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7,523 results for “Annotation”
Coronavirus Twitter Data: A collection of COVID-19 tweets with automated annotations
<p>This dataset contains tweets related to COVID-19. The dataset contains Twitter ids, from which you can download the original data directly from Twitter. Additionally, we include the date, keywords related to COVID-19 and the inferred geolocation. Check detailed information at <a href="http://twitterdata.covid19dataresources.org/index">http://twitterdata.covid19dataresources.org/index</a>.</p>
Object detection annotations on images from the Rijksmuseum
<p>A dataset containing annotations made on images of cultural heritage digital objects from the Rijksmuseum . The annotations resulted from the application of automatic object detection techniques. This dataset was used for the evaluation of the object detection model developed by the project Saint George on a Bike. The service achieved a precision of 79.4% and a recall of 65.7% in this dataset. The dataset contains:</p> <ul> <li>Object detection annotations (SgoaB-Rijksmuseum-objectDetection.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. It contains 792 annotations on 315 images.</li> <li>Human-validated object detection annotations (SgoaB-Rijksmuseum-objectDetection-validatedSubset.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. This data dump includes only the subset of the annotations that were considered correct after human validation. It contains 506 annotations on 283 images.</li> </ul>
Annotation of genes encoding enzymes across marine phytoplankton genomes
<p>Phytoplankton cells span a large size range, from picoplankton (<2µm), nanoplankton (2 to 20µm), microplankton (20 to 200µm) to macroplankton (200 to <2000µm). Cell size interacts with multiple selective pressures, including cellular metabolic rate, light absorption, nutrient uptake, cell nutrient quotas, trophic interactions and diffusional exchanges with the environment. Beyond simple size, cells of different shapes differ in surface area to volume ratio. For example, more elongated cells, such as pennate diatoms, have a larger surface area to volume ratio compared to more rounded cells, such as centric diatoms, of equivalent biovolume, which can in turn influence diffusional exchanges between cells and their environment. We assembled metadata on diverse marine phytoplankters, in parallel with genomic or transcriptomic data annotations to identify genes encoding enzymes, to facilitate analyses of genomic patterns of encoded enzymes across diverse taxa, sizes, growth forms and origins of strains.</p>
Genome sequences and gene annotations for two Ophryocystis lineages
<p>Assembly, annotation, and gene sequences for the <em>Ophryocystis </em>lineages sequenced in "Genome sequence of <em>Ophryocystis elektroscirrha</em>, an apicomplexan parasite of monarch butterflies: cryptic diversity and response to host-sequestered plant chemicals." Each of the two lineages has three associated files: a genome sequence file (.fa), an annotation in .gff3 format, and gene sequences in .fna format. Sequences generated for <em>Ophryocystis elektroscirrha </em>come from direct DNA extraction and sequencing effort and are hosted elsewhere on NCBI as well. The other lineage, prefixed Ophryocystis-elektroscirrha_like, was bioinformatically extracted from the genome of an infected host. As such, we are less confident in its completeness and it is not archived elsewhere. </p>
Functional Annotations of Bacillus stercoris BHUJPV-SS7
<p>This dataset provides comprehensive genome annotations of Bacillus stercoris BHUJPV-SS7, a cellulose-degrading bacterium with potential applications in biomass bioconversion and biotechnology. The strain was isolated from the rhizosphere soil of a mango tree at the experimental field of Banaras Hindu University, India.</p> <p>The genome annotations included in this dataset were generated using three different tools: Prokka (Prokaryotic Genome Annotation), RAST (Rapid Annotations using Subsystems Technology), and CAZy (Carbohydrate-Active Enzymes database). These annotations provide insights into the functional potential of Bacillus stercoris BHUJPV-SS7, including the presence of genes involved in cellulose degradation, lignin degradation, and other carbon degradation processes.</p> <p>This dataset aims to facilitate further research on the biotechnological potential of Bacillus stercoris BHUJPV-SS7 in areas such as biofuel production, waste management, and sustainable development.</p>
Training data for 'Genome annotation with Funannotate' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with funannotate.</p> <p>Genome was assembled following the GTN Flye assembly tutorial, then masked with RepeatMasker.</p> <p>RNASeq data: SRR8534859 reads were mapped to the genome using STAR (toolshed.g2.bx.psu.edu/repos/iuc/rgrnastar/rna_star/2.7.8a+galaxy0), then the bam was downsampled (10% with toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_DownsampleSam/2.18.2.1) to reduce the size of the dataset. Fastq files were then extracted from the resulting bam file (toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_SamToFastq/2.18.2.1).</p> <p>SwissProt_subset.fasta is a subset of SwissProt proteins that are known to have some similarity with the genome (found using Diamond against the genome, then extracting sequences matching with e-value < 0.0001).</p>
