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152 results for “Biometrics”
Lake Mendota, Wisconsin, USA, Zebra Mussel Body Size and Biomass Biometrics 2018
We sampled 98 individuals of the zebra mussel (Dreissena polymorpha) population of Lake Mendota from many littoral zone sites in 2018 to create biometric relationships between several metrics of body size and several metrics of biomass, including length, width, height, living weight, wet weight, dry weight, shell weight, shell-free dry weight, and ash-free dry weight. We selected individuals to span a wide range of body sizes and found strong relationships between most combinations of body size and biomass metrics.
US_UMB and US_UMd Ameriflux towers biometric plot data at the University of Michigan Biological Station, Pellston, MI (1997 to 2024)
These are the annual leaf litterfall carbon fluxes and average soil respiration measurements for the two flux towers (reference, aka 'AmeriFlux' and treatment, aka 'FASET') at UMBS.
Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop
<p>This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function <span class="math-tex">\(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\)</span>, where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW). </p>
SignEEG v1.0 : Multimodal Electroencephalography and Signature Database for Biometric Systems
<p>Noninvasive electroencephalography (EEG) is a method for measuring electrical brain activity from the surface of the scalp. Recent developments in artificial intelligence accelerate the automatic recognition of brain patterns, allowing more reliable and increasingly faster Brain-Computer interfaces, including biometric applications. Biometric research is also focusing on multimodal systems using EEG along with other modalities. This paper presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms with increasing brain functioning: (i) visualizing a signature image, (ii) doing a signature in mind, and (iii) physically drawing a signature. Extensive experiments have been done in order to provide a solid baseline with machine learning classifiers. We release the raw, pre-processed data and easy-to-follow implementation details.</p>
Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach
<p>Notebook and data to accompany the (unpublished) paper titled "Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach". This work expands a previous study, relating particulate matter concentrations and short-term biometric features across multiple participants. </p><p>Github link: https://github.com/mi3nts/DUEDARE_multiple_participants</p>
Figure 1 in Aeshna affinis Vander Linden, 1820 (Odonata: Aeshnidae) in the Iberian Peninsula: A review of past and recent records, and a larval biometric study
Figure 1. Body length (A), prementum (B), supracoxal armature of prothorax (C), lateral spines on segments 6 to 9 of the abdomen and ovipositor (D), extremity of abdomen: cerci, paraprocts, and epiproct (E). / Longitud corporal (A), prementón (B), armadura supracoxal del protórax (c), espinas laterales de los segmentos abdominals 6 a 9 y ovipositor (D), extremo final del abdomen: cercos, paraproctos y epiprocto (E).
Dataset for train and test BRITTANY (Biometric RecognITion Through gAit aNalYsis)
<p>This dataset can be used train and test the BRITTANY tool. Information contained in the dataset is especially suitable to be used as train and test data for neural network-based classifiers.</p> <p>This dataset contains 198 Rosbag files, of 5 seconds duration, recorded in different locations (kitchen, livingroom-window and livingroom-door) with Orbi-One robot stood still. Two sorts of Rosbag files have been recorded. In 90 Rosbag files (train*.bag), data recorded correspond to a person walking in a straight line in front of the robot. Data from five different people have been recorded. For each location and person, six Rosbag files have been recorded.</p> <p>In 108 Rosbag files (test*.bag), data recorded correspond to a person walking in a straight line in front of the robot. Data from six different people have been recorded. Five of those six people are the same as in the other rosbags and the other one is not registered in the system to evaluate the false-positive cases in the system.</p>
Figure 2 in FlorAl biometrics And phenologicAl chArActeriZAtion of flowering And fruiting of the passion fruit PAssiflorA TrinTAE in southwestern BAhiA, BrAZil
Figure 2. Illustration of Passiflora trintae showing the floral parts. A. Sepal and petal; B. Lateral view of flower. C. Apical view of flower. Bar = 1 cm.
Figure 4 in FlorAl biometrics And phenologicAl chArActeriZAtion of flowering And fruiting of the passion fruit PAssiflorA TrinTAE in southwestern BAhiA, BrAZil
Figure 4. Average, maximum, and minimum temperature and precipitation from June 2012 to May 2013, in Vitória da Conquista, Bahia, Brazil (INMET 2013). The bars represent precipitation data, and the lines represent temperature data.
Figure 3 in Histological biomarkers and biometric data on trahira Hoplias malabaricus (Pisces, Characiformes, Erythrinidae): a bioindicator species in the Mearim river, Brazilian Amazon
Figure 3. Values of Bernet et al. (1999) index and Poleksic and Mitrovic-Tutundzic (1994) (HAI), in the dry and rainy seasons.
Figure 2 in Histological biomarkers and biometric data on trahira Hoplias malabaricus (Pisces, Characiformes, Erythrinidae): a bioindicator species in the Mearim river, Brazilian Amazon
Figure 2. Histological lesions in H. malabaricus. (A) Normal gill tissue; (B) aneurysm (arrow); (C) epithelial displacement (arrow); and (D) congestion (arrow).
Figure 1 in Histological biomarkers and biometric data on trahira Hoplias malabaricus (Pisces, Characiformes, Erythrinidae): a bioindicator species in the Mearim river, Brazilian Amazon
Figure 1. Location of the Mearim River stretches in the Baixada Maranhense Environmental Protection Area: Engenho Grande village (A1) and Curral da Igreja village (A2).
Behavioral Biometrics Dataset towards Continuous Implicit Authentication
<p>The provided dataset aims at creating a ground truth upon which continuous implicit authentication models can be built. For this purpose, it contains gestures and sensors data collected from more than 2,000 users playing the BrainRun game (http://brainrun.issel.ee.auth.gr/) available at both Google Play Store and Apple App Store.</p>
Figure 7 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 7. Dendrogram produced by UPGMA clustering based on means of the 7 morphometric characters from 5 species.
Figure 6. PCA scatter plot for the 2 canonical variates generated from the 7 morphometric characters from 5 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 6. PCA scatter plot for the 2 canonical variates generated from the 7 morphometric characters from 5 species.
Figure 4 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 4. Dorsal, lateral, and ventral views, respectively, of the variable baculum types of N. labaumei.
Figure 3 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 3. Dorsal, lateral, and ventral views, respectively, of the different types of bacula of N. nehringi.
Figure 1 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 1. Map showing the geographic origin of the investigated samples from Turkey. The numbers correspond to the localities given in Table 1.
Figure 2 in Biometric variation in Martes foina from mainland Greece and the Aegean Islands
Figure 2. Skull measurements of stone marten: 1. skull length (S-L), 2. facial length (F-L), 3. upper neurocranium length (UpNrc-L), 4. condylobasal length (Cb-L), 5. palatal length (Pa-L), 6. length of maxillary tooth row (MaxT-L); 7. length of the molar row (Mo-L); 8. mandible condyle length (MaCond-L), 9. length of carnassials tooth (CaT-L), 10. mandible coronoid process length (MaCorP-L), 11. width of rostrum (Ro-W), 12. zygomatic breadth (Zy-B), 13. interorbital breadth (InOrb-B), 14. postorbital breadth (PosOrb-B), 15. breadth of braincase (Brc-B), 16. distance between mastoid processes (MaP-Di), 17. cranium height measured from the auditory bulla (CrAuB-H), 18. angular process coronoid process distance (AnPCorP-Di).
Figure 3 in Biometric variation in Martes foina from mainland Greece and the Aegean Islands
Figure 3. Plot of the first two discriminant functions by using skull measurements for male stone martens from mainland Greece, the Aegean Islands, and Crete.
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.