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4,612 results for “labelled”
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Dataset
<p>This contains the dataset for the target label prediction subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the target_label_prediction folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the target label prediction track can be found <a href="https://zenodo.org/record/7217508">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Test Set
<p>This contains the test set for the target label prediction track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full target label prediction track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Supplementary material - Label-free multimodal nonlinear optical microscopy reveals hallmarks of bone composition in pathophysiological conditions
<p>The repository features Excel worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: </p> <ul> <li>Figure 4. SHG intensity values measured from WT and KO murine spines counterparts, both in parallel and orthogonal light polarization with respect to the craniocaudal axis of the vertebrae.</li> <li>Figure 5A. Quantization of ColIa1 gene expression level after mRNA extraction from WT and Dpp3 KO mice.</li> <li>Figure 5B. Absorbance values at 540 nm after Sirius Red staining of primary osteoblast isolated from WT and Dpp3 KO neonatal calvaria, used to assess and compare the in-vitro collagen production of the two murine models.</li> <li>Figure 7. SRS intensity ratios measured between bone and bone marrow regions of WT and Dpp3 KO models, both at the 2850 cm-1 Raman mode of CH2 stretching in lipids and at the 2920 cm-1 Raman mode of CH3 stretching in proteins.</li> <li>Figure 8. Data of gene expression analysis of selected genes relevant for lipid transport, uptake, and metabolism (i.e., CD 36, Fabp4, Lrp1, Fatp1, PGC1, CPT1, Pex7 and Glut1), in the flushed bone of WT and Dpp3 KO mice.</li> </ul>
FIGURES 1–9. Gnathodracon spp. 1–3 in The importance of correct labelling of types: an example in Tortricidae (Lepidoptera) and its rectification
FIGURES 1–9. Gnathodracon spp. 1–3: Gnathodracon lukimayumbe Razowski, 2012; 1.—Holotype ♂, 2.—Holotype labels, — Holotype labels, 3.—Holotype male genitalia; 4–9: Gnathodracon orbiculina Razowski, 2012; 4.—Holotype ♂, 5.—Holotype labels, 6.—Holotype male genitalia, 7.—Paratype ♀, 8.—Paratype labels, 9.—Paratype female genitalia.
FIGURES 331–334. Coniceromyia habitus and holotype label. 331. C in Taxonomic revision of Coniceromyia Borgmeier (Diptera: Phoridae), with the description of three new species from Brazil
FIGURES 331–334. Coniceromyia habitus and holotype label. 331. C. pilicoxa holotype; 332. C. vespertilio holotype; 333. C. vespertilio holotype labels; 334. C. vespertilio recently collected specimen.
Carolina Bay Object Detector Images and Labels
<p>Contained here (in CarolinaBayTrainingData.zip) are the images (jpegs) and labels (csv) that were used to train a bounding box object detector for Carolina Bays detailed in the following paper:</p> <p>Lundine, M., Trembanis, A., Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models, Remote Sensing, 2021, Volume 13(18), 3770, <a href="https://doi.org/10.3390/rs13183770">https://doi.org/10.3390/rs13183770</a>.</p> <p>Each image is a single channel jpeg. The channel corresponds to gridded LiDAR elevation values remapped to 256 values. Each image was normalized individually for maximum contrast.</p> <p>The labels csv contains the coordinates for each bounding box annotation of Carolina Bays in each available image. The columns are: filename, width, height, label, xmin, ymin, xmax, ymax, label_value. Each row is an annotation of a Carolina Bay. Filename corresponds to the image, width is the width in pixels of that image, height is the height in pixels of that image. xmin, ymin, xmax, ymax are the bounding box coordinates for the annotation. Label is 'bay' for each annotation, with a label_value of 1.</p> <p>Feel free to experiment with this dataset, add to it, and improve upon the results.</p> <p>Also contained here (in CarolinaBayDetections.zip) are the detection results (as unaggregated polygons, as aggregated polygons, as smooth polygons, and as points).</p>
Ten novels by Arthur Conan Doyle with subgenre labels and topics
<p>Ten novels by Arthur Conan Doyle with subgenre labels and topics. </p>
FLIM Data and analysis of the hands-on sessions "CF44-1 Label-free metabolic FLIM with 2 photon excitation"
<p>This FLIM dataset on HeLa cells and human astrocytes was acquired during the hands-on sessions <em>CF44-1 Label-free metabolic FLIM with 2 photon excitation</em> during the <a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">German BioImaging </a><a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">workshop on FLIM in Munich</a>.</p> <p>The FLIM data are analyzed with the open source software FLUTE available on GitHub: <a href="https://github.com/LaboratoryOpticsBiosciences/FLUTE"><strong><em>https://github.com/LaboratoryOpticsBiosciences/FLUTE</em></strong></a></p> <p>and published on Biological imaging Journal: <a href="https://www.cambridge.org/core/journals/biological-imaging/article/flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data/862F290EC14187741BDA6B58E9868FA2"><strong><em>Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211</em></strong></a></p>
