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1,944 results for “Class I”
Order of Polonia Restituta IV Class – a case
The Officer's Cross of the Order of Polonia Restituta IV Class – a case ID no.: MNS-H/4198 Museum: Nowy Sącz District Museum https://muzea.malopolska.pl/en/objects-list/2343 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Male dingo urinary scents code for age class and wild dingoes respond to this information
<p>Chemical information in canid urine has been implicated in territoriality and influences the spacing of individuals. We identified the key volatile organic compound (VOC) components in dingo (<em>Canis lupus dingo</em>) urine and investigated the potential role of scents in territorial spacing. VOC analysis, using headspace gas chromatography-mass spectrometry (GC-MS), demonstrated that the information in fresh urine from adult male dingoes was sufficient to allow statistical classification into age categories. Discriminant function analyses demonstrated that the relative amounts or combinations of key VOCs from pre-prime (3-4 years), prime (5-9 years), and post-prime (≥10 years) males varied between these age categories, and that scents exposed to the environment for 4 (but not 33) days could still be classified to age categories. Further, a field experiment showed that dingoes spent less time in the vicinity of prime male dingo scents than other scents. Collectively, these results indicate that age-related scent differences may be discriminable by dingoes. Previous authors have suggested the potential to use scent as a management tool for wild canids by creating an artificial territorial boundary/barrier. Our results suggest that identifying the specific signals in prime-age male scents could facilitate the development of scent-based tools for non-lethal management.</p>
Forest inventory, leaf area index, and leaf functional traits of various land cover classes in Kulen, Cambodia
<ol><li><strong>Sub-title 1: </strong>Forest inventory of evergreen forest, regrowth forest, and evergreen forest in Kulen, Cambodia. (<strong>File name: </strong><i>Forest_Inventory_Pub.txt)</i> <strong> </strong></li><li><strong>Sub-title 2: </strong>Species leaf area, chlorophyll a and b and leaf dry matter content of 30 species collected from evergreen forests, regrowth forests, and cashew plantation in Kulen, Cambodia. (<strong>File name:</strong> <i>Leaf_Trait_Species_Pub.txt)</i></li><li><strong>Sub-title 3: </strong>Canopy and total leaf area index from evergreen forests, regrowth forests, and cashew plantation in Kulen, Cambodia. (<strong>File name: </strong><i>LAI_Pub.txt </i>)</li></ol>
Lineage-Specific Class-A GPCR Dynamics Reflect Diverse Chemosensory Adaptations in Lophotrochozoa
Open the record for dataset details and reuse information.
Jemma Metadata Classes
<p>Jemma Metadata Classes</p> <p>> file: Jemma_Metadata_Classes.csv<br> > size: 269.7 MB<br> > num_rows: 1,184,186<br> > columns: project_id, package_id, class_id, class_path, class_name</p>
Segmentation Zoo UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.
<p><strong>Doodleverse/Segmentation Zoo UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.</strong></p> <p>These UNet model data are based on the Coast Train v1 Landsat-8 labeled imagery subset. Models have been fitted to 4 different types of data</p> <p>1. NDWI (1 band): (g-nir)/(g+nir)</p> <p>2. MNDWI (1 band): (swir-g)/(swir+g)</p> <p>3. RGB (3 band): red, green, blue</p> <p>4. RGB-NIR-SWIR (5 band): red, green, blue, nir, swir</p> <p>Classes are: {0: water, 1: whitewater, 2:sediment, 3:other}. These classes have been remapped from the original 11 classes<br> </p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p>
Segmentation Zoo Res-UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.
