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271 results for “annotated dataset”
Line-level Named Entity Recognition annotation for the George Washington and IAM datasets
<p>Line-level Named Entity annotation for the George Washington and IAM datasets. The word-level annotations from Oliver Tüselmann [3] were extended to line-level to enable experimentation with line-level coupled HTR+NER models. We also publish the line-level partition files that result from the partition proposed by [3].</p>
Annotated dataset mentions corpus in IR/ML/NLP domain
<p>This corpus is a re-annotated version of the dataset available at <a href="https://github.com/xjaeh/ner_dataset_recognition">https://github.com/xjaeh/ner_dataset_recognition</a> and described in the following publication:</p> <p>Heddes, J.; Meerdink, P.; Pieters, M.; Marx, M. The Automatic Detection of Dataset Names in Scientific Articles. <em>Data</em> 2021, <em>6</em>, 84. <a href="https://doi.org/10.3390/data6080084">https://doi.org/10.3390/data6080084</a></p> <p>The corpus contains 6000 sentences in the IR/ML/NLP domains, with dataset annotations.</p> <p>The original corpus in CSV covered only explicitly named and reused datasets. In addition, "conjunctions" of datasets were annotated in a single span.</p> <p>We review entirely the annotation to include new datasets too (as developed in the described research work of the source articles) and to annotate separately every individual datasets. In addition, we re-packaged the corpus into a more standard JSON with annotation offsets. Python scripts for conversion are available at <a href="https://github.com/kermitt2/dataset_recognition_resources">https://github.com/kermitt2/dataset_recognition_resources</a></p> <p>We thank the original authors of the corpus for their very valuable resource !</p> <p> </p>
Audiovisual Moments in Time: A Large-Scale Annotated Dataset of Audiovisual Actions
<p>We present Audiovisual Moments in Time (AVMIT), a large-scale dataset of audiovisual action events. In an extensive annotation task 11 participants labelled a subset of 3-second audiovisual videos from the Moments in Time dataset (MIT). For each trial, participants assessed whether the labelled audiovisual action event was present and whether it was the most prominent feature of the video. The dataset includes the annotation of 57,177 audiovisual videos, each independently evaluated by 3 of 11 trained participants. From this initial collection, we created a curated test set of 16 distinct action classes, with 60 videos each (960 videos). We also offer 2 sets of pre-computed audiovisual feature embeddings, using VGGish/YamNet for audio data and VGG16/EfficientNetB0 for visual data, thereby lowering the barrier to entry for audiovisual DNN research. We further carried out an experiment to explore the utility of the AVMIT annotations and feature embeddings. A series of 6 Recurrent Neural Networks (RNNs) were trained on either AVMIT-filtered audiovisual events or modality-agnostic events from MIT, and then tested on our audiovisual test set. In all RNNs, top 1 accuracy was increased by 2.71-5.94\% by training exclusively on audiovisual events, even outweighing a three-fold increase in training data. We anticipate that the newly annotated AVMIT dataset will serve as a valuable resource for research and comparative experiments involving computational models and human participants, specifically when addressing research questions where audiovisual correspondence is of critical importance.</p>
Counted Biopores dataset used in 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p>Counted biopores dataset used in the article: 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'</p> <p>Originally collected as part of a field trial at the University of Bonn in 2012, described in the following paper:</p> <p>Eusun Han, Timo Kautz, Ute Perkons, Marcel Lüsebrink, Ralf Pude, and Ulrich Köpke.Quantification of soil biopore density after perennial fodder cropping.Plant and Soil, 394(1-2):73–85, sep 2015. ISSN 15735036. doi:10.1007/s11104- 015- 2488- 3</p>
Counted Nodules dataset used in 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p>Counted Nodules dataset used in the article: 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'</p>
RAM55 annotated dataset
<p>This .hd5 file contains an annotated (via DSSP and other tools) version of the RAM55 rotamer state substitution dataset (see https://doi.org/10.1093/molbev/msz122).</p>
Dataset - research methodology annotation (Information Science)
<p>Datasets used for developing text mining methods for extracting research methods reported in Information Science journal articles. </p>
Jingju a cappella singing syllable boundary and duration annotation dataset
<p>This dataset is a collection of syllable boundary annotations and syllable duration annotations of a cappella singing performed by jingju (京剧, Beijing opera) professional and amateur singers. This dataset was used as the experimental dataset in the following work:</p> <blockquote> <p>Rong Gong, Nicolas Obin, Georgi Dzhambazov and Xavier Serra, “Score-Informed syllable segmentation for jingju a cappella singing voice with Mel-frequency intensity profiles," in<em> Folk Music Analysis workshop (FMA) 2017, Málaga, Spain</em></p> </blockquote> <p><strong>Audio Content</strong></p> <p>The audio files are the a cappella singing arias recordings, which are stereo or mono, sampled at 44.1 kHz, and stored as wav files. They can be found at this link http://doi.org/10.5281/zenodo.344932</p> <p>The wav files are recorded by two institutes: those file names ending with ‘qm’ are recorded by C4DM Queen Mary University of London; others file names ending with ‘upf’ or ‘lon’ are recorded by MTG-UPF. If you use the dataset in your work, please cite the following publication.</p> <blockquote> <p>D. A. A. Black, M. Li, and M. Tian, “Automatic Identification of Emotional Cues in Chinese Opera Singing,” in <em>13th Int. Conf. on Music </em><em>Perception and Cognition</em> (ICMPC-2014), 2014, pp. 250–255.</p> </blockquote> <p><strong>Annotations</strong></p> <p>The syllable boundary annotation is in Textgrid format (Praat). The annotation is done in both phrase-level and syllable-level. The syllable duration annotation is in cvs format. Please consult Readme text in both folders for further details. The parsing code of the annotation files is provided in ‘pycode’ folder. </p> <p><strong>Availability of the Dataset</strong></p> <p>The annotations and codes in this dataset are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p> <p><strong>Contact</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us.</p> <p>Rong Gong: rong<dot>gong<at>upf<dot>edu</p> <p>Rafael Caro Repetto: rafael<dot>caro<at>upf<dot>edu</p>
