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Figure 2 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 2. Bemisia tabaci (Gennadius), (labeled as "extreme variant" by Louise Russell), Yucca Valley, Calif., 8-XI-61, ex: Hibiscus.
Figure 9 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 9. Bemisia tabaci complex, West Malaysia, Genting Highlands, 10/2/85, ex. Fern Fronds. (Reprinted by permission from Springer Science+Business Media B.V.).
Figure 5 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 5. Holotype, Bemisia argentifolii Bellows and Perring, From Lab Culture, U.C. Riverside, Dec. 1992, ex: Phaseolus limensis.
Figure 1 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 1. SYNTYPE, Bemisia tabaci (Gennadius) Slide #1, Specimen #4, Athens, Greece, June 10, 1889, P. Gennadius, coll., Q3120, Bur. Ent. #4449.
Figure 4 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 4. Type material, Bemisia poinsettiae Hempel, Belo Horizante, Minas, Brazil, II-192, ex: Poinsettia.
Figure 7 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 7. Bemisia argentifolii Bellows and Perring, specimen 108D, Mecca California, IX-9-91, ex. Citrus.
Figure 8 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 8. Bemisia argentifolii Bellows and Perring, specimen 68B #4, University of Arizona stock culture, Tucson, ex. cotton.
Fig. 3 in Cephalic labial gland secretions of males as species recognition signals in bumblebees: are there really geographical variations in the secretions of the Bombus terrestris subspecies? (Hymenoptera: Apidae: Bombus)
Fig. 3: Map with pie charts for the eight, probably 'active', compounds ('active' compounds = 100 %), illustrating the composition of labial gland secretions of B. terrestris.
Fig. 1 in Cephalic labial gland secretions of males as species recognition signals in bumblebees: are there really geographical variations in the secretions of the Bombus terrestris subspecies? (Hymenoptera: Apidae: Bombus)
Fig. 1: Linear regression of the percentage of the total peak area of 2,3-dihydrofarnesol dodecanoate vs. 2,3-dihydrofarnesol for males of B. terrestris terrestris (Ter-07) aged 14 days (●) and 21 days (▲) old; see Table 2 for details.
NorHand v3 / Dataset for Handwritten Text Recognition in Norwegian
<p>This dataset comprises Norwegian letter and diary documents from 19th and early 20th century. It can be used to train Handwritten Text Recognition (HTR) models.</p>
CryoVirusDB: An Expert Labelled Cryo-EM Image Dataset for AI-Driven Virus Particle recognition and Extraction
<p><span>With the advancements in instrumentation, image processing algorithms, and computational capabilities, single-particle electron cryo-microscopy (cryo-EM) has achieved nearly atomic resolutions in the 3D reconstruction of viruses. These detailed structures play a crucial role in comprehending the biological functions and advancing the development of more precise vaccines and antiviral treatments. Despite the effectiveness of deep learning in analyzing microscopic images, its potential in identifying and extracting virus particles from cryo-EM micrographs has been hindered by the limited availability of diverse and high-quality datasets. In this study, we introduce 'CryoVirusDB,' a labeled dataset containing coordinates of accurately selected virus particles in cryo-EM micrographs. CryoVirusDB comprises 9,941 micrographs featuring 9 different viruses along with the coordinates of 0.2 million virus particles in total. We anticipate that CryoVirusDB will enhance the capabilities of deep learning in accurately identifying virus particles in cryo-EM micrographs, thereby facilitating the subsequent 2D-3D reconstruction process.</span></p> <p><span>Instructions to download and use dataset: https://github.com/BioinfoMachineLearning/CryoVirusDB</span></p>
Evidence for individual vocal recognition in a pair-bonding poison frog, Ranitomeya imitator