<p><strong>Doodleverse/Segmentation Zoo models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.</strong></p> <p>These model data are based on the Coast Train v1 Landsat-8 labeled imagery subset. Models have been fitted to 4 different types of data</p> <p>1. NDWI (1 band): (g-nir)/(g+nir)</p> <p>2. MNDWI (1 band): (swir-g)/(swir+g)</p> <p>3. RGB (3 band): red, green, blue</p> <p>4. RGB-NIR-SWIR (5 band): red, green, blue, nir, swir</p> <p>Classes are: {0: water, 1: whitewater, 2:sediment, 3:other}. These classes have been remapped from the original 11 classes<br> </p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files:</p> <p>1. config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2. weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3. model card file: this is a json file containing the following fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p> <p> </p>
Wikidata Subsets of 4 Gene Wiki Classes
<p>Chemical compound (Q11173), disease (Q12136), gene (Q7187), and protein (Q8054) are some of the main classes containing the Gene Wiki WikiProjects. In this repository, we put the corresponding subsets of each class. The subsets contain all instances of the four main classes (no sub-classes). All subsets are extracted from the Wikidata JSON dump of 3 January 2022 (<a href="https://t.co/vdmJc8V1v2">https://t.co/vdmJc8V1v2</a>)</p>
Wikidata Subsets of 4 Gene Wiki Classes (wdsub)
<p>Chemical compound (Q11173), disease (Q12136), gene (Q7187), and protein (Q8054) are some of the main classes containing the Gene Wiki WikiProjects. In this repository, we put the corresponding subsets of each class. The subsets contain all instances of the four main classes (no sub-classes). All subsets are extracted from the Wikidata JSON dump of 3 January 2022 (<a href="https://t.co/vdmJc8V1v2">https://t.co/vdmJc8V1v2</a>) using wdsub (https://github.com/weso/wdsub)</p>
Planar Graph Classifier - Class Information
<p>CSV files associating image files from the Planar Graph Classifier - Generated Graph Images (<a href="https://doi.org/10.5281/zenodo.6510092">10.5281/zenodo.6510092</a>) with planarity information.</p> <p>Class 0: graph drawn with networkx spring layout<br> Class 1: graph drawn with networkx planar layout </p>
Data from: Neurotransmitter content heterogeneity within an interneuron class shapes inhibitory transmission at a central synapse
<p>Neurotransmitter content is deemed the most basic defining criterion for neuronal classes, contrasting with the intercellular heterogeneity of many other molecular and functional features. Here we show, in the adult mouse brain, that neurotransmitter content variegation within a neuronal class is a component of its functional heterogeneity. Most Golgi cells (GoCs), the well-defined class of cerebellar interneurons inhibiting granule cells (GrCs), contain cytosolic glycine, accumulated by the neuronal transporter GlyT2, and GABA in various proportions. To assess the functional consequence of this neurotransmitter variation, we paired GrCs recordings with optogenetic stimulations of single GoCs, which preserve the intracellular transmitter mixture. We show that the strength and decay kinetics of GrCs IPSCs, which are entirely mediated by GABA<sub>A</sub> receptors are negatively correlated to the presynaptic expression of GlyT2 by GoCs. We isolate a slow spillover component of GrCs inhibition that is also affected by the expression of GlyT2, leading to a 56 % decrease in relative charge. Acute manipulations of cytosolic GABA and glycine supply recapitulate the modulation of IPSC charge, supporting the hypothesis that presynaptic loading of glycine negatively impact the GABAergic transmission in mixed interneurons through a competition for vesicular filling. Our results suggest that heterogeneity of neurotransmitter supply within the GoC class may provide a presynaptic mechanism to tune the gain of the stereotypic granular layer microcircuit, thereby expanding the realm of possible dynamic behavior.</p>
MHC class II genotype-by-pathogen genotype interaction for infection prevalence in a natural rodent-Borrelia system
<p><span>MHC genes are extraordinarily polymorphic in most taxa. Host-pathogen coevolution driven by negative frequency-dependent selection (NFDS) is one of the main hypotheses for the maintenance of such immunogenetic variation. Here we test a critical but rarely tested assumption of this hypothesis—that MHC alleles affect resistance/susceptibility to a pathogen in a strain-specific way, i.e. that there is a host genotype-by-pathogen genotype interaction. </span><span>In a field study of bank voles naturally infected with the tick-transmitted bacterium </span><em><span>Borrelia afzelii</span></em><span>, we tested for MHC class II (</span><em><span>DQB</span></em><span>) genotype-by-</span><span><em>B. afzelii</em> </span><span>strain interactions for infection prevalence between ten </span><em><span>DQB</span></em><span><em> </em>alleles and seven strains. One allele (<em>DQB</em>*37) showed an interaction</span><span>, </span><span>such that voles carrying <em>DQB</em>*37 had higher prevalence of two strains and lower prevalence of one strain than individuals without the allele. These findings were corroborated by analyses of strain composition of infections, which revealed an effect of <em>DQB</em>*37 in the form of lower </span><span>b</span><span> diversity among infections in voles carrying the allele. Taken together, these results provide rare support at the molecular genetic level for a key assumption of models of antagonistic coevolution through NFDS. </span></p>
