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2,139 results for “recognition”
Takeout gene expression is associated with temporal kin recognition
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Sexual selection and species recognition promote complex male courtship displays in ungulates
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Data for publication: Recognition of non-CpG repeats in Alu and ribosomal RNAs by the Z-RNA binding domain of ADAR1 induces A-Z junctions
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Data from: Frogs with a southern drawl: Wide recognition space facilitates heterospecific aggression in territorial cricket frogs
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Data from: Species recognition limits mating between hybridizing ant species
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Data from: Genomic and ecological divergence support recognition of a new species of endangered Satyrium butterfly (Lepidoptera, Lycaenidae)
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Geographic differences in individual recognition linked with social but not nonsocial cognition
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Data from: Highlighting potential physical and chemical cues involved in conspecific recognition system in a predator nematode, Seinura caverna
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Data from: Hybridization, reinforcement selection and sex-dependent reproductive character displacement of sperm and egg recognition proteins
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Data from: Molecular phylogenetics of Distephanus supports the recognition of a new tribe, Distephaneae (Asteraceae)
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Morphological, molecular, and biogeographic evidence for specific recognition of Euthamia hirtipes and Euthamia scabra (Asteraceae, Astereae)
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Confocal microscopy images for: Surface remodeling and inversion of cell-matrix interactions underlie community recognition and dispersal in Vibrio cholerae biofilms
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A lectin receptor-like kinase controls self-pollen recognition in Phlox
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Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents
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The effect of diet on colony recognition and cuticular hydrocarbon profiles of the invasive Argentine ant, Linepithema humile
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Figure 3 in Antennae and the role of olfaction and contact stimulation in mate recognition by males of the pollinating fig wasp Ceratosolen gravelyi (Hymenoptera: Agaonidae)
Figure 3. Micrographs of the antennae and chemosensory sensilla of male Ceratosolen gravelyi. (a) Scanning electron micrograph of the head and antennae, showing an exposed area of the 2nd–3rd flagellomeres (F2–F3). (b) Scanning electron micrographs of an excised antenna, showing the scape (Sc), pedicel (Pe) and flagellum (F). (c) High magnification image of the terminal flagellomere (the 3rd flagellomere, F3) showing the three types of chemoreception sensilla: multiporous plate sensilla (MPS) and basiconic sensilla types 1 (BS-1) and 2 (BS-2). Note the terminal indentation (De) in the BS on the insets in the lower left corner. (d) Longitudinal section of a multiporous plate sensillum showing dendritic branches (DB) running parallel to the sensillar lymph (SL) and ending with cuticular pores (Po) at the sensillum surface. (e) Longitudinal section of the basal area of a basiconic sensilla type 1 inserted into a socket surrounded by a raised cuticular ring (CR). (f) Longitudinal section of the basal area of a basiconic sensilla type 2. (g–i) Cross-sections of a multiporous plate sensillum and basiconic sensilla types 1 and 2. The sensillar wall (SW) of BS-1 and BS-2 is non-porous at this level.
Wrist Vascular Biometric Recognition Using a Portable Contactless System - Video
<p>Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR<sup>®</sup>. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR<sup>®</sup>. The results obtained by combining these three elements, TGS-CVBR<sup>®</sup>, PIS-CVBR<sup>®</sup>, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.</p>
Audiovisual Aerial Scene Recognition Dataset
<p>This dataset provides 5075 paired images and sound clips categorized to 13 scenes, for exploring the aerial scene recognition task. For more details, please refer to <a href="http://arxiv.org/abs/2005.08449">our paper</a> and <a href="https://github.com/DTaoo/Multimodal-Aerial-Scene-Recognition">code</a>.</p>
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (>2M images) traffic sign recognition dataset (<a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the <a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a> dataset were cropped from the <a href="https://github.com/olivesgatech/CURE-TSD">CURE-TSD</a> dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Real-world images were obtained from the BelgiumTS video sequences and simulated images were generated with the Unreal Engine 4 game development tool. Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. Unreal and real sequences were processed with state-of-the-art visual effect software Adobe(c) After Effects to simulate challenging conditions, which include rain, snow, haze, shadow, darkness, brightness, blurriness, dirtiness, colorlessness, sensor and codec errors. Please refer to our <a href="https://github.com/olivesgatech/CURE-TSR">GitHub page</a> for code, papers, and more information.</p> <p>Instructions: </p> <p>The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp"</p> <ul> <li> <p>sequenceType: 01 - Real data 02 - Unreal data</p> </li> <li> <p>signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking</p> </li> <li> <p>challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze</p> </li> <li> <p>challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.</p> </li> <li> <p>Index: A number shows different instances of traffic signs in the same conditions.</p> </li> </ul>
An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes
<p>This is a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside-outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14\%. Due to the lack of research efforts in the literature dedicated for studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing the researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.</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.