Annotated Video Dataset of Fencing Movements and Corresponding Error Patterns
<p>Video dataset containing 1289 videos and their augmentation of four fencing movements, performed by a variety of fencers. The main actions included are counterattack, lunge, flèche and parry. For each movement samples with typical error patterns are included and annotated. Additionally labels for mulit-labelling are documented. The corresponding paper "Mastering Fencing Techniques with Machine Learning: A Video-Based Classification and Correction System" is published at the 10th IEEE Swiss Conference on Data Science (SDS 2023)</p>
A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment
<p>This dataset contains 1660 images of electric substations with 50705 annotated objects. The images were obtained using different cameras, including cameras mounted on Autonomous Guided Vehicles (AGVs), fixed location cameras and those captured by humans using a variety of cameras. A total of 15 classes of objects were identified in this dataset, and the number of instances for each class is provided in the following table:</p> <table align="center"> <caption>Object classes and how many times they appear in the dataset.</caption> <thead> <tr> <th scope="col">Class</th> <th scope="col">Instances</th> </tr> </thead> <tbody> <tr> <td>Open blade disconnect</td> <td>310</td> </tr> <tr> <td>Closed blade disconnect switch</td> <td>5243</td> </tr> <tr> <td>Open tandem disconnect switch</td> <td>1599</td> </tr> <tr> <td>Closed tandem disconnect switch</td> <td>966</td> </tr> <tr> <td>Breaker</td> <td>980</td> </tr> <tr> <td>Fuse disconnect switch</td> <td>355</td> </tr> <tr> <td>Glass disc insulator</td> <td>3185</td> </tr> <tr> <td>Porcelain pin insulator</td> <td>26499</td> </tr> <tr> <td>Muffle</td> <td>1354</td> </tr> <tr> <td>Lightning arrester</td> <td>1976</td> </tr> <tr> <td>Recloser</td> <td>2331</td> </tr> <tr> <td>Power transformer</td> <td>768</td> </tr> <tr> <td>Current transformer</td> <td>2136</td> </tr> <tr> <td>Potential transformer</td> <td>654</td> </tr> <tr> <td>Tripolar disconnect switch</td> <td>2349</td> </tr> </tbody> </table> <p>All images in this dataset were collected from a single electrical distribution substation in Brazil over a period of two years. The images were captured at various times of the day and under different weather and seasonal conditions, ensuring a diverse range of lighting conditions for the depicted objects. A team of experts in Electrical Engineering curated all the images to ensure that the angles and distances depicted in the images are suitable for automating inspections in an electrical substation.</p> <p>The file structure of this dataset contains the following directories and files:</p> <p> images: This directory contains 1660 electrical substation images in JPEG format.</p> <p>images: This directory contains 1660 electrical substation images in JPEG format.</p> <ul> <li><strong>labels_json: </strong>This directory contains JSON files annotated in the VOC-style polygonal format. Each file shares the same filename as its respective image in the images directory.</li> <li><strong>15_masks:</strong> This directory contains PNG segmentation masks for all 15 classes, including the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory.</li> <li><strong>14_masks:</strong> This directory contains PNG segmentation masks for all classes except the porcelain pin insulator. Each file shares the same name as its corresponding image in the images directory.</li> <li><strong>porcelain_masks:</strong> This directory contains PNG segmentation masks for the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory.</li> <li><strong>classes.txt:</strong> This text file lists the 15 classes plus the background class used in LabelMe.</li> <li><strong>json2png.py:</strong> This Python script can be used to generate segmentation masks using the VOC-style polygonal JSON annotations.</li> </ul> <p>The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. The dataset is expected to be useful for researchers, practitioners, and engineers interested in developing and testing object detection and segmentation models for automating inspection and maintenance activities in electrical substations.</p> <p>The authors would like to thank UTFPR for the support and infrastructure made available for the development of this research and COPEL-DIS for the support through project PD-2866-0528/2020—Development of a Methodology for Automatic Analysis of Thermal Images. We also would like to express our deepest appreciation to the team of annotators who worked diligently to produce the semantic labels for our dataset. Their hard work, dedication and attention to detail were critical to the success of this project.</p>
Figs 10–18 in An annotated type catalogue of the Cerambycidae (Insecta: Coleoptera) in the Zoological Museum Hamburg
Figs 10–18. Secondary type specimens deposited at the Zoological Museum Hamburg. 10. Zographus regalis poleti Le Moult, 1939, paratype (ZMH 843927), labels. 11–12. Zographus regalis favareli Le Moult, 1939, paratype (ZMH 824644). 13–14. Zographus regalis dyoti Le Moult, 1939, paratype (ZMH 843084). 15–16. Zographus regalis boni Le Moult, 1939, paratype (ZMH 843939). 17–18. Zographus regalis lualabenis Le Moult, 1939, paratype (ZMH 843029). Scale bars = 1 cm.