Simple Steam Engine 3 (GWR 4400 Class)
After 10 incredibly long months, I'm proud to present yet another low=poly steam engine. It was an interesting experience getting back into modelling, and I'd like to thing that this came out pretty well. The railing is a little wonky so i may revist this model in the future, but it should be sufficient for your digital steam engine needs... whatever those are. As always, the cabin was the hardest part to get right, but by the grace of God it came out alright. It may not have been the best decision to combine the smokebox and crew cabin, but I wanted to avoid Z-fighting in the final product. Source: Objaverse 1.0 / Sketchfab
Gene and protein sequence features augment HLA class I ligand predictions
<p>Dataset and analyses supporting the manuscript "Gene and protein sequence features augment HLA class I ligand predictions".</p> <p>The "peptides" files contain the mass-spec detected peptides obtained from HLA ligandomics performed on the indicated tumor lines. </p> <p>The "protein data" files contain the RNAseq data (TPM) and Ribosome profiling data (ribosome occupancy) per protein, for each tumor line. </p> <p>The "source data" zip archive contains the source data underlying the figures of the manuscript.</p> <p>The "HLA ligandome analyses" zip archive contains the R scripts used for all data analysis in the manuscript, including all data and output files. These analyses can also be found at https://github.com/kasbress/HLA_Ligandome_Analyses/</p> <p> </p> <p> </p>
2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
<p>This dataset consists of 615 images of <em>Scaphoideus titanus</em> (ST) and <em>Orientus ishidae</em> (OI) from yellow sticky traps (YST). Among these, 150 photos, which lack target insects, have been repurposed as background images. Insect annotations comprise 1329 ST and 1506 for OI, ensuring an almost class-balanced dataset. The images were acquired through four distinct methods:</p> <ul> <li>Photos from the field;</li> <li>Images of stored YST (T = 5±1°C) and reared insects within a controlled greenhouse environment;</li> <li>Digital scans of YST collected during regular monitoring activities in the fields;</li> <li>Photos from a smart trap prototype installed in our experimental vineyard.</li> </ul> Structure of the dataset, showing the number of images from each data source and the corresponding class annotations. <table><tbody> <tr> <td>Image source</td> <td>Number of images</td> <td>ST annotations</td> <td>OI annotations</td> <td>Number of background images</td> </tr> <tr> <td> <p>Field</p> </td> <td>18</td> <td>3</td> <td>101</td> <td>8</td> </tr> <tr> <td>Laboratory</td> <td>157</td> <td>473</td> <td>863</td> <td>8</td> </tr> <tr> <td>scanned</td> <td>390</td> <td>853</td> <td>542</td> <td>84</td> </tr> <tr> <td>smart-trap</td> <td>50</td> <td>0</td> <td>0</td> <td>50</td> </tr> </tbody> </table> <p>We provide the yellow sticky trap images already cropped in the pre-processing stage, the corresponding enhanced datasets focusing on <em>brightness & contrast</em>, <em>sharpness</em>, and a combination of both. Finally the annotations exported in YOLO format.</p> <h3>Dataset structure</h3> <ol> <li><em>crop/</em></li> <li><em>bright/</em></li> <li><em>sharp/</em></li> <li><em>bright_and_sharp/</em> </li> <li><em>labels/</em></li> </ol> <p>At the time of publication, this dataset is the largest publicly available resource in the control of FD vectors. Detailed documentation, along with model benchmarking and performance results is given in an accompanying journal paper: (paper under submission).</p> <h3>Deployment</h3> <p>You can use this dataset as starting point to train your own insect detection models. Open source Python scripts to deploy the trained models can be found in our <a href="https://github.com/checolag/insect-detection-scripts/tree/main">Github</a> repository.</p>
Improved estimation of the prevalence of bovine cysticercosis and the diagnostic test characteristics in the absence of a reference standard using Bayesian Latent Class models, the example of Jimma and Ambo Abattoirs, Ethiopia
<p>Bovine cysticercosis is an infection of cattle musculature with the cestode parasite of humans known as Taenia saginata. This bovine cysticercosis data was collected from two Ambattoirs in Ethiopia namely Ambo and Jimma. Dissection of the predilection site, Ag-ELISA, and meat inspection were the diagnostic methods employed. Cysticerci collected during dissection of the predilection site were also confirmed using multiplex PCR. </p>
Figure 1 in Communities of the Bidentetea class of small coastal river valleys of the Western Pomerania (Poland)
Figure 1. Map of study area (the north of Western Pomerania, Poland).
