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.
1,487
datasets available to search
ShareScore release 0.9.0
Dataset results
1,487 results for “Tagging”
Animal lifestyle affects acceptable mass limits for attached tags
Open the record for dataset details and reuse information.
Foraging behavior of tagged rock ants (Temnothorax rugatulus)
Open the record for dataset details and reuse information.
Steelhead passage at a mid-sized dam in California assessed using PIT-tags
Open the record for dataset details and reuse information.
Tandem Tag Assay Optimized for Semi-automated in vivo Autophagic Activity Measurement in Arabidopsis thaliana roots
<p>This is demo data set for AuTToFlux, the semi-automated assay for <em>in vivo</em> autophagic activity measurement in <em>Arabidopsis thaliana</em> roots.</p> <p>The protocol and software required for the assay are available here: <a href="https://github.com/jonasoh/AuTToFlux">https://github.com/jonasoh/AuTToFlux</a></p>
Contextual Tags for music auto-tagging
<p>The dataset is composed of 15 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, chill, club, dance, gym, happy, night, party, relax, running, sad, sleep, summer, work, workout". For each track one or multiple tags are associated with it indicating that users listen to the track in the associated context. </p> <p>The creation of the dataset and the initial baseline of an auto-tagging model is described in the paper: Ibrahim, Karim M., Jimena Royo-Letelier, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC." <em>2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em>. IEEE, 2020.</p> <p>The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: <a href="https://developers.deezer.com/api">https://developers.deezer.com/api</a> </p> <p>.</p>
Tagged original datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged, non-formatted, original datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems', T. Murovič, A. Trost.</p> <p>Available from the original authors:</p> <p>1. <a href="https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/">https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/</a> (UNWS-NB15 dataset)</p> <p>2. <a href="https://www.unb.ca/cic/datasets/nsl.html">https://www.unb.ca/cic/datasets/nsl.html</a> (NSL-KDD dataset)</p>
A genomic data set of single‐nucleotide polymorphisms (SNPs) generated by ddRAD tag sequencing in Q. petraea (Matt.) Liebl. populations from Central-Eastern Europe and Balkan Peninsula
<p>This genomic dataset provides highly variable single-nucleotide polymorphism (SNP) markers from georeferenced natural <em>Quercus petraea</em> (Matt.) Liebl. populations collected in Bulgaria, Hungary, Romania, Serbia, Bosnia and Herzegovina, Kosovo and Albania. These SNP loci can be used to assess genetic diversity, differentiation, population structure, and can also be used to detect signatures of selection and local adaptation.</p>
Interactive Tagging Networks (Following/Followers and Tags on 1 million Twitter Users)
<p><strong>Abstract</strong> (our paper)</p> <p>How do users behave if they can tag each other in social networks? In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems.</p> <p><strong>Data</strong></p> <p>twitter.seed.users:<br> The first column is the user id, and the second column is the json of the user objects on Twitter. This is the set of 1 million seed users to collect the following data.</p> <p>twitter.tagging.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id.</p> <p>twitter.tagging-out-going-from-seed-users.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id. This is only the out-going edges from the seed users, <em>i.e.</em>, this is a subset of twitter.tagging.network.</p> <p>twitter.following.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id).</p> <p>twitter.following-closed-seed-users.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id). This is not used in the following publication paper, but will be useful in other studies.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Patterns in Interactive Tagging Networks. <em>Proceedings of the Ninth International AAAI Conference on Web and Social Media (ICWSM-15)</em>. pp.513-522, 2015.<br> http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10556</p> <p><strong>Code</strong></p> <p>Our code outputting experiment results made available at:<br> https://github.com/yamaguchiyuto/icwsm15</p>
The Annotated Corpus of Classical Tibetan (ACTib), Part I - Segmented version, based on the BDRC digitised text collection, tagged with the Memory-Based Tagger from TiMBL.