Dataset for "Bacterial genome annotation" and "AMR gene detection" workflows
<p>This dataset is associated with the workflows "Bacterial genome annotation" and "AMR gene detection in an assembled bacterial genome".</p>
TweetC19SR-Eng - Manually annotated dataset of English language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
TweetC19SR-Spa - Manually annotated dataset of Spanish language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
Dataset and Analysis Scripts for Survey "Understanding Security Tactics in Microservice APIs using Annotated Software Architecture Decomposition Models -- A Controlled Experiment"
<pre>Dataset, R-Scripts and questionnaire templates for our survey <em>Understanding Security Tactics in Microservice APIs using Annotated Software Architecture Decomposition Models -- A Controlled Experiment.</em></pre>
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
<p>We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:</p> <p>1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. </p> <p>2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.</p> <p>3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research. Due to the large size of the multi-modal dataset (DEM 1.91G, NDVI 164G, VVVH 372G), these dataset are stored on Baidu Yunpan, the download link is :https://pan.baidu.com/s/12AKbiqOXSf4fnm7mYkCE0g?pwd=230k, the extraction code is 230k.</p> <p>The image patches and their corresponding annotated patches are respectively stored in "image_patch.zip" and "label_patch.zip" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The corresponding DEM, NDVI and VVVH patches are all in form of ".tif", with size of 512x512 (due to the different resolution of DEM, NDVI and VVVH patches, they are all uniformly resized to the same scale as the image patch). </p> <p>The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train_num.txt","val_num.txt","test_num.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).</p> <p>We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.</p>
Large-scale annotated dataset for cochlear hair cell detection and classification
<p>Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5 - 15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following <i>in-vivo</i> and <i>in-vitro</i>ototoxic drug application. The dataset includes over 107,000 hair cells which have been manually identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease. </p><p>Associated code is provided here: https://github.com/indzhykulianlab/hcat-data</p>
Public metagenome datasets annotated using SingleM, using a supplemented reference package.
<p>The SingleM package used for supplementing is available at 10.5281/zenodo.10360136</p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Deep Learning Annotation Dataset and Images of Pea Aphids
<p><span>The small size and extensive polymorphisms of aphids make it difficult to identify larvae and adults solely based on their morphology. Here, we present an identification tool for the developmental stages of <em>Acyrthosiphon</em> <em>pisum</em> (Hemiptera: Aphididae) based on deep learning as a proof of concept. You Only Look Once (YOLO) algorithm is one of the most effective deep learning techniques for object detection. Although several studies have been conducted using deep learning technology for the detection and counting of tiny pests, the type of light source and size of the images were the limiting factors, as training was highly focused on uniform datasets and small insects. One way to overcome this problem is to introduce many types of datasets obtained from various light sources and microscopic magnifications. This strategy minimizes errors and omissions in aphid detection across all developmental stages in aphid individuals to the greatest extent possible. The experimental results showed that our modified YOLOv8 model could obtain over 95.9% and 99% accuracy for mean average precision (mAP) and Recall, respectively, under various light sources, such as yellow, white, and natural light, and stereomicroscope magnifications. This study showed an improved accuracy of aphid recognition at all developmental stages.</span><span> </span><span>The study presents a novel deep learning model utilizing the YOLO algorithm to identify developmental stages of </span><em><span>A</span></em><span>. </span><em><span>pisum</span></em><span>. This model achieves high accuracy across various light sources and magnifications, thereby enhancing aphid biology studies.</span></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>
Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection
Open the record for dataset details and reuse information.
Mouse Annotated Dataset
<p>Contains 5 minutes clips from a 2 day recording of 10 mice (5*SWISS + 5*C57BL/6). For each video there is a .csv file that contains the manual annotation of every frame. The data was acquired with EthoProfile, a 10 cage rack developed as part of a research project, and used to train and test the DeepEthoProfile annotation software.</p> <p> </p> <p>Version update:</p> <p>Additionally, files ending with "_map.csv" have been processed from the original data files to contain exactly 8 annotations.</p> <p>Files ending with "_map_new.csv" are a reviewed version with more consistent annotaions.</p> <p>Files ending with "_map_new_v2.csv" are a reviewed version with an additional behavior category, 'None', for the frames where the mouse is turned away from the camera but not resting.</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.