Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,612
datasets available to search
ShareScore release 0.9.0
Dataset results
4,612 results for “labelled”
SN2N's 3D dataset of outer mitochondrial membrane network of live COS-7 cells labeled with Tom20-mCherry on SD-SIM sysytem.
Open the record for dataset details and reuse information.
Lower face images labeled with corresponding motion sickness scores
<p>This is the dataset associated with the manuscript "Detecting in-car VR Motion Sickness from Lower Face Action Units". This dataset includes 1) all lower face images captured by the HP Omnicept VR headset during a motion sickness induction study; 2) Each image is labeled with a corresponding motion sickness score reported by a participant immediately after each motion sickness induction block finished</p> <p>For example,</p> <p>if the name of an image is 1-0_2, then it means that the participant whose ID=1 reported a motion sickness score=2 after the 1st motion sickness induction block finished;</p> <p>if the name of an image is 1-1_4, then it means that the participant whose ID=1 reported a motion sickness score=4 after the 2nd motion sickness induction block finished.</p>
Fig. 2 - The label for MSNM i12963 in Turriculate gastropods (Coelostylinidae) from the Esino limestone outcrop (Ladinian, Lombardy) of the Stoppani Collection housed at the Museo Civico di Storia Naturale, Milan (Italy)
Fig. 2 - The label for MSNM i12963, Nerinea megaspira, hand written by Stoppani.
Quantifying the Spatial Distribution of Fluorescently-Labeled Cell Markers in Stroma-Rich Tumors
<p>Cell measurements (intensity for various compartments, cell morphology, spatial distance from stroma) from image processing of mouse xenograft tumour tissues stained for DAPI, Pan Cytokeratin, Fibronectin, pNDRG1 or Ki67. </p>
A general framework for optimizing arterial spin labeling MRI experiments
<p>This repository contains the simulation data, preprocessed in vivo data, and analysis code used in the Magnetic Resonance in Medicine article titled "A general framework for optimizing arterial spin labeling MRI experiments" (<a href="https://doi.org/10.1002/mrm.27580">https://doi.org/10.1002/mrm.27580</a>). Please cite this article if you use this code or data in your work.</p>
Flute audio labelled database for Automatic Music Transcription
<p>Automatic Music Transcription (ATM) is a well-known task in the Music Information Retrieval (MIR) domain and consists on the computation of a symbolic music representation from an audio recording. In this work, our focus is to adapt algorithms that extract musical information from an audio file for a particular instrument. The main objective is to study the automatic transcription of digitized music support systems. Currently, these techniques are applied to a generic sound timbre, to sounds to any instrument for further analysis and conversion to a digital music encoding and final score format. The results of this project add new knowledge in this automatic transcription field, since traverse flute has been selected as the instrument on which to focus all the process and, until now, there is no database of flute sounds for this purpose.</p> <p>For so, we have recorded some sounds, both monophonic and polyphonic music. These audio files have been processed by the chosen transcription algorithm and converted to a digital music encoding format for its posterior alignment with the original recordings. Once all these data have been converted to text, the resulting labeled database its constituted by the initial audios and final aligned files.</p> <p>Furthermore, after this process and from the obtained data, an evaluation of the transcriptor behavior has been made based on two main techniques: note and frame level.</p> <p>This database includes the original audio files (.wav), transcribed MIDI files (.mid), aligned MIDI files (.mid), aligned text files (.txt) and evaluation files (.csv).</p>
359,569 commits with source code density; 1149 commits of which have software maintenance activity labels (adaptive, corrective, perfective)
