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.
61
datasets available to search
ShareScore release 0.9.0
Dataset results
61 results for “Deep learning classification”
Video classification using deep learning
<p>Material accompanying the paper "Frame-by-frame annotation of video recordings using deep neural networks". Contains a selection of videos used in the paper, manual annotations, and results. Code is contained in the associated GitHub repository. See the readme for details.</p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
Explainable AI for unveiling deep learning pollen classification model - Pollen dataset
<p>Dataset consists automatic particle detector Rapid-E measurements of pollen grains from 12 classes: Acer, Alnus, Alopecurus, Carex, Cupressus, Dactylis, Juglans, Morus, Platanus, Populus, Salix and Ulmus. Data are available i json format.</p> <p>Dataset also contains preprocessed data packed into csv files of 3 modalities: spectrum, lifetime, scattering and additional features from scattering and lifetime data are also available. These are ready to be used with machine learning models. Labels 0, 1, 2, ... 11 correspond to alphabetical order of examined pollen classes Acer, Alnus, Alopecurus ... Ulmus.</p> <p> </p>
A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model
<p>A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model</p>
Dataset: Whole blood count, used in: "AIDeveloper: deep learning image classification in life science and beyond"
<p>Real-time deformability cytometry (RT-DC) data of whole blood measurements.<br> Data was used to train and validate a neural net to perform a blood count based on brightfield images of RT-DC.</p> <p>01_Model: Contains the final model as well as an AIDeveloper meta-file that allows to reproduce the training procedure. The metafile preciesely defines which dataset was used for training and which for validation as well as all parameters that were set in AIDeveloper.</p> <p>The following folders contain data that was used for training (and validation):</p> <ul> <li>Cambr</li> <li>KIK</li> <li>20190306_DextranBlood_AI_DataSet</li> <li>Gs_Blood_Train</li> </ul> <p>Testing data is stored on figshare:<br> https://figshare.com/articles/Krater_et_al_2020_Data_zip/9902636</p>
Microscopy Imaging Dataset: Trypanosoma brucei Bloodstream Form Classification Using Deep Learning
<p>This dataset provides a comprehensive collection of microscopic images and associated labels, specifically designed to facilitate the automated classification of <em>Trypanosoma brucei</em> bloodstream forms—slender and stumpy. Accurate differentiation of these life cycle stages is vital for understanding the parasite's biology, transmission dynamics, and adaptation mechanisms in its mammalian host.</p> <p><strong>Contents:</strong></p> <ul> <li><strong>Image Data</strong>: Microscopic images of <em>T. brucei</em> bloodstream forms captured under standard imaging conditions, encompassing a broad array of image quality, cellular arrangements, and morphological characteristics.</li> <li><strong>Label Data</strong>: Annotation files for each image, specifying cellular forms as slender or stumpy, essential for supervised machine learning applications.</li> <li><strong>Supplementary Files</strong>: Additional Excel files providing information on training, testing, and validation splits, alongside test results for model evaluation.</li> </ul> <p><strong>Purpose:</strong></p> <p>This dataset serves as a valuable resource for researchers in parasitology, machine learning, and computational biology. It supports investigations into the biology and life cycle of <em>T. brucei</em>, while also providing a robust testbed for developing, validating, and benchmarking image processing and classification algorithms tailored to parasite morphology.</p> <p><strong>Data Collection and Methodology:</strong></p> <p>The dataset was compiled using advanced deep learning techniques, integrating the Cellpose segmentation algorithm with a custom-trained Xception model optimized for classifying <em>T. brucei</em> forms. The model achieved 97% classification accuracy, demonstrating effective application in handling complex cell images and distinguishing between slender and stumpy forms in challenging imaging conditions.