Figs 1–9 in An annotated type catalogue of the Cerambycidae (Insecta: Coleoptera) in the Zoological Museum Hamburg
Figs 1–9. Primary and secondary type specimens deposited at the Zoological Museum Hamburg. 1–2. Primary type: Ites colasi Lepesme, 1943, ♂, holotype (ZMH 824639). 3–9. Secondary types. 3–4. Acridoschema favareli Le Moult, 1938, paratype (ZMH 824657). 5–6. Cyclopeplus peruvianus Tippmann, 1939, ♀, paratype (ZMH 824664). 7–8. Leucographus catalai Villiers, 1939, sex, paratype (ZMH 824638). 9. Zographus regalis poleti Le Moult, 1939, dorsal view, sex, paratype (ZMH 843927) Scale bars = 1 cm.
Figs 108–112. Bombyliidae male genitalia. 108 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 108–112. Bombyliidae male genitalia. 108. Parisus aurantiacus (Macquart). a. ventral view. b. lateral view (from Hesse 1938). 109. Desmatoneura meridionalis (Hesse). 110. Xeramoeba apricaria Hesse. 111. Anthrax pithecius Fabricius. 112. Anthrax aygulus Fabricius (all from Hesse 1956).
Figs 101–106. Bombyliidae male genitalia. 101 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 101–106. Bombyliidae male genitalia. 101. Bombomyia bombiformis (Bezzi). a. lateral view. b. ventral view. 102. Bombylisoma senegalense (Macquart). a. lateral view. b. ventral view. 103. Gonarthrus kalaharicus Hesse. 104. Eurycarenus dichopticum Bezzi. a. lateral view. b. ventral view. 105. Systoechus mixtus (Wiedemann). 106. Beckerellus terminatus (Becker). a. lateral view. b. ventral view (all from Hesse 1938).
Figs 113–118. Bombyliidae male genitalia. 113 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 113–118. Bombyliidae male genitalia. 113. Spogostylum incisurale (Macquart). 114. Spogostylum punctipenne (Wiedemann). 115. Exhyalanthrax abruptus (Loew). 116. Pachyanthrax lutulentus (Bezzi) a. lateral view. b. ventral view. 117. Thyridanthrax perspicillaris (Loew). 118. Heteralonia umbrosa (Loew) (all from Hesse 1956).
Figs 95–99. Bombyliidae wings. 95 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 95–99. Bombyliidae wings. 95. Heteralonia spoliata (Bezzi). 96. Exoprosopa batrachoides Bezzi. 97. Ligyra atricosta Bezzi (all from Bezzi 1924). 98. Heteralonia azaniae Greathead and Evenhuis sp. n. 99. Exoprosopa enigma Greathead and Evenhuis sp. n.
Figs 91–94. Bombyliidae wings. 91 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 91–94. Bombyliidae wings. 91. Thyridanthrax perspicillaris (Loew) (from Bezzi 1924). 92. Hemipenthes velutinus (Meigen) (from Austen 1937). 93. Exhyalanthrax transiens (Bezzi) (from Austen 1929). 94. Litorhina dentifera (Bezzi) (from Bezzi 1924).
Figs 87–90. Bombyliidae wings. 87 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 87–90. Bombyliidae wings. 87. Pteraulax flexicornis Bezzi (from Hesse 1956). 88. Petrorossia letho (Wiedemann) (from Hull 1973). 89. Dicranoclista simpsoni Bezzi. 90. Anthrax aygulus Fabricius (all from Bezzi 1924).
Figs 79–82. Bombyliidae wings. 79 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 79–82. Bombyliidae wings. 79. Systropus leptogaster Loew (from Bezzi 1924). 80. Heterotropus sp. (Namibia) (original) 81. Australoechus punctifer (Bezzi). 82. Systoechus robustus Bezzi (all from Bezzi 1924).
Figs 83–85. Bombyliidae wings. 83 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 83–85. Bombyliidae wings. 83. Anisotamia ruficornis Macquart (from Austen 1937). 84. Othniomyia tylopelta Hesse. 85. Prorachthes conspersipennis Hesse (all from Hesse 1938).
Fig. 78 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Fig. 78. Exoprosopa sp., scanning electron micrograph of female fore tarsi. a. fore tarsal segments showing setation. b. detail of modified hairs with clubbed apices.
Figs 76–77. Bombyliidae habitus drawings. 76 in Annotated keys to the genera of African Bombylioidea (Diptera: Bombyliidae; Mythicomyiidae)
Figs 76–77. Bombyliidae habitus drawings. 76. Eurycarenus melanurus Bezzi, male (from Bezzi 1924). 77. Stomylomyia europaea (Loew), female (from Austen 1937).
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.