<p>Individually distinctive vocalizations are widespread in nature, although the ability of receivers to discriminate these signals has only been explored through limited taxonomic and social lenses. Here, we asked whether anuran advertisement calls, typically studied for their role in territory defense and mate attraction, facilitate recognition and preferential association with partners in a pair-bonding poison frog (<em>Ranitomeya imitator</em>). Combining no- and two-stimulus choice playback experiments, we evaluated behavioral responses of females to male acoustic stimuli. Virgin females oriented to and approached speakers broadcasting male calls independent of caller identity, implying that females are generally attracted to male acoustic stimuli outside the context of a pair bond. When pair-bonded females were presented with calls of a mate and a stranger, they showed significant preference for calls of their mate. Moreover, behavioral responses varied with breeding status: females with eggs were faster to approach stimuli than females that were pair-bonded but did not currently have eggs. Our study suggests a potential role for individual vocal recognition in the formation and maintenance of pair bonds in a poison frog and raises new questions about how acoustic signals are perceived in the context of monogamy and biparental care.</p>
Research Data: Facial Expression Recognition under Visual Field Restriction
<p>This dataset contains the following files:</p> <p><strong>-</strong> <strong>view_trial.xlsx:</strong> Excel spreadsheet containing data from individual trials.<br><strong>-</strong> <strong>view_participant.xlsx:</strong> Excel spreadsheet containing data aggregated at the participant level.<br><strong>- consensus.xlsx:</strong> Excel spreadsheet containing consensus data analysis.<br><strong>- image_id_list.txt:</strong> Text file listing the IDs of the images used in the study from The Karolinska Directed Emotional Faces (KDEF); https://kdef.se/.</p> <p>These files provide comprehensive data used in the research project titled "Exploring the Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study" conducted by M. B. Urtado, R. D. Rodrigues, and S. S. Fukusima. The dataset is intended for analysis and replication of the study's findings.</p> <p>Please, when using these data, we kindly request citing the following article:<br>Urtado, M.B.; Rodrigues, R.D.; Fukusima, S.S. <strong>Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study</strong>. <em>Behavioral Sciences</em> <strong>2024</strong>, <em>14</em>, 355. <a href="https://doi.org/10.3390/bs14050355">https://doi.org/10.3390/bs14050355</a></p> <p>The study was approved by the Research Ethics Committee (CEP) of the University of São Paulo (protocol code 41844720.5.0000.5407). </p>
GeoEDdA: A Gold Standard Dataset for Named Entity Recognition and Span Categorization Annotations of Diderot & d'Alembert's Encyclopédie
<p>This repository contains a gold standard dataset for named entity recognition and span categorization annotations from Diderot & d’Alembert’s Encyclopédie entries.</p> <p>The dataset is available in the following formats:</p> <ul> <li>JSONL format provided by <a href="https://prodi.gy/" rel="nofollow">Prodigy</a></li> <li>binary spaCy format (ready to use with the spaCy train pipeline)</li> </ul> <p>The Gold Standard dataset is composed of 2,200 paragraphs out of 2,001 Encyclopédie's entries randomly selected. All paragraphs were written in 19th-century French.</p> <p>The spans/entities were labeled by the project team along with using pre-labelling with early machine learning models to speed up the labelling process. A train/val/test split was used. Validation and test sets are composed of 200 paragraphs each: 100 classified under 'Géographie' and 100 from another knowledge domain. The datasets have the following breakdown of tokens and spans/entities.</p> <h2>Tagset</h2> <ul> <li><strong>NC-Spatial</strong>: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. <code>ville</code>, <code>la rivière</code>, <code>royaume</code>.</li> <li><strong>NP-Spatial</strong>: a proper noun identifying the name of a place (spatial named entities), e.g. <code>France</code>, <code>Paris</code>, <code>la Chine</code>.</li> <li><strong>ENE-Spatial</strong>: nested spatial entity , e.g. <code>ville de France</code> , <code>royaume de Naples</code>, <code>la mer Baltique</code>.