<p>This corpus is a part-of-speech tagged version of</p> <p>Wallman, Jeff, Rowinski, Zach, Ngawang Trinley, Tomlinson, Chris, & Keutzer, Kurt. (2017). Collection of Tibetan etexts compiled by the Buddhist Digital Resource Center [Data set]. Zenodo. http://doi.org/10.5281/zenodo.821218</p> <p>using the training data of</p> <p>Hill, Nathan W., & Garrett, Edward. (2017). A part-of-speech (POS) tagged corpus of Classical Tibetan [Data set]. Zenodo. http://doi.org/10.5281/zenodo.574878</p> <p>using the memory based tagger of</p> <p>https://languagemachines.github.io/mbt/</p> <p>Please note that the files are not post-processed or manually corrected and that a small number of files in the KarmaDelek directory were still annotated, although the original xml-input was corrupted already.</p>
The Annotated Corpus of Classical Tibetan (ACTib), Part II - POS-tagged version, based on the BDRC digitised text collection, tagged with the Memory-Based Tagger from TiMBL
<p>This corpus is a part-of-speech tagged version of</p> <p>Wallman, Jeff, Rowinski, Zach, Ngawang Trinley, Tomlinson, Chris, & Keutzer, Kurt. (2017). Collection of Tibetan etexts compiled by the Buddhist Digital Resource Center [Data set]. Zenodo. http://doi.org/10.5281/zenodo.821218</p> <p>using the training data of</p> <p>Hill, Nathan W., & Garrett, Edward. (2017). A part-of-speech (POS) tagged corpus of Classical Tibetan [Data set]. Zenodo. http://doi.org/10.5281/zenodo.574878</p> <p>Please note that the files are not post-processed or manually corrected and that a small number of files in the KarmaDelek directory were still annotated, although the original xml-input was corrupted already.</p> <p> </p> <p>using the memory based tagger of</p> <p>https://languagemachines.github.io/mbt/</p>
Trypanosoma brucei bloodstream form tagging: Targeted subcellular protein localisation.
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen Trypanosoma brucei by endogenous tagging with mNeonGreen (mNG). This master deposition includes a summary of the localisations, primer sequences and DOI indexing, provided in a directory structure analogous to the TrypTag genome-wide procyclic form project: <a href="https://doi.org/10.5281/zenodo.6862298">https://doi.org/10.5281/zenodo.6862298</a> It does not include any microscopy data, which are spread over multiple Zenodo DOIs. Instead, this deposition and the raw and processed data directories include an index to each DOI.</p> <p><strong>localisations.tsv</strong><br> Tab-delimited table, which can be opened in Excel, of localisation annotations for each gene tagged. Also includes primer sequences used, the 96 well plate in which tagging was carried out organised with one row per gene ID, with sets of columns for N and C terminal tagging.</p> <p><strong>geneselection.tsv</strong><br> Tab-delimited table of criteria used for gene selection for tagging. This includes presence/absence of a <em>Leishmania major </em>or <em>Trypanosoma cruzi </em>ortholog, localisation and signal intensity by procyclic form tagging (TrypTag) and upregulation at mRNA level.</p> <p><strong>id_doi_index.tsv</strong><br> Tab-delimited table listing all Trypanosoma brucei Lister 427 gene IDs, if tagging was attempted at the N or C terminus and, if so, the Zenodo DOI at which to find the microscopy data. To download data for a particular gene ID, find its entry in this table, go to the corresponding Zenodo DOI and download <plateid_date>.zip for the raw microscopy data or <plateid_date>_processed.zip for the processed microscopy data. In the latter, images are named by gene ID and tagged terminus.</p> <p><strong>plate_doi_index.tsv</strong><br> Tab-delimited table listing all 96 plates which were part of the targeted bloodstream form tagging project and the Zenodo DOI at which the data can be found. Downloading the data from all of these Zenodo DOIs gives the full microscopy dataset.</p> <p><strong>trypTag_BSF_master.zip</strong><br> Zip file containing the master directory structure for the targeted bloodstream form tagging project database. This contains all internal code which was used to build the bloodstream form tagging database from the raw microscopy data.</p> <p><strong>readme.docx</strong><br> Documentation on data access and rebuilding the database using trypTag_BSF_master.zip</p>
Tracking movements in an endangered capercaillie population using DNA-tagging
