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6,250 results for “Classification”
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
Data from: Crowdsourcing training material for automated bird sound classification – a pilot study
<p>Data from the manuscript "Crowdsourcing training material for automated bird sound classification – a pilot study" by Petteri Lehikoinen, Meeri Rannisto, Ulisses Camargo, Aki Aintila, Patrik Lauha, Esko Piirainen, Panu Somervuo & Otso Ovaskainen</p>
4-way Tabla Stroke Classification with Models Adapted from ADT
<p>Recordings and 4-way stroke category annotations of tabla playing of solo compositions and accompaniment to vocals (tabla recorded in isolation) released with the following conference paper:</p> <p>M. A. Rohit, A. Bhattacharjee, and P. Rao, “Four-way Classification of Tabla Strokes with Models Adapted from Automatic Drum Transcription”, in Proc. of the 22nd Int. Society for Music Information Retrieval Conf., Online, 2021.</p> <p>The dataset is split into test and train sets. The test set consists of 10 pieces of only the tabla accompaniment recorded in perfect isolation to prerecorded solo Hindustani vocal tracks. It contains 20 minutes of audio and nearly 4,500 strokes. These recordings, made on 3 unique tabla sets by 2 different artists, are diverse in terms of tuning, tala (metre), and tempo. The training set consists of solo compositions and common theka patterns recorded from 10 different tabla-sets. The total audio duration is about 1.25 hours and there are 26,600 strokes.</p> <p>The test set was annotated by first running an automatic onset detector to obtain stroke onsets, followed by manually assigning the four-way labels by listening to the audio and visually inspecting the spectrogram. The training set was annotated by automatically aligning the composition score (supplied by artists) with the audios, and replacing the bols with corresponding target stroke categories. Given the imperfect score-stroke matching, labels were manually verified to assign the same category to similar sounding bols.</p> <p>Onsets are separated into folders for each stroke category. Each '.onsets' file in these folders corresponds to an audio ('.wav') file of the same name and is a text file containing a list of time instants where an onset of a particular stroke category occurs.</p>
Sentiment Analysis of RUU PDP with Naive Bayes, Support Vector Machine, and Random Forest Classification Algorithm
<p>Dataset from the results of data crawling via Twitter which discusses the Rancangan Undang Undang Pelindungan Data Pribadi to be used in the sentiment analysis process. The dataset is divided into several parts according to the process executed on RapidMiner.</p>
The Application of Machine Learning for Classification on Blood Pressure Variability. A New Approach for an Old Idea - Professor Kelvin Tsoi (The Chinese University of Hong Kong, School of Public Health and Primary Care)
<p>This video is the eighth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>The Application of Machine Learning for Classification on Blood Pressure Variability. A New Approach for an Old Idea - Professor Kelvin Tsoi (The Chinese University of Hong Kong, School of Public Health and Primary Care)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/liLVKA-JHiI</p>
Enzyme Substrate Classification Dataset for SDRs and SAM-MTases
<p>This dataset contains sequence information, three-dimensional structures (from AlphaFold2 model), and substrate classification labels for 358 short-chain dehydrogenase/reductases (SDRs) and 953 S-adenosylmethionine dependent methyltransferases (SAM-MTases).</p> <p>The aminoacid sequences of these enzymes were obtained from the UniProt Knowledgebase (https://www.uniprot.org). The sets of proteins were obtained by querying using InterPro protein family/domain identifiers corresponding to each family: IPR002347 (SDRs) and IPR029063 (SAM-MTases). The query results were filtered by UniProt annotation score, keeping only those with score above 4-out-of-5, and deduplicated by exact sequence matches.</p> <p>The structures were submitted to the publicly available AlphaFold2 protein structure predictor (J. Jumper et al., Nature, 2021, 596, 583) using the ColabFold notebook (https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.1-premultimer/batch/AlphaFold2_batch.ipynb, M. Mirdita, S. Ovchinnikov, M. Steinegger, Nature Meth., 2022, 19, 679, https://github.com/sokrypton/ColabFold). The model settings used were msa_model = MMSeq2(Uniref+Environmental), num_models = 1, use_amber = False, use_templates = True, do_not_overwrite_results = True. The resulting PDB structures are included as ZIP archives</p> <p>The classification labels were obtained from the substrate and product annotations of the enzyme UniProtKB records. Two approaches were used: substrate clustering based on molecular fingerprints and manual substrate type classification. For the substate clustering, Morgan fingerprints were generated for all enzymatic substrates and products with known structures (excluding cofactors) with radius = 3 using RDKit (https://rdkit.org). The fingerprints were projected onto two-dimensional space using the UMAP algorithm (L. McInnes, J. Healy, 2018, arXiv 1802.03426) and Jaccard metric and clustered using k-means. This procedure generated 9 clusters for SDR substrates and 13 clusters for SAM-MTases. The SMILES representations of the substrates are listed in the SDR_substrates_to_cluster_map_2DIMUMAP.csv and SAM_substrates_to_13clusters_map_2DIMUMAP.csv files.</p> <p><br> The following manually defined classification tasks are included for SDRs: NADP/NAD cofactor classification; phenol substrate, sterol substrate, coenzyme A (CoA) substrate. For SAM-MTases, the manually defined classification tasks are: biopolymer (protein/RNA/DNA) vs. small molecule substrate, phenol subsrates, sterol substrates, nitrogen heterocycle substrates. The SMARTS strings used to define the substrate classes are listed in substructure_search_SMARTS.docx.<br> </p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</p>
Figs 32–34. Nogodinidae, hind wing. 32 in Notes on higher classification of the family Nogodinidae (Hemiptera: Auchenorrhyncha: Fulgoroidea), with description of new tribe and new species
Figs 32–34. Nogodinidae, hind wing. 32 – Forculus gibbosus Distant, male, holotype, Ceylon (BMNH); 33 – Nogodina reticulate (F.), male, Santorem (ZIN); 34 – Bladina sp., Columbia (ZIN).
