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7,523 results for “Annotation”
FIG. 2. — A in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 2. — A sparulus (Diplodus annularis (Linnaeus, 1758)), with its "shining golden nape" (Ovid). Photo credit: Waelbi (CC BY-SA 3.0).
FIG. 1 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 1. – Lat. gladius (above) with a detail of the bill (below), i.e. the "referential constraint" encoded in the descriptive name (lit. "sword"). Engraving from Salviani (1554: pl. 39).
FIG. 9. — A in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 9. — A Roman lacertus (in the foreground). The one depicted is a Scomber sp. (Atlantic mackerel). Detail from a Roman mosaic from Pompeii, 1st century CE (Museo Archeologico Nazionale, Napoli; photo credit: A. Guasparri).
FIG. 14 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 14. — The Roman folk-taxonomy of aquatic turtles (mus2, testudo). Abbreviations: FK, folk kingdom; INT, intermediate; FG, folk-generic; FS, folk-specific. Symbols: *, prototypical; /, synonymy; (…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources; […], not in the checklist inasmuch as non-aquatic. See each entry in Appendix 1 for details.
FIG. 17 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 17. — The Roman folk-taxonomy of cete/beluae marinae (lit. "marine beasts"), which comprises various marine animals. Abbreviations: FK, folk kingdom; LF, life-form; FG, folk-generic. Symbols: *, prototypical; /, synonymy; (…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources. See each entry in Appendix 1 for details.
FIG. 13. — A in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 13. — A spiny lobster (locusta), with some externally shelled molluscs: A, a mussel (musculus); B, clams (chemae); C, spiny dye murices (murices or purpurae). Fresco from Herculaneum, 1st century CE (Museo Archeologico Nazionale, Napoli; photo credits: A. Guaspari).
FIG. 16. — A in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 16. — A, European pond turtle Emys orbicularis (Linnaeus, 1758); B, Dermochelys coriacea (Vandelli, 1861). Photo credits: Katya, CC BY-SA 2.0 (A); Claudia Lombard, CC BY 2.0 (B).
FIG. 3. — A in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 3. — A sample of those aquatic animals the Romans would have called piscis, i.e. fish, molluscs and crustaceans. Detail from a Roman mosaic from Pompeii, Ist century CE (Museo Archeologico Nazionale, Napoli; photo credit: A. Guasparri).
FIG. 12 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 12. — The Roman folk-taxonomy of cancer2/crustata (i.e., mostly, our crustaceans). Abbreviations: Abbreviations: LF, life-form; LF1+, sublife-form; INT, intermediate; FG, folk-generic; FS, folk-specific. Symbols: *, prototypical; /, synonymy; (…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources;+ (superscript), multiple ethnotaxonomic ascription due to different statements in the sources. See each entry in Appendix 1 for details.
FIG. 4 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 4. — The Roman folk-taxonomy of mollia (i.e. our cephalopods) and other invertebrates (sponges, jellyfish, etc.). Abbreviations: FK, folk kingdom; LF, lifeform; LF1+, sublife-form; INT, intermediate; FG, folk-generic; FS, folk-specific. Symbols: + (superscript), multiple ethnotaxonomic ascription due to different statements in the sources; /, synonymy;(…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources;?, presumed folk taxon. See each entry in Appendix 1 for details.