Gender labels for Spanish affiliated researchers
<p>Three test sets compiled using the Ranking of researchers in Spain and Spaniards abroad: https://www.webometrics.info/en/GoogleScholar/Spain.</p> <p> </p>
Effectiveness of labels in digital art experience: psychophysiological and behavioral evidence
<p><span>The "Experimental condition" folder comprises data from 40 participants: folders from S01 to S34 include Empatica and Pupil Labs recordings, along with a MATLAB matrix; folders from S35 to S40 contain only the MATLAB matrix.</span></p> <p><span>The "Control condition" folder contains data from 20 participants: folders from S01 to S15 include Empatica and Pupil Labs recordings, along with a MATLAB matrix; folders from S16 to S20 only contain the MATLAB matrix.</span></p> <p><span> </span></p> <p><span>In the MATLAB matrix, each row corresponds to a trial. The columns indicate:</span></p> <p><span>1. </span><span>Number of painting</span></p> <p><span>2. </span><span>Start time of reading label</span></p> <p><span>3. </span><span>End time of reading label</span></p> <p><span>4. </span><span>Start time of viewing painting</span></p> <p><span>5. </span><span>End time of viewing painting</span></p> <p><span>6. </span><span>Response to the item questionnaire about <strong>liking</strong> (<em>“How much do you like the painting?”</em>) - 5-points Likert scale</span></p> <p><span>7. </span><span>Response to the item questionnaire about <strong>interest </strong>(<em>“How interested are you in the painting?”</em>) - 5-points Likert scale</span></p> <p><span>8. </span><span>Response to the item questionnaire about <strong>complexity</strong> (<em>“How complex do you find the painting?”</em>) - 5-points Likert scale</span></p> <p><span>9. </span><span>Response to the item questionnaire about <strong>comprehensibility</strong> (<em>“How comprehensible do you find the painting?”</em>) - 5-points Likert scale</span></p> <p><span>10.</span><span> Response to the item questionnaire about <strong>title informativeness</strong> (“<em>How informative do you find the title of the painting?"</em>) - 5-points Likert scale</span></p> <p><span>11.</span><span>Response to the item questionnaire about <strong>positive emotions</strong> (“<em>How much does this painting evoke positive emotions in you?”</em>) - 5-points Likert scale</span></p> <p><span>12.</span><span>Response to the item questionnaire about <strong>negative emotions</strong> (“<em>How much does this painting evoke negative emotions in you?”</em>) - 5-points Likert scale</span></p> <p><span>13.</span><span>Response to the item questionnaire about <strong>curiosity for other works</strong> (<em>“How curious are you about seeing works by the same author?”</em>) - 5-points Likert scale</span></p> <p><span> </span></p> <p><span>The “Empatica” folder contains several <em>”.csv” </em>files extracted from the Empatica wristband. In our work, we used:</span></p> <p><span>- </span><span>EDA - data from the electrodermal activity sensor expressed in microsiemens (μS). The sample rate is 4 Hz.</span></p> <p><span>- </span><span>HR - Average heart rate extracted from the BVP signal. The first row is the initial time of the session expressed as unix timestamp in UTC. The second row is the sample rate expressed in Hz.</span></p> <p><span>The “Pupil” folder contains several “<em>.csv” </em>files obtained from the Pupil Core Eye Tracker. These files have been exported using the Pupil Player Software. In our work, we used the exported file “pupil_positions.csv”, that includes:</span></p> <p><span>- </span><span>timestamp - timestamp of the source image frame</span></p> <p><span>- </span><span>index – associated frame: closest world video frame</span></p> <p><span>- </span><span>id - 0 or 1 for right and left eye (from the wearer's point of view)</span></p> <p><span>- </span><span>confidence - is an assessment by the pupil detector on how sure we can be on this measurement. A value of `0` indicates no confidence. `1` indicates perfect confidence.</span></p> <p><span>- </span><span>norm_pos_x - x position in the eye image frame in normalized coordinates</span></p> <p><span>- </span><span>norm_pos_y - y position in the eye image frame in normalized coordinates</span></p> <p><span>- </span><span>diameter - diameter of the pupil in image pixels as observed in the eye image frame (is not corrected for perspective)</span></p> <p><span>- </span><span>method - string that indicates what detector was used to detect the pupil (2d the pupil appears as an ellipse available in `3d c++` and `2D c++` detector)</span></p> <p><span>- </span><span>ellipse_center_x - x center of the pupil in image pixels</span></p> <p><span>- </span><span>ellipse_center_y - y center of the pupil in image pixels</span></p> <p><span>- </span><span>ellipse_axis_a - first axis of the pupil ellipse in pixels</span></p> <p><span>- </span><span>ellipse_axis_b - second axis of the pupil ellipse in pixels</span></p> <p><span>- </span><span>ellipse_angle - angle of the ellipse in degree.</span></p> <p><span> </span></p> <p><span>The gaze data, as described in the Data processing section of our work, were extracted from the video recordings of each session, which were not uploaded here for privacy reasons.</span></p>