<p>This dataset comes as SQL-importable file and is compatible with the widely available MariaDB- and MySQL-databases.</p> <p>It is based on (and incorporates/extends) the dataset "<em>1151 commits with software maintenance activity labels (corrective,perfective,adaptive)</em>" by Levin and Yehudai (<a href="https://doi.org/10.5281/zenodo.835534">https://doi.org/10.5281/zenodo.835534</a>).</p> <p>The extensions to this dataset were obtained using <em>Git-Tools</em>, a tool that is included in the <strong>Git-Density</strong> (<a href="https://doi.org/10.5281/zenodo.2565238">https://doi.org/10.5281/zenodo.2565238</a>) suite. For each of the projects in the original dataset, Git-Tools was run in <em>extended</em> mode.</p> <p>The dataset contains these tables:</p> <ul> <li><strong>x1151</strong>: The original dataset from Levin and Yehudai. <ul> <li>despite its name, this dataset has only 1,149 commits, as two commits were duplicates in the original dataset.</li> <li>This dataset spanned 11 projects, each of which had between 99 and 114 commits</li> <li>This dataset has <strong>71</strong> features and spans the projects <em>RxJava, hbase, elasticsearch, intellij-community, hadoop, drools, Kotlin, restlet-framework-java, orientdb, camel</em> and <em>spring-framework</em>.</li> </ul> </li> <li><strong>gtools_ex</strong> (short for <em>Git-Tools, extended</em>) <ul> <li>Contains <strong>359,569</strong> commits, analyzed using Git-Tools in extended mode</li> <li>It spans all commits and projects from the x1151 dataset as well.</li> <li>All 11 projects were analyzed, from the initial commit until the end of January 2019. For the projects <em>Intellij</em> and <em>Kotlin</em>, the first 35,000 resp. 30,000 commits were analyzed.</li> <li>This dataset introduces <strong>35 new</strong> features (see list below), 22 of which are <em><strong>size</strong></em>- or <em><strong>density</strong></em>-related.</li> </ul> </li> </ul> <p>The dataset contains these views:</p> <ul> <li><strong>geX_L</strong> (short for Git-<em>tools, extended, with labels</em>) <ul> <li>Joins the commits' labels from <em>x1151</em> with the extended attributes from <em>gtools_ex</em>, using the commits' hashes.</li> </ul> </li> <li><strong>jeX_L</strong> (short for <em>joined, extended, with labels</em>) <ul> <li>Joins the datasets <em>x1151</em> and <em>gtools_ex</em> entirely, based on the commits' hashes.</li> </ul> </li> </ul> <p> </p> <p>Features of the <strong>gtools_ex</strong> dataset:</p> <ul> <li><strong>SHA1</strong></li> <li><strong>RepoPathOrUrl</strong></li> <li><strong>AuthorName</strong></li> <li><strong>CommitterName</strong></li> <li><strong>AuthorTime </strong>(UTC)</li> <li><strong>CommitterTime </strong>(UTC)</li> <li><strong>MinutesSincePreviousCommit</strong>: Double, describing the amount of minutes that passed since the previous commit. Previous refers to the <strong>parent</strong> commit, not the previous in time.</li> <li><strong>Message</strong>: The commit's message/comment</li> <li><strong>AuthorEmail</strong></li> <li><strong>CommitterEmail</strong></li> <li><strong>AuthorNominalLabel</strong>: All authors of a repository are analyzed and merged by Git-Density using some heuristic, even if they do not always use the same email address or name. This label is a unique string that helps identifying the same author across commits, even if the author did not always use the exact same identity.</li> <li><strong>CommitterNominalLabel</strong>: The same as <em>AuthorNominalLabel</em>, but for the committer this time.</li> <li><strong>IsInitialCommit</strong>: A boolean indicating, whether a commit is preceded by a parent or not.</li> <li><strong>IsMergeCommit</strong>: A boolean indicating whether a commit has more than one parent.</li> <li><strong>NumberOfParentCommits</strong></li> <li><strong>ParentCommitSHA1s</strong>: A comma-concatenated string of the parents' SHA1 IDs</li> <li><strong>NumberOfFilesAdded</strong></li> <li><strong>NumberOfFilesAddedNet</strong>: Like the previous property, but if the net-size of all changes of an added file is zero (i.e. when adding a file that is empty/whitespace or does not contain code), then this property does not count the file.