the proportion of classes of techniques of works of art (source: https://kor.uni-frankfurt.de)
<p>Data<br> * records representing works of arts were classified orcording to their technique/material<br> ** not every work of art had a technique/material value<br> ** not all technique/material values have been included into one of the classes</p> <p>Processing<br> ** the script uses d3.js</p> <p> </p>
Percentage of individuals per size class of available prey for Setophaga petechia gundlachi
<p>Supplemetary materials 3. Percentage of individuals per size class of available prey for <em>Setophaga petechia gundlachi </em>during the reproductive season in Bajo de Santa Ana (n = 159) and Laguna de Cobre-Itabo (n = 813) of Havana, Cuba.</p>
Equivalence Classes of the LOD Cloud
<p>This data set contains all the 49 million non-singleton equivalence classes resulting from the transitive closure of over 556 million owl:sameAs statements extracted from the LOD Cloud in the 2015 LOD Laundromat crawl. These equivalence classes are the result of the transitive closure of the owl:sameAs links available in the <a href="http://sameas.cc">sameAs.cc</a> data set.</p> <p>We represent these non-singleton equivalence classes using two CSV files:</p> <p>1. id2terms.csv: contains in the first column the equivalence class identifier (randomly generated number) and in the rest of the columns all IRIs belonging to this equivalence class, which theoretically should refer to the same real world entity. In the following, we present an example of one row of this file, where "42467584" in the first column represents the ID of this equivalence class, and the 4 other columns represent the IRIs that are identical after transitive closure: </p> <blockquote> <p>42467584 <http://nl.dbpedia.org/resource/Cnodocentron_trilineatum> <http://sv.dbpedia.org/resource/Cnodocentron_trilineatum> <http://vi.dbpedia.org/resource/Cnodocentron_trilineatum> <http://www.wikidata.org/entity/Q2304468></p> </blockquote> <p>2. terms2id.csv: contains two columns, representing a mapping between each IRI in the sameAs.cc data set involved in a owl:sameAs link with the equivalence class it belongs to. In the following, we present an example of one row in this file:</p> <blockquote> <p><http://nl.dbpedia.org/resource/Cnodocentron_trilineatum> 42467584</p> </blockquote> <p>In addition to the closure of all owl:sameAs links (available in the folder <em><strong>closure_all.zip</strong>), </em>this data set contains an additional two closures, with each closure also containing two CSV files with the same structure as presented above. These two additional closures are the following:</p> <p><strong>- <em>closure_099.zip</em> </strong>represents the closure of all owl:sameAs links in the sameAs.cc data set after discarding around 1 million probably erroneous owl:sameAs links (with error degree >0.99). This error degree is computed based on the community structure of the network, described in the approach of <a href="https://www.cs.vu.nl/~frankh/postscript/ISWC2018.pdf">[Raad et al., 2018]</a>.</p> <p><strong>- </strong><em><strong>closure_04.zip</strong> </em>represents the closure of all owl:sameAs links in the sameAs.cc data set after discarding around 150 million owl:sameAs links (with error degree >0.4). The evaluation conducted in <a href="https://www.cs.vu.nl/~frankh/postscript/ISWC2018.pdf">[Raad et al., 2018]</a> shows that the 400M owl:sameAs links with an error degree <= 0.4 have higher probability of correctness compared to other links.</p> <p>The availability of these 3 different closures allows Linked Data practitioners for the first time to control in practice, the trade-off between (a) using more identity links, possibly not all correct, and benefiting from more contextual information from the LOD Cloud, and (b) using a smaller subset of higher quality identity links for limiting the risk of propagating erroneous identity links and information through the application of owl:sameAs semantics, i.e. transitive, symmetric, reflexive and property sharing.</p>
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.