<p>Knowing the location and movements of individuals at various temporal and spatial scales is an important facet of behavior and ecology. In threatened populations, movements that would ensure gene flow and population viability are often challenged by habitat fragmentation. Also in those endangered populations capturing and handling individuals to tag them, or to obtain tissue samples, can present additional challenges. DNA tagging, i.e. non-invasive individual identification of samples, can reveal movement patterns. We used fecal material genetically assigned to individuals to indirectly track movements of a large-bodied, endangered forest bird, Cantabrian capercaillie (<em>Tetrao urogallus cantabricus</em>). We wanted to know how the birds were using the fragmented forest landscape, and whether they showed fidelity to display areas. We used multi-event capture-recapture models to estimate fidelity to display areas among three consecutive mating seasons. We identified 127 individuals, and registered movements of 22 females and 48 males. Most observed movements were as expected relatively short, concentrated around display areas. We did not find differences in movement distances between females and males within mating seasons, or between them. Fidelity to display areas among seasons was 0.62 (± 0.12 SE) for females and 0.77 (± 0.07 SE) for males. The best CR model suggested no sex or season effects. Several longer movements, up to 9.9 km, linked distant display areas, demonstrating that Cantabrian capercaillies were able to move between different parts of the study area, complementing previous studies on gene flow. Those longer movements may be taking birds out of the study area, and into historical capercaillie territories, which still include substantial forest cover. The non-invasive DNA tagging approach provided a much larger sample size than would have been feasible with direct tracking. Lack of information on the social status of individuals and timing of movements are some disadvantages of DNA tagging.</p>
Top quark pair events for heavy flavour tagging and vertexing at the LHC
<p>This data contains jets from top anti-top decays. The event and parton shower are simulated in Pythia 8 with a centre of mass energy of 13 TeV, with detector response modelled in the Delphes framework. The detector response is modelled on the ATLAS detector, and a mean pileup of 50 was used.</p> <p>The dataset consists of jets, jet constituents, and truth heavy-flavour hadrons. For each jet, up to 50 charged constituents and 5 truth hadrons are included. Each constituent includes a link to the truth hadron associated, if such a link exists. </p> <p>Provided are 5 files, which are detailed below</p> <ul> <li>class_dict.yaml - Details the relative weights for classification labels, based on the frequency of occurrence for a given entry. Labels are detailed below.</li> <li>norm_dict.yaml - Contains the means and standard deviations of variables that can be used for training, allowing for scaling.</li> <li>pp_output_train.h5 - 13.5 million training jets, consisting of 4.5 million b-jets, c-jets, and light-flavoured jets. Resampling is applied over the jet pT and eta, to ensure equivalent kinematic distributions</li> <li>pp_output_val.h5 - 1.35 million jets for validation, consisting of 450,000 of each jet flavour. Kinematics are resampled in the same way as the training file.</li> <li>pp_output_test_ttbar.h5 - 1.35 million jets for evaluation, consisting of 450,000 of each jet flavour, with no kinematic resampling applied.</li> </ul> <p>Each h5 file contains the following groups:</p> <ul> <li>Jets - (N,) - N jets, including variables such as jet kinematics, flavours, and summary statistics on the number of hadrons and constituents in the jet.</li> <li>Consts (N,50) - Up to 50 charged constituents per jet. Includes details on constituent kinematics and identification. A variable 'valid' is True for tracks in the jet, and False for all other tracks. The additional variable 'truth_hadron_idx' details which hadron (if any) a constituent is associated to.</li> <li>Hadrons (N, 5) - Up to 5 truth heavy-flavour hadrons per jet. Each hadron includes details on kinematics. The variable 'hadron_idx' represents an ID for the hadron, and matches to the constituent variable 'truth_hadron_idx'.</li> </ul> <p>Each group contains both variables that can be used in training, and truth labels, which are as follows:</p> <ul> <li>Jets <ul> <li>flavour - Flavour ID of the jet, 5 for b-jets (containing at least 1 b-hadron within a 0.4 dR(jet, hadron) match), 4 for c-jets (no b-hadrons, and contains at least 1 c-hadron), 0 for light-flavoured jets (contains no b- or c-hadrons)</li> </ul> </li> <li>Consts <ul> <li>truth_hadron_idx - integer that refers to hadron that produced the constituent. '-1' for padded tracks, or tracks with no truth heavy flavour hadron (e.g, pileup, hadronisation).</li> <li>truth_vertex_idx - integer that refers to the vertex a constituent came from - if two tracks have an equivalent truth_vertex_idx, they originate from the same vertex.</li> </ul> </li> <li>Hadrons <ul> <li> <div>hadron_idx - The idx of the hadron, value is '-1' for padded hadrons, 0-4 for remaining hadrons. If a constituent 'truth_hadron_idx' matches with this value, then the constituent came from this heavy flavour hadron decay.</div> </li> <li>flavour - The flavour of the hadron. '5' if the hadron contains at least 1 b-quark, '4' if there is no b-quark but a c-quark is present, '-1' for padded hadrons</li> <li>pt, lxy, dr, mass - The transverse momentum (pt) [GeV], transverse distance between hadronic decay vertex and the primary vertex (lxy) [mm], dR(Jet, Hadron), and the hadron truth mass [GeV]</li> </ul> </li> </ul> <p><span>This dataset allows for studies into algorithms that aim to perform vertex reconstruction.</span></p>
RS3L: A jet tagging dataset for self-supervised learning based on re-simulation