</li> <li><strong>Relation</strong>: spatial relation, e.g. <code>dans</code>, <code>sur</code>, <code>à 10 lieues de</code>.</li> <li><strong>Latlong</strong>: geographic coordinates, e.g. <code>Long. 19. 49. lat. 43. 55. 44.</code></li> <li><strong>NC-Person</strong>: a common noun that identifies a person (nominal spatial entity), e.g. <code>roi</code>, <code>l'empereur</code>, <code>les auteurs</code>.</li> <li><strong>NP-Person</strong>: a proper noun identifying the name of a person (person named entities), e.g. <code>Louis XIV</code>, <code>Pline</code>.</li> <li><strong>ENE-Person</strong>: nested people entity, e.g. <code>le czar Pierre</code>, <code>roi de Macédoine</code>.</li> <li><strong>NP-Misc</strong>: a proper noun identifying entities not classified as spatial or person, e.g. <code>l'Eglise</code>, <code>1702</code>, <code>Pélasgique</code></li> <li><strong>ENE-Misc</strong>: nested named entity not classified as spatial or person, e.g. <code>l'ordre de S. Jacques</code>, <code>la déclaration du 21 Mars 1671</code>.</li> <li><strong>Head</strong>: entry name</li> <li><strong>Domain-Mark</strong>: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. <code>Géographie</code>, <code>Geog.</code>, <code>en Anatomie</code>.</li> </ul> <h2>HuggingFace</h2> <p>The GeoEDdA dataset is available on the HuggingFace Hub: <a href="https://huggingface.co/datasets/GEODE/GeoEDdA">https://huggingface.co/datasets/GEODE/GeoEDdA</a></p> <h2>spaCy Custom Spancat trained on Diderot & d’Alembert’s Encyclopédie entries</h2> <p>This dataset was used to train and evaluate a custom spancat model for French using <a href="https://spacy.io/" rel="nofollow">spaCy</a>. The model is available on HuggingFace's model hub: <a href="https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda" rel="nofollow">https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda</a>.</p> <h2>Acknowledgement</h2> <p>The authors are grateful to the <a href="https://aslan.universite-lyon.fr/" rel="nofollow">ASLAN project</a> (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). Data courtesy the <a href="https://artfl-project.uchicago.edu/" rel="nofollow">ARTFL Encyclopédie Project</a>, University of Chicago.</p>
A review of Appalachian Dasycerus Brongniart, and the recognition of cryptic diversity within Dasycerus carolinensis Horn (Coleoptera: Staphylinidae: Dasycerinae)
<p>Previous analyses have revealed deep divergences among populations of the relictual and enigmatic rove beetle, <em>Dasycerus carolinensis </em>Horn. New data from additional populations, molecular markers, and morphology unambiguously reveal this 'species' to represent a complex of closely related species, distinguishable by characters of the male genitalia, and corresponding closely to geographically coherent clades discovered by molecular analyses. Calibrated dating analyses show Appalachian <em>Dasycerus</em> to have been diverging in the region for more than 10 million years, yet largely respecting important biogeographic barriers in the region, such as the French Broad and Little Tennessee River drainages. In addition to discussing finer scale biogeographic patterns in the group, we formally recognize 9 new species from within what was formerly known as <em>D. carolinensis</em>: <em>D. virginiensis</em> <strong>sp. nov.</strong><em>, D. tuckasegee</em> <strong>sp. nov.</strong><em>, D. pacolet</em> <strong>sp. nov.</strong><em>, D. chattooga</em> <strong>sp. nov.</strong><em>, D. itseyi</em> <strong>sp. nov.</strong><em>, D. unicoi</em> <strong>sp. nov.</strong><em>, D. nikwasi</em> <strong>sp. nov.</strong><em>, D. egwanulti</em> <strong>sp. nov.</strong><em>, </em>and <em>D. gadalutsi</em> <strong>sp. nov.</strong><em>. </em>It was not, however, possible to assign all samples to one of these species, and specimens from some sparsely sampled outlying areas, northern Alabama and central Tennessee in particular, may represent additional species. </p>
MultiPosture: A Dataset of body joints keypoints extracted using MediaPipe for multi-task sitting posture recognition with upper and lower body labels