</li> <li><strong>NumberOfLinesAddedByAddedFiles</strong></li> <li><strong>NumberOfLinesAddedByAddedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesDeleted</strong></li> <li><strong>NumberOfFilesDeletedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesDeletedByDeletedFiles</strong></li> <li><strong>NumberOfLinesDeletedByDeletedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesModified</strong></li> <li><strong>NumberOfFilesModifiedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfFilesRenamed</strong></li> <li><strong>NumberOfFilesRenamedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesAddedByModifiedFiles</strong></li> <li><strong>NumberOfLinesAddedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByModifiedFiles</strong></li> <li><strong>NumberOfLinesDeletedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesAddedByRenamedFiles</strong></li> <li><strong>NumberOfLinesAddedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByRenamedFiles</strong></li> <li><strong>NumberOfLinesDeletedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>Density</strong>: The ratio between the two sums of all lines added+deleted+modified+renamed and their resp. gross-version. A density of zero means that the sum of net-lines is zero (i.e. all lines changes were just whitespace, comments etc.). A density of of 1 means that all changed net-lines contribute to the gross-size of the commit (i.e. no useless lines with e.g. only comments or whitespace).</li> <li><strong>AffectedFilesRatioNet</strong>: The ratio between the sums of <em>NumberOfFilesXXX</em> and <em>NumberOfFilesXXXNet</em></li> </ul> <p> </p> <p>This dataset is supporting the paper <strong>"<em>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities</em></strong><strong>"</strong>, as submitted to the <em>QRS2019</em> conference (The 19th IEEE International Conference on Software Quality, Reliability, and Security). Citation: Hönel, S., Ericsson, M., Löwe, W. and Wingkvist, A., 2019. Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. In <em>The 19th IEEE International Conference on Software Quality, Reliability, and Security</em>.</p>
Mapped reads for the 24h labeling experiment of Herzog et al., Nature Methods 2017
<p>This are the mapped reads for samples GSM2666816,GSM2666817,GSM2666818,GSM2731767,GSM2731768,GSM2731769 against the Mouse genome (mm10, gtf from Ensembl 90). Reads were mapped (after rRNA removel) using STAR:</p> <p>STAR --runMode alignReads --runThreadN 8 --genomeDir ens90.STAR-index --readFilesIn X.fastq --outSAMmode NoQS --outSAMattributes nM MD </p> <p>These are the test data for GRAND-SLAM (https://github.com/erhard-lab/gedi/wiki/GRAND-SLAM)</p>
Image Label Wikification Collection (ILWC)
<p>Image Label Wikification Collection (ILWC) is a test collection for evaluating systems that link descriptional labels of images to Wikipedia pages as the labels' entities. ILWC consists of 450 images and a total of 2,280 user-assigned labels with manual correct annotations. </p> <p>This collection contains following files:</p> <ul> <li>image_URL.txt</li> <li>topics.txt</li> <li>rels.txt</li> <li>ImageCLEF_concepts.txt</li> </ul> <p>image_URL.txt is a list of image files and <a href="https://www.flickr.com">Flickr</a> URLs from which the files were downloaded.</p> <p>topics.txt is a topic file. First two letters of topic ID in the topic file correspond to the collection category of animal names. The correspondence is as follows: </p> <ol> <li>Albatross</li> <li>Bee</li> <li>Bison</li> <li>Boar</li> <li>Coyote</li> <li>Cricket</li> <li>Jaguar</li> <li>Kite</li> <li>Llama</li> <li>Mouse,</li> <li>Panther</li> <li>Quail,</li> <li>Stingray</li> <li>Tiger shark</li> <li>Whippet</li> </ol> <p>rels.txt is a list of correct Wikipedia pages for user-assigned image labels of the topics.</p> <p>ImageCLEF_concepts.txt is a list of the used concepts in <a href="https://www.imageclef.org/2014/annotation">ImageCLEF2014 Scalable Concept Image Annotation task</a> test collection for Image Label Wikification task.</p>
Model weights for single-label, 5-class models from https://doi.org/10.1101/482877