<p>Jet tagging dataset used for "Re-Simulation-based Self-Supervised Learning" (RS3L, <a href="https://arxiv.org/abs/2403.07066">arXiv:2403.07066</a>). Simulated partons are re-showered with various parton shower configurations and reconstructed with the Delphes3 detector software. <br><br>The <code>singletons</code> array contains information about the jet and has dimensions <code>N_examples x N_augmentations x N_singletons</code>. The augmenations are ordered by: <br><br>1. nominal scenario: jet showered with Pythia8<br>2. changing the numerical seed in Pythia8<br>3. changing the scale controlling the probability for final state radiation by 1/sqrt(2)<br>4. changing the scale controlling the probability for final state radiation by sqrt(2)<br>5. using Herwig7 as parton shower</p> <p>The variables stored in the <code>singletons</code> array are:</p> <p><code>singletons = ['jettype','parton1_pt','parton1_eta','parton1_phi','parton1_e','parton2_pt','parton2_eta','parton2_phi','parton2_e', 'jet_pt','jet_eta','jet_phi','jet_e','jet_msd','jet_n2']</code><br><br></p> <p>For gluon-initiated jets, <code>parton1</code> and <code>parton2</code> are the two gluon daughters (see below). For any other jet type, parton1 is the main particle (W, H, Z, or single quark), and parton2 is filled with 0s.</p> <p>The <code>jet_pflow_cands</code> contains the particles clustered into the jet. The array has a shape <code>N_examples x N_augmentations x N_features x N_particles</code>.<br><br>The features per particle are:</p> <p><code>features = ['pt','relpt','eta','phi','dr','e','rele','charge','pdgid','d0','dz']</code><br><br>The <code>jettype</code> gives the parton at the origin of the jet: <br><br><code>jettype: <br>1: q<br>2: c<br>3: b<br>4: H->bb<br>5: g->qq<br>6: g->cc<br>7: g->bb<br>8: g->gg<br>9: W->two quarks<br>10: Z->qq<br>11: Z->bb</code></p> <p> </p>
EpiMapper - CUT&Tag Demo - Required files
Open the record for dataset details and reuse information.
EpiMapper - CUT&Tag FASTQ
Open the record for dataset details and reuse information.
Simple labelled data for WildFi tag lifting experiment
<p>A tiny dataset with ~2000 recordings. We used it to test the WildFi's capabilities of using a classifier to derive simple certain states of the tag from its sensor readings.</p> <div></div>
The Ones That Got Away: Chemical Tagging of Globular Cluster-Origin Stars with Gaia BP/RP Spectra
<p>Catalog of predictions associated with <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240900197K/abstract" target="_blank" rel="noopener">the paper "The Ones That Got Away: Chemical Tagging of Globular Cluster-Origin Stars with Gaia BP/RP Spectra."</a> The columns of the included tables are described in Appendix A.</p> <p>xp-validation_v1.fits is the predictions for the validation dataset (stars with known APOGEE abundances)</p> <p>xp-n-catalog_v1.fits is the catalog of new abundance predictions from the Gaia XP (BP/RP) spectra</p> <p>prediction-variances_v1.fits is the table of variances for each network output across 100 network predictions. The Gaia DR3 source ID is also included and corresponds to a source ID in the xp-n-catalog.</p>
Data and Source codes for: Real-time Radial Tagging for Quantification of Left Ventricular Torsion
<p> </p> <p>Magnetic Resonance Imaging measurement raw data, simulation, and reconstruction codes used in our paper about ‘Real-time Radial Tagging for Quantification of Left Ventricular Torsion' (DOI:10.1002/mrm.29169).</p> <p> </p> <p> </p> <p> </p>
Tagged Corpus of Early English Correspondence Extension Sampler (TCEECES)
<p>The <em>Tagged Corpus of Early English Correspondence Extension Sampler</em> (TCEECES) is the third public release from the 18th-century part of the <em>Corpora of Early English Correspondence</em> (CEEC-400).</p> <p>The TCEECES forms one part of the full CEECES. The other parts are the <a href="https://www.doi.org/10.5281/zenodo.4644243">CEECES part 1</a> (released 1 April 2021), and the <a href="https://www.doi.org/10.5281/zenodo.5887101">CEECES part 2</a> (released 11 April 2022).</p> <p>The TCEECES is an extract from the full <em>Tagged Corpus of Early English Correspondence Extension</em> (TCEECE), which remains unpublished.</p> <p>See the accompanying manual for more information on the TCEECES; see the manuals for the CEECES 1 and the CEECES 2 for more information on the CEECES. See <a href="https://varieng.helsinki.fi/CoRD/corpora/CEEC/">https://varieng.helsinki.fi/CoRD/corpora/CEEC/</a> for more on the CEEC-400.</p> <p>Citation:</p> <p>TCEECES = <em>Tagged Corpus of Early English Correspondence Extension Sampler</em>. Compiled by Terttu Nevalainen, Helena Raumolin-Brunberg, Samuli Kaislaniemi, Mikko Laitinen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Tanja Säily and Anni Sairio at the Department of Languages, University of Helsinki. Spelling standardised by Mikko Hakala, Minna Palander-Collin, Minna Nevala, Emanuela Costea, Anne Kingma and Anna-Lina Wallraff. Annotated by Lassi Saario and Tanja Säily. XML conversion and encoding by Lassi Saario. Helsinki: VARIENG, 2022.</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.