data from the SURFRAD (https://gml.noaa.gov/grad/surfrad/overview.html), ARM (https://arm.gov/capabilities/observatories/nsa) and KIT (https://www.imk-asf.kit.edu/english/skl_stations.php) networks.</p> <p><strong>Data & File Overview:</strong></p> <p>Short description: AVHRR GAC LST daytime monthly mean composites: daily land surface temperature data are averaged to monthly composites for every afternoon and mid-day satellite (10 different satellites).</p> <ul> <li>File List: This dataset contains monthly daytime land surface temperature (LST) data for the AVHRRs onboard NOAA and MetOp satellites. </li> <li>Filename: Pan_Arctic_LST_avhrr_XXXXX_YYYYMM_DAY__***.nc, where XXXXX represents the satellite identifier, YYYYMM the monthly timestamp (YYYY=year, MM=month) and *** the timestamp of the file generation.</li> <li>Relationship between files: Each file covers a one-month period and is recorded by a different satellite.</li> </ul> <p>Satellite identifiers:<br><em>AVN07 : NOAA 7</em><br><em>AVN09 : NOAA 9</em><br><em>AVN11 : NOAA 11</em><br><em>AVN14 : NOAA 14</em><br><em>AVN16 : NOAA 16</em><br><em>AVN18 : NOAA 18</em><br><em>AVN19 : NOAA 19</em><br><em>AVMEA : MetOp-A</em><br><em>AVMEB : MetOp-B</em><br><em>AVMEC : MetOp-C</em></p> <p><strong>Data specific information:</strong></p> <p>The LST files are available as a gridded product in the WGS84 coordinate reference system and are distributed as NetCDF files. The dataset covers the pan-Arctic region (-180°, 90°, 180°, 50°) at a spatial resolution of 0.05°x0.05° pixel size.<br>Each *.nc file contains one variable (LST) with three dimensions (time, lat, lon) and five coordinates (time, lat, lon, band and spatial_ref).</p> <p>- spatial_ref (): stores the spatial information, such as the coordinate reference system (CRS) and WKT string.<br>- time (time): stores the timestamp, here the month and the year of the monthly mean. The timestamp is the same for all pixels belonging to the same composite.<br>- lat (lat): stores the latitude of each pixel<br>- lon (lon): stores the longitude of each pixel<br>- band (): empty inherited layer </p> <p> </p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Dupuis, S., Göttsche, F.-M., & Wunderle, S. (2024). Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region. <em>The Cryosphere, 18</em>(12), 6027-6059. <a href="https://doi.org/10.5194/tc-18-6027-2024" target="_blank" rel="nofollow noopener">https://doi.org/10.5194/tc-18-6027-2024</a></p> <p> </p> <div> <div><span>@Article</span><span>{</span><span>tc-18-6027-2024</span><span>,</span></div> <div><span>AUTHOR</span><span> = </span><span>{</span><span>Dupuis, S. and G\"ottsche, F.-M. and Wunderle, S.</span><span>}</span><span>,</span></div> <div><span>TITLE</span><span> = </span><span>{</span><span>Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region</span><span>}</span><span>,</span></div> <div><span>JOURNAL</span><span> = </span><span>{</span><span>The Cryosphere</span><span>}</span><span>,</span></div> <div><span>VOLUME</span><span> = </span><span>{</span><span>18</span><span>}</span><span>,</span></div> <div><span>YEAR</span><span> = </span><span>{</span><span>2024</span><span>}</span><span>,</span></div> <div><span>NUMBER</span><span> = </span><span>{</span><span>12</span><span>}</span><span>,</span></div> <div><span>PAGES</span><span> = </span><span>{</span><span>6027--6059</span><span>}</span><span>,</span></div> <div><span>URL</span><span> = </span><span>{</span><span>https://tc.copernicus.org/articles/18/6027/2024/</span><span>}</span><span>,</span></div> <div><span>DOI</span><span> = </span><span>{</span><span>10.5194/tc-18-6027-2024</span><span>}</span></div> <div><span>}</span></div> </div> <p> </p> <p><strong>Information about funding sources that supported the collection of the data:</strong><br>Dr. Alfred Bretscher Fund (University of Bern)</p> <p> </p>
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