Annotated images from yeast cell lifespans - Training & Test sets - DetecDiv (id01)
<p>This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below).</p> <p>It contains ~250 000 images from 250 cellular lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image.</p> <p>Besides, this folder also contains a .mat file containing 250 timeseries of classes corresponding to the lifespan of the 200 cells.</p> <p> </p> <p>The dataset used for training (200 cellular lifespans) is in the folder /trainingset, while the dataset used for testing (50cellular lifespans) is in the folder /testset</p> <p>It has been used to train the network <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a></p> <p><a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p> </p> <p><strong>Data type</strong>: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat)</p> <p><strong>Microscopy data type</strong>: Brightfield images with 3 stacks, each stack representing a color of RGB.</p> <p><strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS</p> <p><strong>Cell type</strong>: Budding yeast wild type cell (BY4742)</p> <p><strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat</p> <p><strong>Image size</strong>: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Wikipedia video games similarity dataset with expert annotations
<p>A video games NLP dataset extracted from Wikipedia.</p> <p>For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections.</p> <p>The article structure, and particularly the sub-titles and paragraphs are kept in these picese.</p> <p>Provided as well are 90 seeds with recommended articles, annotated by human experts.</p>
Dataset: Sentiment Analysis annotation of News headlines covering the Olympic legacy of Rio 2016 and London 2012 published by the Brazilian and British online media
<p>Dataset of 464 news headlines with sentiment manually annotated by a domain expert using the labels positive, negative and neutral. Data contains URLs for news articles published between 2004-2020 by the British and Brazilian media in English and Brazilian Portuguese covering the Olympic legacies of London 2012 and Rio 2016. Articles were collected from the news outlets’ websites using Google search engine.</p> <p>News outlets:</p> <ul> <li>The Guardian</li> <li>Daily Mail</li> <li>Globo</li> <li>Estadao</li> </ul>
T2Dv2 numeric columns annotation
<p>This files contains the annotation of numeric columns of the T2Dv2 dataset.</p>
Tara Pacific Qualitative Photo Annotations
<p>This data is the result of photographic annotations done manually through Matlab for the photographs captured during the Tara Pacific Expedition (2016-2018). More details can be found in the readme file.</p>
An annotated corpus of clinical trial publications supporting schema-based relational information extraction
<p>Repository of an annotated corpus of clinical trial abstracts supporting schema-based relational information extraction and the code for the inter-annotation agreement calculation and the baseline information extraction method.</p>
Dataset of UAV thermal video sequences with annotations for MOTS benchmarking
<p>Instance segmentation dataset created for the research 'Monitoring Mammalian Herbivores via Convolutional Neural Networks implemented on Thermal UAV imagery'. It comprises 959 frames, 20.647 masks, and 239 tracks, and consists of 7 video sequences depicting aerial thermal imagery of cattle collected with a UAV (Parrot ANAFI Thermal) in two outdoor farms in the Netherlands. Data were acquired at three temperatures (10ºC, 19ºC, and 26.5ºC), under sunny and overcast weather conditions, at various angles of inclination (including nadir), and at heights ranging between 8-28 meters. Ground truth was labeled manually with the Computer Vision Annotation Tool <em>CVAT</em>.</p>
MigrationsKB: A Knowledge Base of Migration related annotated Tweets
<p><strong>MigrationsKB(MGKB)</strong> is a public Knowledge Base of anonymized <strong>Migration</strong> related <strong>annotated</strong> tweets. The MGKB currently contains over <strong>200 thousand</strong> tweets, spanning over 9 years (January 2013 to July 2021), filtered with 11 European countries of <em>the United Kingdom, Germany, Spain, Poland, France, Sweden, Austria, Hungary, Switzerland, Netherlands and Italy</em>. <strong>Metadata</strong> information about the tweets, such as Geo information (<strong>place name</strong>, <strong>coordinates</strong>, <strong>country code</strong>). <strong>MGKB</strong> contains <strong>entities</strong>, <strong>sentiments</strong>, <strong>hate speeches</strong>, <strong>topics</strong>, <strong>hashtags</strong>, <em>encrypted user mentions</em> in RDF format. The schema of <strong>MGKB</strong> is an extension of TweetsKB for migrations related information. Moreover, to associate and represent the potential economic and social factors driving the migration flows such as <a href="https://ec.europa.eu/eurostat/web/main/home"><strong>eurostat</strong></a>, <a href="https://www.statista.com/"><strong>statista</strong></a>, etc. FIBO ontology was used. The extracted <strong>economic indicators</strong>, such as GDP Growth Rate, are connected with each Tweet in RDF using geographical and temporal dimensions. The user IDs and the tweet texts are encrypted for privacy purposes, while the tweet IDs are preserved.</p> <p>For this version, the <strong>MGKB</strong> is delivered as a whole and separately by year. The extracted entities and topic words are also published.</p> <p>Online SPARQL endpoint <a href="https://mgkb.fiz-karlsruhe.de/sparql/">https://mgkb.fiz-karlsruhe.de/sparql/</a></p> <p>More information please refer to the website <a href="https://migrationskb.github.io/MGKB/">https://migrationskb.github.io/MGKB/</a>.</p> <p>Please contact Yiyi Chen (yiyi.chen@partner.kit.edu) for pretrained models (sentiment analysis/hate speech detection/ETM) if necessary.</p>
MGBC-26640: nucleotide sequences for gene annotations
<p>Nucleotide sequences of annotated genes from the 26,640 high-quality, non-redundant genomes of the MGBC.</p>
Aliarcobacter butzleri gene annotation and transcriptome data
<p>Genomes assembled sequences, functional annotation files (Prokka), logFC table of 3 A. butzleri strains isolated from human (LMG 10828<sup>T</sup>, LMG 11119, 31).</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.