</p> <p><strong>Usage:</strong></p> <p>Researchers are encouraged to use this dataset to:</p> <ul> <li>Analyze and classify the life cycle stages of <em>T. brucei</em> bloodstream forms in microscopic images.</li> <li>Develop and test deep learning models for single-cell image segmentation and classification.</li> <li>Explore cellular morphology patterns and refine machine learning approaches for other single-cell imaging applications.</li> </ul> <p><strong>Citation:</strong></p> <p>Please cite the original dataset if you utilize this resource in your research to acknowledge its contribution to the field.</p> <p><strong>Access and Availability:</strong></p> <p>This dataset is openly available through Zenodo, enabling researchers to download, explore, and apply it in various fields, from parasitology to advanced computational biology.</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>
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
<p>We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores $\simeq 0.85$ on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction.</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Deep Learning Neural Network Development for the Classification of Bacteriocin Sequences Produced by Lactic Acid Bacteria
<p>This project contains the following underlying data:</p> <h3> Software-Related Files</h3> <p><strong> · </strong><strong>BacLABNet_script.ipynb</strong> (Deep Learning Neural Network for classification of Bacteriocin Sequences) </p> <p><strong> · </strong><strong>embed_proteins.py </strong>(Recurrent Neural Network to obtained the embedding vectors)</p> <p> · <strong> </strong><strong>model_I22.h5</strong> (This file contains the trained weights of the trained model)</p> <p> · <strong>model_I22.json</strong> (This file contains the structure of the trained model)</p> <p><strong> · </strong><strong>rnn_gru.pt</strong> (Initial weights of the Recurrent Neural Network to obtain embedding vectors)</p> <p><strong> · </strong><strong>List_kmers.csv</strong> (List of 5-mers and 7-mers obtained from dataset after it filtered sequences shorter than 50 aa and longer than 2000 aa)</p> <p><strong> </strong></p> <p> <strong>Files Used for Training, Testing, and Validation of the Neural Network</strong></p> <p> <strong>· </strong> <strong>data_nonBacLAB.csv</strong> (25000 nonBacLAB amino acid sequences retrieved from Uniprot)</p> <p> <strong>· data_BacLAB.csv</strong> (24964 BacLAB amino acid sequences retrieved from Uniprot)</p> <p> <strong> </strong></p> <p><strong> Additional Files</strong></p> <p><strong> · data_BacLAB_and_nonBacLAB.csv </strong>(Combination of sequences from data_BacLAB.csv and data_nonBacLAB.csv)</p> <p> <strong> · </strong><strong>all k.mers list.xlsx </strong>(Table of all k-mers obtained for k=3,5,7,15,20)</p> <p> </p> <p><strong>Note:</strong> Codes are additionally available on GitHub. </p> <p><span>Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication)(http://creativecommons.org/publicdomain/zero/1.0/)</span></p>
Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging
<p>This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository.</p> <p>The artifacts amount to the following:</p> <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Acer pseudoplatanus</em> sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Pinus sylvestris </em>sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>
Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies
<p><span>Here we generated the global sand dune pattern map at a resolution of 30 m, named GSDP30. The GSDP30 map encompasses 11 types of sand dune patterns (SDPs): simple crescentic dunes, compound-complex crescentic dunes, simple linear dunes, compound-complex linear dunes, dome dunes, star dunes, parabolic dunes, dendritic dunes, network dunes, sand sheets, and others. The map is divided into 331 Tiff tiles, each characterized by a size of 15,360 × 15,360 pixels and named according to the longitude and latitude coordinates of its upper-left corner.</span></p>
Data for paper "OC_Finder: A deep learning-based software for osteoclast segmentation, classification, and counting"
