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6,250 results for “Classification”
FIGURE 10 in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 10. Trichocoelina janetscheki (Lengersdorf, 1953) (from Greenland). A. Gonostylus, ventromedial. B. Hypopygium, ventral. Scale 0.1 mm.
FIGURE 2 in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 2. Hypopygium (A) and basal part of hypopygium (B), ventral. A. Trichocoelina biplex sp. n. (holotype). B. T. dicksoni sp. n. (holotype). Scale 0.1 mm.
FIGURE 9. Gonostylus, ventral. A in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 9. Gonostylus, ventral. A. Trichocoelina incrassata sp. n. (holotype). B. T. ithyspina sp. n. (holotype). C. T. magnifica sp. n. (holotype). D. T. jukkai sp. n. (holotype). Scale 0.1 mm.
FIGURE 18 in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 18. Intergonocoxal lobes of hypopygium, ventral. A. Trichocoelina aemula (holotype). B. T. dispansa sp. n. (paratype). C. T. ithyspina sp. n. (holotype). D. T. jukkai sp. n. (paratype). E. T. planilobata sp. n. (holotype). F. T. semusta sp. n. (holotype). Scale 0.05 mm.
FIGURE 15. Hypopygium, ventral. A in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 15. Hypopygium, ventral. A. Trichocoelina semusta sp. n. (holotype). B. T. tecta sp. n. (holotype). Scale 0.1 mm.
FIGURE 7. Hypopygium, ventral. A in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 7. Hypopygium, ventral. A. Trichocoelina incrassata sp. n. (holotype). B. T. magnifica sp. n. (holotype). Scale 0.1 mm.
FIGURE 6. Gonostylus, ventral. A in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 6. Gonostylus, ventral. A. Trichocoelina dispansa sp. n. (holotype). B. T. dividua sp. n. (holotype). C. T. hians (holotype). D. T. imitator (holotype). Scale 0.1 mm.
FIGURE 14. Hypopygium, ventral. A in Re-classification of Lycoriella Frey sensu lato (Diptera, Sciaridae), with description of Trichocoelina gen. n. and twenty new species
FIGURE 14. Hypopygium, ventral. A. Trichocoelina quintula sp. n. (holotype). B. T. semisphaera sp. n. (holotype). Scale 0.1 mm.
Figs 98-106 in Classification, Natural History, and Evolution of the Subfamily Peloniinae O (Coleoptera: Cleroidea: Cleridae). Part IX. Taxonomic revision of the New World genus Muisca S
Figs 98-106: Phalli. (98) Muisca dilatata. (99) M. insigna. (100) M. apicalis. (101) M. dozieri. (102) M. irrorata. (103) M. hirtula. (104) M. togata. (105) M. xanthura. (106) M. fera.
Figs 82-83 in Classification, Natural History, and Evolution of the Subfamily Peloniinae O (Coleoptera: Cleroidea: Cleridae). Part IX. Taxonomic revision of the New World genus Muisca S
Figs 82-83: Various organs. (82) M. octonotata, head, ventral view. (83) M. octonotata, forebody, ventral view.
Figs 2-13 in Classification, Natural History, and Evolution of the Subfamily Peloniinae O (Coleoptera: Cleroidea: Cleridae). Part IX. Taxonomic revision of the New World genus Muisca S
Figs 2-13: Various structures of Muisca testacea. (2) Head, frontal view. (3) Head, ventral view. (4) Head, dorsal view. (5) Prothorax, ventral view. (6) Antenna, male. (7) Spicular fork. (8) Maxilla. (9) Labrum. (10) Metendosternite. (11) Mandible. (12) Labium. (13) Metathoracic wing.
Library Service Platforms (LSPs) characteristics classification via DELPHI method raw data
<p>The purpose of the research that is related to the dataset is to identify the innovative features of the LSPs (Library Service Platforms) that differentiate them from the LMS (Library Management Systems), as well as to evaluate their importance, based on the opinions of the Greek information scientists. The method used is the Delphi 2-round questionnaire. The dataset contains the results of the Delphi method. Each sheet contains the ranking results from a group of 17 experts from two rounds of the DELPHI method.</p>
Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression
<p>Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.</p>
Datasets of ASONAM-2015 paper "Tweet sentiment: From classification to quantification"
<p>Datasets used for the following ASONAM 2015 paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: Tweet Sentiment: From Classification to Quantification<br> Authors: Wei Gao and Fabrizio Sebastiani<br> Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar<br> ---------------------------------------------------------------------------------------------------</p> <p>[Content]</p> <p>* SemEval2013, SemEval2014, SemEval2015 datasets:<br> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: sanders, sst, omd, hcr, gasp<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Traing set for learning the final sentiment model<br> - X.test.feature.txt: Test set<br> where X is one of sanders, sst, omd, hcr and gasp.</p> <p>For more details, please refer to the paper.</p> <p><br> [Citation]<br> You can cite the folowing paper when referring to the dataset:</p> <p>@inproceedings{gao2015tweet,<br> title={Tweet sentiment: From classification to quantification},<br> author={Gao, Wei and Sebastiani, Fabrizio},<br> booktitle={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},<br> pages={97--104},<br> year={2015},<br> organization={IEEE}<br> }</p>
FIG. 1 in Le mirage des classifications naturelles. Remarques critiques sur un totem, d'Aristote à l'anthropologie contemporaine
FIG. 1. — Représentation de la catégorie des oiseaux avec distance variable visà-vis du prototype (Rouge-gorge), selon la théorie de Rosch, pour les États-Unis.
GLC_FCS30-2020:Global Land Cover with Fine Classification System at 30m in 2020
<p>The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset. </p>
Figure 15 in Noyesaphytis (Chalcidoidea: Aphelinidae) - an unusual new genus from Madagascar, and a reassessment of Aphelininae classification based on morphology
Figure 15. Phylogenetic relationships and inferred tribal classification of Aphelininae based on a maximum likelihood analysis of 50 morphological characters. The tree shown had the greatest log-likelihood score from 50 independent runs. Numbers above branches indicate support values from 1000 ultrafast bootstrap replicates. Eretmocerus hayati was designated as the outgroup. Taxa followed by an asterisk were described since Kim and Heraty (2012)
Figure 4 in Noyesaphytis (Chalcidoidea: Aphelinidae) - an unusual new genus from Madagascar, and a reassessment of Aphelininae classification based on morphology
Figure 4. Noyesaphytis lasallei holotype female; 4. Right antenna, internal aspect, detail of anelli.
Figure 10 in Noyesaphytis (Chalcidoidea: Aphelinidae) - an unusual new genus from Madagascar, and a reassessment of Aphelininae classification based on morphology
Figure 10. Noyesaphytis lasallei paratype male; 10. Scanning electron micrograph, dorsal head and mesosoma.
Figure 14 in Noyesaphytis (Chalcidoidea: Aphelinidae) - an unusual new genus from Madagascar, and a reassessment of Aphelininae classification based on morphology
Figure 14. One of eighteen most parsimonious trees generated using successive approximations weighting from a phylogenetic analysis of 50 morphological characters (Length: 252; CI: 0.32; RI: 0.72). Bootstrap values greater than 50 are shown above branches. Eretmocerus hayati was designated as the outgroup. Taxa followed by an asterisk were described since Kim and Heraty (2012)
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.