Figs 1–4 in Notes on higher classification of the family Nogodinidae (Hemiptera: Auchenorrhyncha: Fulgoroidea), with description of new tribe and new species
Figs 1–4. Nogodinidae, rostrum (2, 4) and hind tarsomere (1, 3). 1, 2 – Bladina fawleri Fennah, female, Belize; 3, 4 – Pisacha sp., female, Japan, Ryukyu.
Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers
<p>In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file.</p> <p>Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. </p> <p>Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers.</p> <p>Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. </p> <p>Results and Conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.</p>
Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks
<p>Classification dataset used in Kosiba et al. 2020 (10.1093/mnras/staa1723) consisting of candidate clusters in the XCLASS survey</p> <p>Training and testing images and corresponding labels low-z (clusters, 0<z<0.3), hi-z (clusters, z>0.3), nearby galaxy, point source (point, double source, star/AGN), and other (artefact, edge)</p>
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
A Point Cloud Dataset of Vehicles Passing Through a Toll Station for use in Training Classification Algorithms
<p>This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing, point cloud validation, and vehicle labeling. Additionally, a detailed description of the structure and content of the dataset is provided, along with some potential applications of its use. The dataset consists of 36,026 total object classes: 31,432 cars, campers, vans and 2-axle trucks with a single tire on the rear axle, 452 minibuses with a single tire on the rear axle, 1158 buses, 1179 2-axle small trucks, 797 2-axle large trucks, and 1008 trucks with 3 or more axles. The point clouds were captured using a LiDAR sensor and Doppler effect speed sensors. The dataset can be used to train and evaluate algorithms for range data processing, vehicle classification, vehicle counting, and traffic flow analysis. The dataset can also be used to develop new applications for intelligent transportation systems.</p> <table> <tbody> <tr> <td>Type</td> <td>Description</td> <td>Quantity</td> </tr> <tr> <td>1</td> <td>Cars, campers, vans and 2-axle trucks with<br>a single tire on the rear axle</td> <td>31,432</td> </tr> <tr> <td>2</td> <td>Minibuses with a single tire on the rear axle</td> <td>452</td> </tr> <tr> <td>3</td> <td>Buses</td> <td>1,158</td> </tr> <tr> <td>4</td> <td>Trucks with 3 or more axles</td> <td>1,008</td> </tr> <tr> <td>5</td> <td>2-axle small trucks</td> <td>1,179</td> </tr> <tr> <td>6</td> <td>2-axle large truck</td> <td>797</td> </tr> <tr> <td>Total</td> <td> </td> <td>36,026</td> </tr> </tbody> </table>
Index based dataset for training ML classification models
<p>This dataset contains 202122 rows of data containing 61 unique indices from different world urban areas.</p>
OpenAlex Topic Classification v1 Model Artifacts and Training Data
<p>This is all data used to train the topic classification model and also the model artifacts to deploy the model. Please see the github repo for more information:</p> <p>https://github.com/ourresearch/openalex-topic-classification</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Figure 18 in A new classification of Callianassidae and related families (Crustacea: Decapoda: Axiidea) derived from a molecular phylogeny with morphological support
Figure 18. Diagnostic characters for genera of Eucalliacidae. Posterior carapace, sternite 7, pleopod1, coxa 4, basis of pereopod 5: a, Pseudocalliax. Carapace, eyestalks, antennules, antennae: b, Eucalliax. Telson, uropod: c, Calliaxina; d, Eucalliaxiopsis. Pleomere 6, telson, uropod: e, Paraglypturus; f, Eucalliax. Major cheliped: g, Paraglypturus; h, Eucalliax. Minor cheliped: i, Paraglypturus; j, Calliax; k, Pseudocalliax. Pereopod 3: k, Calliaxina; l, Calliax. Pereopod 4: m, Paraglypturus. Original illustrations: a, Pseudocalliax tooradin NMV J303; e, Calliaxina SA-01, UF 36699; h, Eucalliax quadracuta, Panama, NHMW 25916.
Figure 17 in A new classification of Callianassidae and related families (Crustacea: Decapoda: Axiidea) derived from a molecular phylogeny with morphological support
Figure 17. Diagnostic characters for genera of Ctenochelidae. Major cheliped: a, Ctenocheles; b, Ctenocheloides; c, Kiictenocheloides; d, Gourretia. Minor cheliped: e, Ctenocheloides; f, Kiictenocheloides; g, h, Paragourretia; i, Gourretia. Maxilliped 3: j, Gourretia. Male coxa 5: k, Laurentgourretia. Male pleopods 1, 2: l, m, Laurentgourretia. Original illustrations: k–m, Laurentgourretia rhopalommata, MNHN-IU-2014-11417.
Figure 14 in A new classification of Callianassidae and related families (Crustacea: Decapoda: Axiidea) derived from a molecular phylogeny with morphological support
Figure 14. Diagnostic characters for genera of Callichiridae. Pereopod 3: a, Audacallichirus; b, Karumballichirus; c, Neocallichirus; d, Lepidophthalmus; e, Mucrollichirus. Pleon, telson, uropods: f,Callichirus; g,Grynaminna; h,Michaelcallianassa. Telson, uropod:i,Audacallichirus; j, Balsscallichirus; k, Glypturoides; l, Karumballichirus; m, Lepidophthalmus; n, Mocallichirus; o, Kraussillichirus; p, Neocallichirus. Original illustrations: n, Mocallichirus mocambiquensis, UF 13986; e, Mucrollichirus mucronatus, MNHN-IU-2013-2777.
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.