AlleNoise - large-scale text classification benchmark dataset with real-world label noise
<div> <div> <div> <div> <p><span>AlleNoise</span><span> is a benchmark dataset for large-scale multi-class text classification with real-world label noise. It consists of e-commerce product titles from Allegro.com with corresponding category labels. The noise distribution comes from actual users of a major e-commerce marketplace, so it realistically reflects the semantics of human mistakes. In addition to the noisy labels, we provide human-verified clean labels and a meaningful, hierarchical taxonomy of categories. Code and data is available at https://github.com/allegro/AlleNoise.<br></span></p> </div> </div> </div> </div>
Images of roundabouts and junctions classified and labeled using the Labelme tool.
<p>The dataset consists of 2000 PNG images (512 x 512 pixels) of roundabouts and junctions, with different dimensions and orientations. This dataset has been created to train a convolutional neural network for object detection (roundabouts and junctions) in various areas of the Community of Madrid.</p> <p>This series of images has been obtained from the National Plan of Aerial Orthophotography (PNOA), and they will be classified and identified using the Labelme tool. Therefore, in addition to the .png format images, there are also .json files that contain the labels that have been created and identified in each of the downloaded images.</p> <p>These data are classified into 7 types of classes: normal roundabouts (rotondaN), roundabouts split into junctions (rotondaEn), T-junctions without island (enlaceTsi), T-junctions with island (enlaceTci), U-turn junctions (enlaceTA), crossings, and lane change junctions (enlaceBI2).</p> <p>The procedure followed is as follows:</p> <p>1. Using the QGIS tool, a geopackage file was created to mark the different areas where an image was to be downloaded.<br>2. Using a Python script, requests are made to the PNOA to download images.<br>3. The various roundabouts and junctions obtained are labeled and classified.<br>4. If more variability or data is needed, the process is repeated.</p> <p>The shared dataset consists of a compressed file with the images and labels used for the completion of a final degree project.</p> <p> </p>
Examined specimen data from collection labels in Megachile (Austrochile) taxonomic revision
<p>The species in the subgenus <em>Austrochile </em>Michener, 1965<em> </em>(genus <em>Megachile</em>) construct brood cells from resin that are stuck individually to twigs or in clusters on the bark of trees. We therefore propose 'resin pot bees' as common name for the bees in this subgenus. The species are revised and redescribed, and 78 additional species are described. The following names were synonymized: <em>Chalicodoma</em> (<em>Austrochile</em>) <em>subferox</em> Meade-Waldo, 1915 syn. nov. = <em>Megachile</em> (<em>Austrochile</em>) <em>kirbyana</em> Cockerell, 1906 comb. nov.; <em>Chalicodoma</em> (<em>Austrochile</em>) <em>recisa Cockerell, </em>1913 syn. nov., <em>Chalicodoma</em> (<em>Austrochile</em>) <em>simpliciformis</em> Cockerell, 1918 syn. nov., <em>Chalicodoma</em> (<em>Austrochile</em>) <em>kirbiella</em> Rayment, 1953 syn. nov. = <em>Megachile</em> (<em>Austrochile</em>) <em>modesta</em> Smith, 1892 comb. nov.; <em>Chalicodoma</em> (<em>Austrochile</em>) <em>subremotula</em> Rayment, 1934 syn. nov., <em>Chalicodoma</em> (<em>Austrochile</em>) <em>portlandiana</em> Rayment, 1953 syn. nov. = <em>Megachile</em> (<em>Austrochile</em>) <em>rottnestensis</em> Rayment, 1934 comb. nov.; <em>Chalicodoma</em> (<em>Austrochile</em>) <em>sexmaculata</em> Smith, 1868 syn. nov. = <em>Megachile</em> (<em>Rozenapis</em>) <em>ignita</em> Smith, 1853.</p> <p>All descriptions are accompanied by high resolution diagnostic images and distribution maps. Data on flower visitation and phenology are given. Dichotomous keys to both sexes of the species are provided and interactive keys to the species will be available on the LUCID website.</p>
Promise+: A dataset of labeled software requirements
<p>This dataset is an expansion of the Promise_exp dataset, containing software requirements classified by experts</p>
Event Abstraction on Partially Ordered Event Data using Label Propagation
Open the record for dataset details and reuse information.