<p>This dataset contains skeletal pose data extracted from video recordings of 13 participants performing various sitting postures in home environments. The data was processed using MediaPipe Pose Heavy model and includes 4,800 frames of 3D skeletal coordinates (x, y, z) for 11 key body joints, with each frame manually labeled for both upper and lower body posture classifications.</p> <p>The data is stored in CSV format with normalized coordinates relative to hip center, containing 33 input dimensions (11 joints × 3 coordinates) representing key skeletal points. To protect participant privacy, only the processed skeletal coordinates are included, with no raw video or image data due to privacy constraints.<br><br></p> <p>Upper Body Labels:</p> <ul> <li>TUP: Upright trunk position</li> <li>TLB: Trunk leaning backward</li> <li>TLF: Trunk leaning forward</li> <li>TLR: Trunk leaning right</li> <li>TLL: Trunk leaning left</li> </ul> <p>Lower Body Labels:</p> <ul> <li>LAP: Legs apart</li> <li>LWA: Legs wide apart</li> <li>LCS: Legs closed</li> <li>LCR: Legs crossed right over left</li> <li>LCL: Legs crossed left over right</li> <li>LLR: Legs lateral right</li> <li>LLL: Legs lateral left</li> </ul> <p>Each frame in the dataset has been manually labeled and validated by experts, making it particularly suitable for developing and evaluating machine learning models for ergonomic monitoring systems, ambient assisted living applications, and general posture recognition research.</p> <p>This dataset was collected as part of the study:</p> <p><strong>D. Carneros-Prado, L. Cabañero-Gómez, E. Johnson, I. González, J. Fontecha and R. Hervás, "A Comparison Between Multilayer Perceptrons and Kolmogorov-Arnold Networks for Multi-Task Classification in Sitting Posture Recognition," in <em>IEEE Access</em>, vol. 12, pp. 180198-180209, 2024, doi: 10.1109/ACCESS.2024.3510034. </strong><em><a href="https://ieeexplore.ieee.org/abstract/document/10772228" target="_blank" rel="noopener">link</a></em></p> <p> </p>
US Automatic Number Plate Recognition Market 2024 To 2033
<p><a href="https://www.custommarketinsights.com/report/us-automatic-number-plate-recognition-market/" target="_blank" rel="noopener">US Automatic Number Plate Recognition Market Size</a>, Trends and Insights By Application (Traffic Management, Toll Collection, Law Enforcement, Parking Management), By Product Type (Fixed ANPR Systems, Mobile ANPR Systems, Portable ANPR Systems), By End User (Government Agencies, Law Enforcement, Toll Operators, Parking Management Firms), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p> <p><strong>Reports Description</strong></p> <p>As per the current market research conducted by the CMI Team, the <a href="https://www.custommarketinsights.com/report/us-automatic-number-plate-recognition-market/"><strong>US Automatic Number Plate Recognition Market</strong></a> is expected to record a CAGR of <strong>9.06%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>3.19 Billion</strong>. By 2033, the valuation is anticipated to reach USD 6.93 billion<strong>.</strong></p> <p>This chart shows the count of images and instances trained and validated for car and license plate detection that form a part of ANPR systems in the US marketplace. Shares images were analyzed: 183 images of the training subset and 20 images of the validation subset. In the case of car detection, there were 819 instances 740 were designated for training and 79 for validation.</p> <p>Moreover, 246 license plate instance samples were used, with 227 in the training and 19 in the validation sets. These datasets illustrate the tremendous amount of work and precision involved in the ANPR technology because of the increasing use of such systems for managing traffic issues, enforcing laws and collecting tolls on highways across the United States. This extensive data training emphasizes the accuracy and sharpness of the ANPR systems that authorities provide to the market by definition of vehicle identification and control.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=58799" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=58799</a></p>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Multi-modal dataset for music genre recognition based on six different modalities for LMD-aligned and SLAC datasets
