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6,250 results for “Classification”
Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>
Figure 1 in How not to conduct a scientific debate: a counterpoint to the recent critique of the "pragmatic classification" of jumping spiders (Arthropoda: Arachnida: Araneae: Salticidae)
Figure 1. Neighbor-joining tree of selected barcode sequences of Clubiona species from the BOLD database. The Holarctic core Clubiona s. str. is indicated in green, the genevensis group in red. The key features, as discussed in the text, are also found in maximum parsimony and maximum likelihood trees of the same sequences.
Figure 4 in A new enigmatic lineage of Dascillidae (Coleoptera: Elateriformia) from Eocene Baltic amber described using X-ray microtomography, with notes on Karumiinae morphology and classification
Figure 4. Baltodascillus serraticornis gen. et sp. nov., holotype, X-ray micro-CT renderings: (a) head, frontal view; (b) head and thorax, fronto-ventral view; (c) pro- and mesothorax, ventral view; (d) abdomen (ventrites 2–5), lateral view; (e) abdomen (ventrites 2–5), ventral view; (f) tarsus, ventral view; (g) tarsus, dorsal view. Scale bars = 1.0 mm.
Figure 3 in A new enigmatic lineage of Dascillidae (Coleoptera: Elateriformia) from Eocene Baltic amber described using X-ray microtomography, with notes on Karumiinae morphology and classification
Figure 3. Baltodascillus serraticornis gen. et sp. nov., holotype, Xray micro-CT renderings: (a) head and thorax, dorsal view; (b) head and thorax, ventral view; (c) head and thorax, right lateral view; (d) head and thorax, left lateral view. Scale bars = 1.0 mm.
Figure 2 in A new enigmatic lineage of Dascillidae (Coleoptera: Elateriformia) from Eocene Baltic amber described using X-ray microtomography, with notes on Karumiinae morphology and classification
Figure 2. Baltodascillus serraticornis gen. et sp. nov., holotype: (a) habitus, lateral view; (b) pronotum and scutellar shield, dorsal view; (c) head and pronotum, dorsal view; (d) head and thorax, ventral view; (e) tarsus, ventro-lateral view; (f) tarsus, ventral view. Scale bars = 1.0 mm (a–d), 0.3 mm (e, f).
Figure 1 in A new enigmatic lineage of Dascillidae (Coleoptera: Elateriformia) from Eocene Baltic amber described using X-ray microtomography, with notes on Karumiinae morphology and classification
Figure 1. Baltodascillus serraticornis gen. et sp. nov., holotype: (a) habitus, dorso-frontal view; (b) habitus, ventro-lateral view. Scale bar = 2.0 mm.
Figure 5 in A new enigmatic lineage of Dascillidae (Coleoptera: Elateriformia) from Eocene Baltic amber described using X-ray microtomography, with notes on Karumiinae morphology and classification
Figure 5. Pleolobus fuscescens Philippi et Philippi, 1864, female from Chile: (a) habitus, dorsal view; (b) head, lateral view; (c) head, pronotum and scutellar shield, dorsal view. Scale bars = 4.0 mm (a), 1.0 mm (b, c). All images by Jorge Jensen.
Interactive map of Heat Stress Compensability Classification (HSCC) application in 96 United States cities.
