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6,250 results for “Classification”
Vegetation history classification for Watersheds 1, 2, and 3, Andrews Experimental Forest, 1959-1990
The objective of this study was to create GIS layers depicting vegetation cover over the history of the small experimental watersheds (WS 1, 2, and 3) based on aerial photography from the 1950s to 1990, for landscape ecology and spatial modeling studies. Aerial photos were interpreted for hydrologically relevant vegetation types (conifer, broadleaf, grasses and bare soil) cover classes, and forest age class was determined for each year (1959, 1962, 1967, 1972, 1979, and 1990). Vegetation data for functional groups (conifer, evergreen broadleaf, deciduous broadleaf) was aggregated from species data available in TP073 for the long term vegetation plots in Watershed 1.
A Land-use/Land Cover Classification of Baltimore City in 1927
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.
The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.
Paralog variant classification and scoring
<p><em>Para_zscore </em>data</p> <p>Input data, annotation of all hg19 missense variants, score for every gene having a paralog in the human genes. This dataset is a supplement for the publication Lal. et al.</p> <p>Information on the files, scripts to generate and use the <em>para_zscore</em> are available under</p> <p>https://git-r3lab.uni.lu/genomeanalysis/paralogs.</p> <p>Version 3582386 updates:</p> <p>- Annovar annotation file for hg38 added</p> <p> </p>
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems
<p><a href="http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf"><strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong></a></p> <p>This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.</p> <p>You can find the code for training testing the models, that was published along with the paper in this <a href="https://github.com/oliverguhr/german-sentiment">repository</a>.</p> <p>The <a href="https://github.com/oliverguhr/german-sentiment-lib"><em>germansentiment</em></a> Python package contains a easy to use interface for the model that was published with this paper.</p> <p> </p> <p> </p>
Fig. 10 in Review of the genus classification of Abiinae (Cimbicidae, Hymenoptera)
Fig. 10. Topology of Abiinae after EW analysis. Other subfamilies of Cimbicidae collapsed, outgroup not shown. Allabia infernalis marked with red, taxa placed in Orientabia Malaise, 1934 according to Taeger et al. (2010) marked with green. Abia fasciata (Linnaeus, 1758) (type species of Zaraea) marked with blue, Abia sericea (Linné, 1767) (type species of Abia) marked with yellow.
Fig. 3 in Review of the genus classification of Abiinae (Cimbicidae, Hymenoptera)
Fig. 3. Habitus lateral, females, except D, which shows ovipositor (upside down). A. Abia akebii (Takeuchi, 1931) (NSMT). B. Abia berezowskii Semenov, 1896 (NRMS). C–D. Abia candens (Konow, 1887) (NHMD). E. Abia metallica Mocsáry, 1909 (NSMT). F. Abia pulcherrima Mallach, 1930 (NRMS). G. Abia niui Wei & Deng, 1999 (NMNH). H. Abia relativa Rohwer, 1910 (NSMT).
Fig. 6 in Review of the genus classification of Abiinae (Cimbicidae, Hymenoptera)
Fig. 6. Head, anterior view, males. A. Abia americana (Cresson, 1880) (NMNH). B. Abia berezowskii Semenov, 1896 (NRMS). C. Abia fulgens Zaddach, 1863 (NHMD). D. Abia melanocera Cameron, 1899 (NRMS). E. Abia sachalinensis Takeuchi, 1931 (NSMT). F. Abia sericea (Linné, 1767) (NSMT). Red arrows = antennomeres in club.
Fig. 8 in Review of the genus classification of Abiinae (Cimbicidae, Hymenoptera)
Fig. 8. Topology of Abiinae after IW analysis with k = 7. Other subfamilies of Cimbicidae collapsed, outgroup not shown. Allabia infernalis Semenov, 1896 marked with red; taxa placed in Orientabia Malaise, 1934 according to Taeger et al. (2010) marked with green. Abia fasciata (Linnaeus, 1758) (type species of Zaraea) marked with blue, Abia sericea (Linné, 1767) (type species of Abia) marked with yellow. Selected characters are mapped on the tree, see Discussion for further explanation.
Fig. 2. Habitus dorsal, males. A. Abia lewisii Cameron, 1887 in Review of the genus classification of Abiinae (Cimbicidae, Hymenoptera)
Fig. 2. Habitus dorsal, males. A. Abia lewisii Cameron, 1887 (NSMT). B. Abia metallica Mocsáry, 1909 (NSMT). C. Abia nitens (Linnaeus, 1758) (NHMD). D. Abia sericea (Linné, 1767) (NHMD). E. Abia triangularis (Takeuchi, 1931) (NSMT). Yellow arrows = depressions with hairy patches on abdominal terga.