<p>Multi-modal dataset for music genre recognition based on six different modalities for the LMD-aligned [1] and SLAC [2] datasets. Further details are provided in [3].</p> <p><strong>Descriptions of files</strong></p> <table> <thead> <tr> <th scope="col">Link</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_Filelist.arff">LMD-aligned_Filelist.arff</a></td> <td>File list with 1575 music tracks selected from the LMD-aligned dataset [1] with tagtraum genre annotations [4] (only a subset of LMD-aligned is used, which includes only pieces for which all six modalities were accessible, and which includes only well-represented genres)</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_ExtractedFeatures.tar.gz">LMD-aligned_ExtractedFeatures.tar.gz</a></td> <td>Raw audio signal and model-based features extracted with AMUSE [5]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_ProcessedFeatures.tar.gz">LMD-aligned_ProcessedFeatures.tar.gz</a></td> <td>Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_Datasets.tar.gz">LMD-aligned_Datasets.tar.gz</a></td> <td>Training, optimization, and test datasets for 3 splits for the recognition of 5 genres in [3]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_Filelist.arff">SLAC_Filelist.arff</a></td> <td>File list with 250 music tracks from the SLAC dataset [2] (genres and sub-genres are provided in the folder structure)</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_ExtractedFeatures.tar.gz">SLAC_ExtractedFeatures.tar.gz</a></td> <td>Raw audio signal and model-based features extracted with AMUSE [5]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_ProcessedFeatures.tar.gz">SLAC_ProcessedFeatures.tar.gz</a></td> <td>Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_Datasets.tar.gz">SLAC_Datasets.tar.gz</a></td> <td>Training, optimization, and test datasets for 3 splits for the recognition of 5 genres and 10 sub-genres in [3]</td> </tr> </tbody> </table> <p><strong>Modalities and feature sub-groups</strong></p> <table> <thead> <tr> <th scope="col">Modality</th> <th scope="col">Sub-group</th> <th scope="col"> <p>Dimensions in processed</p> <p>features of LMD-aligned</p> </th> <th scope="col"> <p>Dimensions in processed</p> <p>features of SLAC</p> </th> </tr> </thead> <tbody> <tr> <td>Audio signal</td> <td>Low-level</td> <td>1-524</td> <td>1-524</td> </tr> <tr> <td>Audio signal</td> <td>Semantic</td> <td>525-810</td> <td>525-810</td> </tr> <tr> <td>Audio signal</td> <td>Structural complexity</td> <td>811-908</td> <td>811-908</td> </tr> <tr> <td>Model-based</td> <td>Instruments</td> <td>909-1018</td> <td>909-1018</td> </tr> <tr> <td>Model-based</td> <td>Moods</td> <td>1019-1146</td> <td>1019-1146</td> </tr> <tr> <td>Model-based</td> <td>Various</td> <td>1147-1402</td> <td>1147-1402</td> </tr> <tr> <td>Playlists</td> <td>Genres</td> <td>1403-1973</td> <td>1403-1973</td> </tr> <tr> <td>Playlists</td> <td>Styles</td> <td>1974-1695</td> <td>1974-1695</td> </tr> <tr> <td>Symbolic</td> <td>Pitch</td> <td>1696-1757</td> <td>1696-1757</td> </tr> <tr> <td>Symbolic</td> <td>Melodic</td> <td>1758-1781</td> <td>1758-1781</td> </tr> <tr> <td>Symbolic</td> <td>Chords</td> <td>1782-1836</td> <td>1782-1836</td> </tr> <tr> <td>Symbolic</td> <td>Rhythm</td> <td>1837-1935</td> <td>1837-1935</td> </tr> <tr> <td>Symbolic</td> <td>Tempo</td> <td>1936-1963</td> <td>1936-1963</td> </tr> <tr> <td>Symbolic</td> <td>Instrument presence</td> <td>1964-2441</td> <td>1964-2441</td> </tr> <tr> <td>Symbolic</td> <td>Instruments</td> <td>2442-2456</td> <td>2442-2456</td> </tr> <tr> <td>Symbolic</td> <td>Texture</td> <td>2457-2480</td> <td>2457-2480</td> </tr> <tr> <td>Symbolic</td> <td>Dynamics</td> <td>2481-2484</td> <td>2481-2484</td> </tr> <tr> <td>Album covers</td> <td>SIFT</td> <td>2485-2584</td> <td>2485-2584</td> </tr> <tr> <td>Lyrics</td> <td>jLyrics descriptors</td> <td>2585-2603</td> <td>2585-2671</td> </tr> <tr> <td>Lyrics</td> <td>Bag-of-Words</td> <td>2604-2703</td> <td> </td> </tr> <tr> <td>Lyrics</td> <td>Doc2Vec</td> <td>2704-2803</td> <td> </td> </tr> </tbody> </table>
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>
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.