FIGURE 1. ORIgINAL LAbELS. A. Conopleura striata HINDS, 1844 in Revisiting the disjunct distribution of Conopleura Hinds, 1844 (Mollusca: Gastropoda: Drilliidae)
FIGURE 1. ORIgINAL LAbELS. A. Conopleura striata HINDS, 1844 OLDEST LAbEL KNOWN. FROM TOP TO bOTTOM: I) PRESUMAbLY HANDWRITTEN bY E.A. SMITH; II) PRESUMAbLY HANDWRITTEN bY J.E. GRAY; III) PRESUMAbLY HANDWRITTEN bY AN UNKNOWN MUSEUM ASSISTANT OR CURATOR [A. SALVADOR, PERS. COMM.]. B. Conopleura latiaxisa CHINO, 2011 HOLOTYPE LAbEL. C. Conopleura aliena SMRIgLIO, MARIOTTINI & CALASCIbETTA, 1999 HOLOTYPE LAbEL. D. Buccinum lima DILLWYN, 1817 OLDEST LAbEL KNOWN, HANDWRITTEN bY L. SPENgLER [T. SCHIøTTE, PERS. COMM.]. E. Kenyonia pulcherrima BRAZIER, 1896 OLDEST LAbEL KNOWN, PRESUMAbLY HANDWRITTEN bY J. BRAZIER [A. CROWTHER, PERS. COMM.]. F. Buccinum labyrinthus GMELIN, 1791 OLDEST LAbEL KNOWN, HANDWRITTEN bY O.A.L. MøRCH [OLDER LAbELS PRESUMAbLY LOST: T. SCHIøTTE, PERS. COMM.].
FIGURES 6 A–Y. Evacanthini types and label data. A–D in Revision of the evacanthine leafhoppers (Hemiptera: Cicadellidae: Evacanthinae) of the Indian subcontinent
FIGURES 6 A–Y. Evacanthini types and label data. A–D. Apphia burmanica Distant; E–H. Apphia assamensis (Ramakrishnan); I–L. Apphia himalayana (Distant); M–P. Bundera venata Distant; Q–T. Evacanthus bellus (Distant); U–Y. E. extremus (Walker).
FIGURES 1–5. Type specimens and their labels. 1–3 in On the generic names proposed in Chrysomelidae (Coleoptera) by Gustaf Johan Billberg
FIGURES 1–5. Type specimens and their labels. 1–3: Chrysomela concolor Fabricius, 1781 (holotype, female); 4–5: Galleruca lausoniae Fabricius, 1798 (lectotype, male).
FIGURE 16. The designated lecotype labels. A in On the blue butterfly taxa established by L.G. Courvoisier with the type locality "Narün" (Lepidoptera, Lycaenidae)
FIGURE 16. The designated lecotype labels. A: Lycaena phlaeas naruena (Courvoisier, 1911); B: Polyommatus icarus wiskotti (Courvoisier, 1911); C: Phengaris cyanecula naruena (Courvoisier, 1911); D: Plebejus idas naruena (Courvoisier, 1913).
Dataset for Doshi et al. "The Possible Harms of Statins: What do Product Labels, Patient Package Inserts, and Pharmacy Leaflets Tell Us?"
<p>Dataset including all prescribing information (drug labels), patient package inserts, and pharmacy leaflets used in our study, as well as our extraction sheets.</p>
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.