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548 results for “data classification”
Cosmic Muon Images open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the Cosmic Muon Images demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
GWitchHunters open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the GWitchHunters demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Deep Sea Explorers open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the Deep Sea Explorers demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
Data for "A New Year-Round Weather Regime Classification for North America"
<p>This dataset supports the work presented in <em>"A New Year-Round Weather Regime Classification for North America" </em>by Lee et al. (2023), published in <em>Journal of Climate</em>: <a href="https://doi.org/10.1175/JCLI-D-23-0214.1">https://doi.org/10.1175/JCLI-D-23-0214.1</a> </p> <p>The dataset includes the Z500 climatology and anomalies, the EOFs/PCs, and the parameters required to attribute each day to a regime, alongside the regimes as assigned over 1979-2022 (time series in netCDF and csv formats). In addition, we also supply the standardized projection of each day onto the cluster-mean normalized Z500 anomalies for each regime, termed the Weather Regime Index (WRI).</p>
Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.
The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Library Service Platforms (LSPs) characteristics classification via DELPHI method raw data
<p>The purpose of the research that is related to the dataset is to identify the innovative features of the LSPs (Library Service Platforms) that differentiate them from the LMS (Library Management Systems), as well as to evaluate their importance, based on the opinions of the Greek information scientists. The method used is the Delphi 2-round questionnaire. The dataset contains the results of the Delphi method. Each sheet contains the ranking results from a group of 17 experts from two rounds of the DELPHI method.</p>
Data set of the article: Using Machine Learning for Web Page Classification in Search Engine Optimization
<p>Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"</p> <p>Abstract of the article:</p> <p>This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research. </p>
Automatic plankton image classification - can capsules and filters help coping with data set shift?
<p>This data set is related to the article 'Automatic plankton image classification - can capsules and filters help coping with data set shift?' published in 'Limnology and Oceanography: Methods' by Plonus <em>et al.</em> (2021).</p> <p>The images belong to the trainings set used to train the models in the aforementioned paper (training_) and three different additional data sets which were used to evaluate the performance of the trained models in application mode (fs446_; fs466_; fs534_). The Python-Script 'separate_files.py' can be used to move all the images in different folders for each data set and class respectively.</p>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
Monthly data on caesarean practice using the Ten Group Classification System (2020-2025)
<p>This dataset present summary statistics (numbers of vaginal deliveries and number of caesarean sections according to the Ten groups) that were collected daily from January 2020 to June 2025 in the 32 hospitals participating in the Quali-Dec project. The data were extracted from the hospitals’ birth registers and medical records by a trained health care provider and controlled quarterly by the country data manager. Monthly summary statistics were collected and managed by using REDCap electronic data capture tools hosted at the Karolinska Institute and controlled at a monthly frequency by the principal data manager for pending data and duplicated records. The dataset is composed with 1984 monthly records that have been uploaded to Redcap: 400 from Argentina, 392 from Burkina Faso, 404 from Thailand and 401 from Vietnam. The data collected covered 728,586 deliveries: 392,194 vaginal births and 336,392 caesarean sections.</p>
Joint representation of molecular networks from multiple species improves gene classification - Data
<p>This is the data the accompanies the manuscript <em>Joint representation of molecular networks from multiple species improves gene classification</em></p> <p>Below is the license agreement for each of the publicly available datasets </p> <ul> <li><a href="https://wiki.thebiogrid.org/doku.php/terms_and_conditions">BioGRID</a></li> <li><a href="http://geneontology.org/docs/go-citation-policy/">GO</a></li> <li><a href="https://www.disgenet.org/legal">DisGeNet</a></li> <li><a href="https://monarchinitiative.org/about/licensing">Monarch</a></li> <li><a href="http://eggnog-mapper.embl.de">eggNOG</a></li> </ul> <p>No license agreement was available on <a href="http://imp.princeton.edu">IMP web site</a>, however we have obtained permission from the owner of the material to redistribute the network.</p>
Data from: Supervised classification of plant communities with artificial neural networks
<p>This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:</p><p>Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. <i>Journal of Vegetation Science</i> 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x</p><p>The meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):</p><ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>
Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset
<p>There are four datasets:</p> <p>1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.</p> <p>2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.</p> <p>3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.</p> <p>The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.</p>
Benchmarking eliminative radiomic feature selection for head and neck lymph node classification - Supplemental data
<p>Supplementary files for the publication "Benchmarking eliminative radiomic feature selection for head and neck lymph node classification"</p>
evaluation data for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"
<p>Data contains sound files of mouse vocalization needed to reproduce the evaluation results for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"</p> <p>If you use any of this data, cite the original source: <a href="https://doi.org/10.1016/j.anbehav.2020.09.006">https://doi.org/10.1016/j.anbehav.2020.09.006</a></p>
Extra Testing Data for paper "OC_Finder: A deep learning-based software for osteoclast segmentation, classification, and counting"
<pre>Here we have 9 datasets we used to validate OC_Finder's performance on various imaging settings. The 9 datasets are inside the folder named "9 datasets for validation experiment". Each dataset is composed of image files and csv files for the coordination of osteoclasts and non-osteoclasts that were manually labelled by human examiner. csv files ending "_posi" has coordination of osteoclasts and "_nega" has coordination of non-osteoclasts. Images in dataset #4, #5, #6, #7, #8, and #9 were resized so the scale of the images matched to the OC_Finder's training dataset. Images in original size before resizing are also provided in "Original images before resizing". Detailed capture setting and resizing information of images in each dataset can be found in "capture setting.xlsx". The number of images in each dataset are as following: #1: 18 #2: 18 #3: 18 #4: 36 #5: 36 #6: 36 #7: 16 #8: 16 #9: 16</pre>
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.