Raw images used for colony classification
<p>Raw images of plates with yeast colonies used in Figure 2 of "Carl et al. A fully automated deep learning pipeline for high-throughput colony segmentation and classification" (Biology Open 2020 : bio.052936 doi: 10.1242/bio.052936 Published 2 June 2020). The paper describes the development of a novel computational pipeline for colony segmentation and classification that achieves accuracy comparable to human performance.</p> <p>The actual data was generated in a project that was published earlier (Duempelmann, L. e<em>t al. </em>Inheritance of a Phenotypically Neutral Epimutation Evokes Gene Silencing in Later Generations. <em>Molecular Cell</em> <strong>74, 3</strong> (2019).) The experiments were testing trans-generational inheritance of <em>ade6<sup>+</sup> </em>silencing in <em>Schizosaccharomyces pombe</em>. <em>ade6<sup>+</sup></em> silencing was first induced by expression of small interfering RNAs (siRNAs) that are complementary to the <em>ade6<sup>+</sup></em> gene in a <em>paf1-Q264Stop</em> nonsense-mutant background, leading to red colonies. Paf1 is a subunit of the Paf1 complex (Paf1C), which represses siRNA-induced heterochromatin formation in <em>S. pombe</em>.</p>
Fig 21. Primary types. A–F in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig 21. Primary types. A–F. Lectotypes (dorsal, labels). A–B. Ulomenes hypocrita Blanchard, 1850. C–D. Gastrohoplus mirabilis Moser, 1921. E–F. Schizochelus flavescens Blanchard, 1850. G–K. Syntype of Hercitis pygmaea Burmeister, 1855 (by Holger Dombrow). G. Dorsal. H. Lateral. I. Frontal. J. Posterior. K. Labels. Scale bars: A, C, E = 2 mm; G–J without scale (specimen about 4–4.2 mm according to original description).
Fig 19 in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig 19. Lectotypes (dorsal, labels). A–B. Mallotarsus spadiceus Blanchard, 1850. C–D. Manodactylus gaujoni Moser, 1919. E–F. Manopus biguttatus Conte de Castelnau, 1840. G–H. Oedichira pachydactyla Burmeister, 1855. I–J. Amphicrania ursina Burmeister, 1855. K–L. Pectinosoma elongata Arrow, 1913. M–N. Aulanota sulcipennis Moser, 1924. O–P. Melolontha rufipennis Fabricius, 1801. Scale bars = 2 mm.
Fig 20 in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig 20. Lectotypes (dorsal, labels). A–B. Pachycerus castaneipennis Guérin-Méneville, 1831. C–D. Anomalochilus singularis Blanchard, 1850. E–F. Demodema fallax Blanchard, 1850. G–H. Plectris tomentosa LePeletier de Saint-Fargeau & Audinet-Serville, 1828. I–J. Gama grandicornis Blanchard, 1850. K–L. Pachylotoma viridis Blanchard, 1850. M–N. Serica marmorea Guérin-Méneville, 1831. O–P. Rhinaspoides aeneofusca Moser, 1919. Scale bars = 2 mm.
Fig 17 in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig 17. Lectotypes (dorsal, labels). A–B. Agaocnemis pruina Moser, 1918. C–D. Corminus canescens Burmeister, 1855. E–F. Anomalonyx uruguayensis Moser, 1921. G–H. Barybas nana Blanchard, 1850. I–J. Ctilocephala pelluscens Burmeister, 1855. K–L. Pseudohercitis viridiaenea Moser, 1921. M–N. Barybas volvulus Burmeister, 1855. O–P. Calodactylus tibialis Blanchard, 1850. Scale bars = 2 mm.
Fig. 14. A, D in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig. 14. A, D. Male habitus, lateral (without some appendages). B–C, F–G. Female abdomen detail (lateral, posterior). E. Male abdomen detail, ventral. H–I. Aedeagus (lateral, parameres apex). A–C. Ancistrosoma klugii Curtis, 1835. D–I. Pectinosoma elongata Arrow, 1913.
Fig. 16. Schizochelus Blanchard, 1850. A–C. Male abdomen, lateroventral. D. Female abdomen, lateral. E–F in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig. 16. Schizochelus Blanchard, 1850. A–C. Male abdomen, lateroventral. D. Female abdomen, lateral. E–F. Aedeagus (lateral, parameres apex). G–L. Protibia−tarsus (male, female) (with detail of tarsus: I = dorsal view; K = ventral view). A, E−H. Schizochelus flavescens Blanchard, 1850. B, I–J. Schizochelus bicoloripes Blanchard, 1850. C–D, K–L. Schizochelus mirabilis (Moser, 1921) comb. nov. Scale bars = 1 mm.
Fig 18 in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig 18. Lectotypes (dorsal, labels). A–B. Ceraspis pruinosa LePeletier de Saint-Fargeau & Audinet- Serville, 1828. C–D. Ceratolontha venezuelae Arrow, 1948. E–F. Chariodactylus chacoensis Moser, 1919. G–H. Philochlaenia virescens Blanchard, 1842. I–J. Clavipalpus dejeani Laporte, 1832. K–L. Ctenotis obesa Burmeister, 1855. M–N. Euryaspis gaudichaudii Blanchard, 1850. O–P. Faula cornuta Blanchard, 1850. Scale bars = 2 mm.
Fig. 11 in Macrodactylini (Coleoptera, Scarabaeidae, Melolonthinae): primary types of type species and taxonomic changes to the generic classification
Fig. 11. ♂♂. A−C. Head–prothorax dorsal, tarsus rotated laterally to apex. D−G. Aedeagus (lateral, parameres apex). H. Head−prothorax, ventral. A, D−E. Chariodactylus chacoensis Moser, 1919. B, F−H. Manodactylus gaujoni Moser, 1919. C. Macrodactylus pumilio Burmeister, 1855. Scale bars = 1 mm.
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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.