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.
598
datasets available to search
ShareScore release 0.9.0
Dataset results
598 results for “classifier”
Produced Data of Naive Bayes Sentiment Classifier
<p>This is the data produced by the running of the Naive Bayes classifier algorithm. It is a list of every word in the vocabulary of the classifier, as well as the number of occurrences of each word, as well as the likelihood ratio of this word. Please note the likelihood ratio is calculated by taking the likelihood of word given a positive label divided by the likelihood of a word given a negative label. This data is licensed under the CC BY 4.0 international license, and may be taken and used freely with credit given. This data was produced by two different datasets, using a Naive Bayes classifier. These datasets were the Polarity Review v2.0 dataset from Cornell, and the Large Movie Review Dataset from Stanford.</p>
Date Fruit classification using a wide range of classifiers
<p>Datasets generated by the techniques RUS and SMOTE. These datasets were used in the paper Date Fruit classification using a wide range of classifiers, accepted for publication in the International Conference on Systems, Signals and Image Processing (IWSSIP) 2023.</p>
Classifying COVID-19 vaccine narratives
<p>We release the augmented Twitter dataset of 355 vaccine-related narratives, created for the following paper. The tweets are labelled as one of four classes: <em>Conspiracy (Cons), Moral, Religious, and Ethical Concerns (MRE), Liberties and Freedom (LF), and Animal Vaccines (AnimalVac)</em>.</p> <pre>@article{li2022classifying, title={Classifying COVID-19 vaccine narratives}, author={Li, Yue and Scarton, Carolina and Song, Xingyi and Bontcheva, Kalina}, journal={arXiv preprint arXiv:2207.08522}, year={2022} }</pre> <p>The paper has been accepted by RANLP 2023.</p> <p> </p> <p> </p>
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), <span>Automated cataloging of oyster toadfish (<em>Opsanus</em> <em>tau</em>) calls using template matching and machine learning</span>. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers. </p>
AimSeg ground truth and classifiers
<p>The data is divided into two distinct datasets: one dedicated to mice undergoing remyelination, known as the validation dataset, and the other focusing on a healthy control specimen. These datasets consist of transmission electron microscopy (TEM) images of the corpus callosum (CC) in adult mice. Both datasets are enriched with annotated ground truth information for two key tasks: instance segmentation (identifying individual myelinated axons) and semantic segmentation (discerning axons, inner cytoplasmic tongue, and myelin). Furthermore, we have incorporated ilastik pixel and object classifiers, specifically trained on remyelinating data, into the repository. To streamline usage, the training dataset has been integrated into the corresponding project file.</p>
Data and codes: Who is calling? Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers
<p>Data and codes that accompany the article titled "Who is calling? Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers".</p>
UVA laser scanning labelled las data over tropical moist forest classified as leaf or wood points
<p>UAV Laser Scanning data collected over neotropical forest (Paracou French Guiana). Four flights conducted over one ha plot in 2021 and 2022.</p> <p>Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction.</p> <p>Predicted values using SOUL model (see ref below) for a single UAV-LS acquisition are provided as separate file.</p> <p> </p>
Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
Open the record for dataset details and reuse information.
Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
Open the record for dataset details and reuse information.
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
Open the record for dataset details and reuse information.
Data from: Koe: Web-based software to classify acoustic units and analyse sequence structure in animal vocalisations
Open the record for dataset details and reuse information.
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
Open the record for dataset details and reuse information.
Statistics and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This publication contains aggregated data for the paper. It also contains the evaluation data of all model/hyper-parameter training and test runs.</p>
Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining
<p>Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomic sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and five site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomic sites have convergent miRNA expression programs, likely reflecting morphologic and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multi-layer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histologic diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.</p>
All Luminosity Templates Associated with "Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates"
<p>Zip files for the luminosity normalized individual stellar templates, and all combinations of SB2 templates. The templates are in fits format. The first table extension contains the template (wavelength, luminosity, variance, error). These luminosity templates have units of erg /s /angstrom.</p>
Modified version of intronIC for classifying U12-type introns in Physarum polycephalum
<p>A modified version of <a href="https://github.com/glarue/intronIC">intronIC</a> with position-specific BPS score weighting to reflect the unique U12-type BPS motif found in <em>Physarum</em>. See the README inside the included directory for instructions on recreating the intron classifications present in the manuscript.</p> <p> </p> <p>IMPORTANT UPDATE (08/2021): There is a typo in the README file within the archive—in order to run the program correctly, the argument flags "-r12" and "-r2" need to be inverted from how they are listed in the example command. In other words, the "-r12" flag needs to be placed where the "-r2" flag is, and vice-versa. Apologies for the error!</p>
Annotated and classified variants from patients with MDS/AML detected in seven public datasets
<p>990 unique validated variants from patients with MDS/AML detected in seven public datasets (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411). Databases and web services were accessed for variant annotation and classification on September 7, 2020.</p>
Classifying Handedness in Chiral Nanomaterials Using Label Noise-Robust Deep Learning
<p>Images of individual Te chiral nanoparticles and labels of their handedness for classifier training.</p>
Training data of protein classifier SolubEcoli.pgc and GDP1.pgc
<p>The classifiers Solub.Ecoli.pgc and GDP1.pgc have been built using Pro-Gyan (https://code.google.com/p/pro-gyan/).</p> <p>The file AGGREGATING.fasta contains chaperone-dependent 502 aggregation prone proteins from <em>E.coli</em>. The file SOLUBLE.fasta contains chaperone-independent 475 soluble proteins from <em>E.coli</em>. The file GROEL_C3.fasta contains GroEL-dependent 83 "class 3" proteins from <em>E.coli</em>. The file SOLUBLE.fasta contains chaperone-independent 475 soluble proteins from <em>E.coli</em>.</p>
Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics
<p>-----------------------------------------------------------------------------------------------------------------</p> <p>Data for "<strong>Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics</strong>" (PLOSONE)</p> <p>Oscar Miguel-Hurtado<sup>1</sup>, Richard Guest<sup>1</sup>, Sarah V. Stevenage<sup>2</sup>,Greg J. Neil<sup>2</sup>,Sue Black<sup>3</sup><br> </p> <ul> <li><sup>1</sup> School of Engineering and Digital Arts, University of Kent, Canterbury, UK</li> <li><sup>2</sup> Department of Psychology, University of Southampton, Southampton, UK</li> <li><sup>3</sup> Centre for Anatomy and Human Identification, University of Dundee, Dundee, UK</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------</p> <p>For more information please contact: O.Miguel-Hurtado-98@kent.ac.uk (Oscar Miguel)</p> <p>-----------------------------------------------------------------------------------------------------------------</p> <p>The zip contains right and left hand geometry images from 112 participants. The images were captured using a Nikon D200 SLR camera (format: jpg, size: 3504x2336 pixels), with both the palm of the hand and camera facing downwards. Participants placed each hand on an acetate sheet with a series of positioning pegs.</p> <p>-----------------------------------------------------------------------------------------------------------------</p> <p>The excel contains a series of length measurements (based on the underlying skeleton of the hand) manually extracted (see Figure 1 for details) along with demographic information from the participants: sex (male or female), height (in cm), weight (in kg) and foot size (in UK sizes).</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.