<p>This repository includes the interactive map in format .html of the very first application of the <strong>Heat Stress Compensability Classification (HSCC) in 96 cities in the United States </strong>showing the proportion of days with compensable and uncompensable heat stress from the top 10th percentile of hottest days from 2005-2020 in each place.</p> <p>This map offers the detailed results of the very first application of the classification system as in the journal article: <strong>The Development of an Adaptive Heat Stress Compensability Classification Applied to the United States</strong>, published in the 4th SNP special issue in the International Journal of Biometeorology. The results of this visualization were obtained from open-source data and coding packages such as Folium, and the model results were obtained by applying the Python Human Heat Balance (PyHHB) on weather dataset freely available.</p> <p>The interactive map offers a detailed visualization of the results from each of the cities, allowing you to see 3 tabs when the icon of the pie chart from each location is clicked.</p> <p><strong>Tab statistics:</strong> Detail per city of Figure 4b of related paper.</p> <p><strong>Tab Histogram 2D: </strong>Details per city of Fig 6 of related paper</p> <p><strong>Tab How to read: </strong>Figure 2 in related paper.</p> <p>Please for questions related to this dataset/code contact Gisel Guzman-Echavarria (gguzma20@asu.edu).</p> <p>Guzman-Echavarria, G., & Vanos, J. (2023). PyHHB: Physiological-based estimations of human survivability and liveability to heat in a changing climate (Nature Communications (1.0.0)). Zenodo. https://doi.org/10.5281/zenodo.10020137</p> <p> </p> <p> </p>
calculated 3D molecular descriptors for the dopant-free HTMs without PSCs classification
<p><strong><span>The calculated 3D molecular descriptors for the dopant-free HTMs (Without Classification of PSCs) along with their photovoltaic properties.</span></strong></p> <p><strong><span><span>The m</span><span>olecular descriptors for all molecules were calculated using the academically free PADEL online tool (http://www.scbdd.com/padel_desc/index/).</span></span></strong></p>
Calculated 1D, and 2D molecular descriptors for the dopant-free HTMs without PSCs classification
<p><strong><span>The calculated 1D, and 2D descriptors for the dopant-free HTMs (Without Classification of PSCs) as well as their photovoltaic properties. </span></strong></p> <p><strong><span>All 1D, and 2D descriptors were calculated using the </span></strong><strong><span><span>academically free PADEL online tool (http://www.scbdd.com/padel_desc/index/)</span></span></strong></p>
IMMUcan panel 1 cell type classification
<p>This repo contains Imaging Mass Cytometry data from 179 cancer patients collected during the IHI2 funded IMMUcan project.</p> <p> </p> <p>Background:</p> <p>IMMUcan (https://immucan.eu/) collects samples from cancer patients for multimodal analysis. Amongst those Imaging Mass Cytometry (IMC). Samples collection occurs over 6 years. For reproducible cell typing over time, a cell phenotype classifier was built from manually annotated data. The classifier was trained on a first batch of annotated images (V1) and then trained again with a second round of annotated images (V3). A final classifier was build which is used within IMMUcan to classify cell phenotypes for IMC panel 1 for all cancer patients.</p> <p> </p> <p>The dataset contains:</p> <ul> <li>sce_labelled_V1.rds_ is the `SingleCellExperiment` object containing all labelled cells from the first batch of labelling</li> <li>sce_labelled_V3.rds_ is the `SingleCellExperiment` object containing all labelled cells from the second batch of labelling</li> <li>rf_images_DCfix.rds_ is the RF model which can be used for cell type predictions</li> <li>sce_raw.rds_ is the `SingleCellExperiment` object containing all cells from the 179 images. the trained classifier has been applied to this dataset which can be used to view the classification results of cell phenotypes on images using the bioconductor packages cytomapper or cytoviewer. </li> <li>all_images.rds a `CytoImageList` object which contains all the images from all images</li> <li>all_masks.rds a `CytoImageList` object which contains all single-cell segmentation masks from all images</li> <li>a zip archive `images_masks` containing the tiff images and masks from all 179 images, a CytoImageList Object for images (all_images.rds) and a CytoListImage object for all masks (all_masks.rds)</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Figures 3–5 in A new bee genus from the pampas of eastern Argentina, with appended notes on the classification of "paracolletines" (Hymenoptera: Colletidae)
Figures 3–5. Details of female of Aenarete roigi, new genus and species. 3. Facial view. 4. Pygidial plate. 5. Mesoscutellum, metanotum, and basal area of propodeum.
Fig. 10 in First record of a gynandromorph of Osmia submicans MORAWITZ, 1870 (Hymenoptera, Megachilidae) - characterisation by morphological and morphometric parameters and critical note on gynander classification
Fig. 10: PCA of 34 morphological parameters in females, males and the left and right side of the gynander (variance-covariance matrix). Pink dot = females; bluish square = males, cross = gynander left, circle = gynander right. Eigenvalue axis 1 = 0.6535, % variance = 84.67; eigenvalue axis 2 = 0.0580, % variance = 7.516.
Fig. 6 in First record of a gynandromorph of Osmia submicans MORAWITZ, 1870 (Hymenoptera, Megachilidae) - characterisation by morphological and morphometric parameters and critical note on gynander classification
Fig. 6: On the left side, the sternites with male characteristics are visible. They show the greenish ore-coloured integument which is also typical for males. The large S2 is characteristic for males. On the right side the smaller female sternites are formed, bearing the black metasomal scopa; photo: L. Haitzinger.