<p>The set of molecules used for training is in "5cls_rmsaltol.csv". If you try to predict from an image of any of these molecules the model will almost certainly be correct because this was the training data. </p> <p>5cls_single_novalidation.model.h5 uses only the molecule with index 0 from 5cls_rmsaltol.csv.</p> <p>5cls_single_valset0.h5 and 5cls_single_valset1.h5 both used 20% of the data as validation set - the respective indexes of those validation sets are givent in 5cls_newskf0.csv and 5cls_newskf1.csv</p> <p> </p> <p> </p> <p> </p>
Trained MNIST predictions for label shift experiments
<p>Trained MNIST predictions for label shift experiments:<br> https://github.com/kundajelab/label_shift_experiments/</p>
Dataset: An Open Dataset and Collection Tool for BMS Point Labels
<p>This dataset consists of a set of CSV files each representing a "dump" of point labels from a set of buildings. Most of the files only contain a single column (point labels); several of them also contain other dimensions such as engineering units, Haystack tags and other BACnet object properties.</p>
Healthcare articles labeled "Professional" and "Non-Professional"
<p>The dataset collected for the research:</p> <p><a href="https://doi.org/10.1109/jcsse61278.2024.10613689" target="_blank" rel="noopener">Professional-level Classification for Healthcare Articles</a></p> <p>URL: https://doi.org/10.1109/jcsse61278.2024.10613689</p> <p> </p> <p>For more details about the experiment, please refer to the paper.</p> <p>The source code for verifying the experiment results is also included.</p> <p> </p>
Corpus of song lyrics in Spanish labeled for gender-based violence against women
<pre><strong>Content:</strong> Labeled corpus including expressions of gender-based violence extracted from song lyrics in Spanish.<br><strong>Tags:</strong> [0: no gender-based violence; 1: gender-based violence] <strong>Description:</strong> It consists of 1000 labeled expressions, of which 778 correspond to expressions without content of gender-based violence and 222 data <br>that contain expressions of gender-based violence collected from song lyrics in Spanish. <strong>Language:</strong> Spanish <strong>Size:</strong> 549KB</pre>
Table S1: Records of bramble shark sightings on 16 December, 2023, offshore of Fujairah, UAE, in the Gulf of Oman. Time is provided in Zulu time (GMT), not in local time. Vessels where sightings occurred are labeled as remotely operated vessels (ROV: Chimera) or human-driven submersibles (Sub: Neptune and Nadir).; Video S1: Video footage of a bramble shark (Echinorhinus brucus) encounter with a submersible at 780 m depth offshore from Fujairah, United Arab Emirates.
<p><span>Table S1: Records of bramble shark sightings on 16 December, 2023, offshore of Fujairah, UAE, in the Gulf of Oman. Time is provided in Zulu time (GMT), not in local time. Vessels where sightings occurred are labeled as remotely operated vessels (ROV: Chimera) or human-driven submersibles (Sub: Neptune and Nadir).</span></p> <p><span>Video S1: Video footage of a bramble shark (<em>Echinorhinus brucus</em>) encounter with a submersible at 780 m depth offshore from Fujairah, United Arab Emirates.</span></p>
Covalent labelling of FabF and FabB by cerulenin and a small series of synthesized compounds
<p><a href="../api/records/13739774/draft/files/FabF,%20FabB%20covalent%20labelling%20of%20selected%20compounds.txt/content" target="_blank" rel="noopener noreferrer">FabF, FabB covalent labelling of selected compounds.txt</a> contains raw data of covalent labelling of the <em>P. Aeruginosa</em> enzymes (FabF wild type (WT), "mut" = FabF C164A, FabB wild type (WT) and "mut" = FabB C161A) by cerulenin and a small set of synthesized compounds. The identity of the compounds in the .txt file can be found in <a href="../api/records/13739774/draft/files/Selected%20compounds.cdx/content" target="_blank" rel="noopener noreferrer">Selected compounds.cdx</a>.</p>
Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis
<p><strong>Please cite the following paper when using this dataset</strong>:</p> <p>N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292</p> <p><strong>Abstract</strong></p> <p>The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this <strong>multilingual dataset of</strong> <strong>60,127 Instagram posts</strong> about mpox, published between <strong>July 23, 2022, and September 5, 2024</strong>. This dataset contains Instagram posts about mpox in <strong>52 languages</strong>. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset.</p> <p>After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post into</p> <ul> <li>one of the fine-grain sentiment classes, i.e., <strong>fear, surprise, joy, sadness, anger, disgust, or neutral</strong>, </li> <li><strong>hate or not hate</strong></li> <li><strong>anxiety/stress detected or no anxiety/stress detected</strong>.</li> </ul> <p>These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications. </p> <p><strong>The 52 distinct languages in which Instagram posts are present in the dataset </strong><strong>are </strong>English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian. </p> <p>The following table represents the data description for this dataset</p> <table> <tbody> <tr> <td> <p><strong>Attribute Name</strong></p> </td> <td> <p><strong>Attribute Description</strong></p> </td> </tr> <tr> <td> <p>Post ID</p> </td> <td> <p>Unique ID of each Instagram post</p> </td> </tr> <tr> <td> <p>Post Description</p> </td> <td> <p>Complete description of each post in the language in which it was originally published</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>Date of publication in MM/DD/YYYY format</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>Language of the post as detected using the Google Translate API</p> </td> </tr> <tr> <td> <p>Translated Post Description</p> </td> <td> <p>Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts.</p> </td> </tr> <tr> <td> <p>Sentiment</p> </td> <td> <p>Results of sentiment analysis (using translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutral</p> </td> </tr> <tr> <td> <p>Hate</p> </td> <td> <p>Results of hate speech detection (using translated Post Description) where each post was classified as hate or not hate</p> </td> </tr> <tr> <td> <p>Anxiety or Stress</p> </td> <td> <p>Results of anxiety or stress detection (using translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.</p> </td> </tr> </tbody> </table>
Block Copolymer-Assembled Nanopores Enable Ultra-Sensitive Label-Free DNA Detection
<p>Relevant datasets for scientific manuscript: </p> <p><strong>Block Copolymer-Assembled Nanopores Enable Ultra-Sensitive Label-Free DNA Detection </strong></p> <p>Maximiliano Jesus Jara Fornerod <span>a</span>, Alberto Alvarez-Fernandez <span>a</span>, Mate Furedi <span>a</span>, Anandapadmanabhan A Rajendran <span>b</span>, Beatriz Prieto-Simon <span>c,d</span>, Nicolas H. Voelcker <span>e,f*</span>, Stefan Guldin <span>a,g*</span></p> <p> </p> <p><em>a Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK. </em></p> <p><em>b Department of Electronic Engineering, Universitat Rovira i Virgili, 43007, Tarragona, Spain </em></p> <p><em>c Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Av. Països Catalans, 16, 43007, Tarragona, Spain </em></p> <p><em>d ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain </em></p> <p><em>e Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia </em></p> <p><em>f Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, Clayton, Victoria, 3168, Australia </em></p> <p><em>g Technical University of Munich, Department of Life Science Engineering, Gregor-Mendel-Straße 4, 85354 Freising, Germany </em></p> <p>*corresponding authors. E-mail addresses: nicolas.voelcker@monash.edu, s.guldin@ucl.ac.uk, guldin@tum.de</p>
Dataset from TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]
<p>TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]</p> <p>Pre-publicatoin upload for VLDB review.</p> <p>Code is at https://github.com/RelationalAI/annotated-tables/. The Github repository name is from a previous draft version and cannot be changed. It is code for TableLabler.</p>
Dataset for Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor
<p>This is a dataset for a paper "Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor". All details about the data are included in the readme file.</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.