Fig. 5 in First record of a gynandromorph of Osmia submicans MORAWITZ, 1870 (Hymenoptera, Megachilidae) - characterisation by morphological and morphometric parameters and critical note on gynander classification
Fig. 5: The metasoma has a greenish-bronze to ore-coloured integument on the left dorsal side and a golden yellow pubescence on the tergite ends of T1-T3 and T6 (male characteristics). On the right side, it has a black (dark bluish) integument and a white pubescence (female characteristics). In T4, the left side has purely male characteristics, while the right side is female on one side and male on the other. T5 has male characteristics throughout; photo: L. Haitzinger.
Fig. 2 in First record of a gynandromorph of Osmia submicans MORAWITZ, 1870 (Hymenoptera, Megachilidae) - characterisation by morphological and morphometric parameters and critical note on gynander classification
Fig. 2: Ranges of measurements of various morphometric parameters (abbreviations, character names, definitions; see Table 2).
Fig. 3 in First record of a gynandromorph of Osmia submicans MORAWITZ, 1870 (Hymenoptera, Megachilidae) - characterisation by morphological and morphometric parameters and critical note on gynander classification
Fig. 3: Areas of the gynander with female (pink) and male (blue-violet) morphological features, schematised (left = dorsal side; right = ventral side). The results for femura, tibia, metatarsi and tarsi 5 were assigned on the basis of the morphometric results. In ventral view, the left side corresponds to the right half of the body, the right side to the left half of the body. No differences could be detected in areas marked in grey.
Fig. 1 in First record of a gynandromorph of Osmia submicans MORAWITZ, 1870 (Hymenoptera, Megachilidae) - characterisation by morphological and morphometric parameters and critical note on gynander classification
Fig. 1: Osmia submicans male, visiting flowers of Hippocrepis comosa (Presq'île de Giens, Hyères, France, 18.03.2008; same site where the gynander was detected); photo: A. Schwabe.
Flood Classification Dataset from the River Wupper in Germany
<h1>Flood Classification Dataset from the River Wupper in Germany</h1> <p>This dataset contains sensor measurements of water levels and rainfall amounts around the river <em>Wupper</em> region and the city of <em>Wuppertal</em> in North Rhine-Westphalia Germany. The dataset shows data over 19 years and from 10 different sensors. The data is sampled to one data point every 30 minutes.</p> <p>The water level measurements are given in [cm] and show 2 different measuring locations: The first is a river water level in the inner city of <em>Wuppertal</em>, called <em>Kluserbrücke</em> (Abbrev.: WL KLU), the second is the water level of the biggest dam, positioned upstream to WL KLU, called <em>Krebsöge </em>(Abbrev.:WL KRE). If the water levels exceeds 125 cm at WL KLU, local authorities would issue a warning as the water level is dangerously high from then on.</p> <p>The rain measurements are given in [mm] and as there is one datapoint given for every 30 minutes, indicates the amount of rain in millimeters fallen in the last 30 minutes. All abbreviations for precipitation sensors carry the prefix <em>Prec</em> and are followed by a name indicating the specific sensor location.</p> <p> </p> <h2>File Structure</h2> <p>The data is split up into train/validation/test and a cross validation datasets. The File is named in this manner:</p> <ul> <li>HWS-<strong>{d}</strong>-<strong>{k}</strong>_multi_label.csv <p>Where:</p> <ul> <li><strong>{d}</strong> indicates the dataset purpose: {TRAIN, VAL, TEST}</li> <li><strong>{k}</strong> indicates the cross-validation fold.</li> </ul> </li> </ul> <h2><br>Source</h2> <p>This dataset is based on data from the Wupperverband (<a title="Wupperverband" href="https://www.wupperverband.de/startseite" target="_blank" rel="noopener">https://www.wupperverband.de/startseite</a>) and the open data platform of the Deutscher Wetterdienst (DWD) (<a title="Opendata DWD" href="https://opendata.dwd.de/" target="_blank" rel="noopener">https://opendata.dwd.de/</a>).</p>
Fig. 3 in Phylogenetic analysis of the tribe Neanurini questions tribal classification of the subfamily Neanurinae (Collembola: Neanuridae)
Fig. 3 Unambiguous morphological character optimisation obtained from an analysis of the data (Appendix 2) under implied weights (k = 17). The numbers above and below circles on the branches show character numbers and states, respectively. White and black circles represent homoplasious and non-homoplasious character state transformations, respectively
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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)
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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.