<pre>This folder contains the source data for the images we used to train the neural network. Tiff files are microscopic images, and each tiff file has two xls or csv files to indicate the location of osteoclasts or non-osteoclasts in each Tiff file. We performed data collection 3 times, and the data from each data collection is stored separately in 3 folders, "Dataset_1", "Dataset_2", "Dataset_3". The "Dataset_1" folder contains a total of 170 tiff images. They are from the cell culture of wild-type cells stimulated with 50 ng/ml RANKL with/without 100 ng/ml TNF-alpha or 10 ng/ml IL-1beta. The location of cells is recorded in xls files. "IMAGE_NAME osteoclasts.xls" is for the coordination of osteoclasts, and "IMAGE_NAME non-osteoclasts.xls" is for the coordination of non-osteoclasts. The x-coordinate is recorded in the "x" column, and the y-coordinate is in the "y" column, with x=0 and y=0 being the coordinates of the upper left corner of the image. Please ignore the columns other than x and y. The "Dataset_2" folder contains a total of 288 images. They are from the cell culture of wild-type cells or cells with gain-of-function mutation of SH3BP2 (KI) stimulated with 25 or 50 ng/ml of RANKL. The image names are composed of the information about the culture conditions, such as gender of cell sources (female and male), the genotype of the cell source (wt: wild-type, KI: Knock-In mutation in Sh3bp2 resulting in the increased osteoclastogenesis), the concentration of RANKL in the culture media (R25: 25 ng/ml of RANKL, R50: 50 ng/ml of RANKL), and the culture period with RANKL stimulation (day 3: cultured with RANKL for 3 days.), excepting the images with the name starting "Image_". The images with the name Image_NUMBER.tif are from the following culture condition; female KI R25. The location of cells is recorded in xls files. "IMAGE_NAME posi.xls" is for the coordination of osteoclasts, and "IMAGE_NAME nega.xls" is for the coordination of non-osteoclasts. The x-coordinate is recorded in the "x" column, and the y-coordinate is in the "y" column, with x=0 and y=0 being the coordinates of the upper left corner of the image. Please ignore the columns other than x and y. The "additional data" folder contains a total of 288 images, which are identical to the images in the "second batch" folder. Using the same images, we collected additional coordination to increase samples. The location of cells is recorded in csv files. "IMAGE_NAME new posi.xls" is for the coordination of osteoclasts, and "IMAGE_NAME new nega.xls" is for the coordination of non-osteoclasts. The x-coordinate is recorded in the "x" column, and the y-coordinate is in the "y" column, with x=0 and y=0 being the coordinates of the upper left corner of the image. Please ignore the columns other than x and y. Please contact Mizuho Kittaka <mkittaka@iu.edu> for the inquery about the dataset.</pre>
Dataset for deep-learning in-situ classification of HIV-1 virion morphology
<p>This dataset contains TEM micrographs for HIV-1 virion samples intended for classification and detection as follows:</p> <ol> <li> <p>HIV-1_virion_classification_backbone_dataset.zip : Contains 1806 .tif images of isolated HIV-1 virions extracted and augmented from TEM micrographs. The images are divided into training (1443 images) and validation (363 images) sets and each of these is divided into eccentric, mature, immature labeled folders:</p> <ul> <li> <p>HIV-1_virion_classification_backbone_dataset/</p> <ul> <li> <p>train/</p> <ul> <li> <p>eccentric/</p> </li> <li> <p>immature/</p> </li> <li> <p>mature/</p> </li> </ul> </li> <li> <p>val/</p> <ul> <li> <p>eccentric/</p> </li> <li> <p>immature/</p> </li> <li> <p>mature/</p> </li> </ul> </li> </ul> </li> </ul> </li> <li> <p>HIV-1_rcnn_dataset_full.zip : Contains 59 .tif TEM micrographs of HIV-1 samples as well as a matching .csv file recording the attributes of each viral instance and coordinates of the rectangular region that contains it:</p> </li> </ol> <ul> <li> <p>region_data_<image_id>.csv:</p> <ul> <li> <p>filename: Name of the image this csv refers to. (Ex: 0131001.png)</p> </li> <li> <p>file_size: Size (bytes) of the image this csv refers to. (Ex: 11755590)</p> </li> <li> <p>file_attributes: Specific attributes of the micrograph. (Ex: None)</p> </li> <li> <p>region_count: Number of viral instances detected in the micrograph. (Ex: 39)</p> </li> <li> <p>region_id: ID of a specific viral region. (Ex: 1)</p> </li> <li> <p>region_shape_attributes: Coordinates of the bounding box of <region_id> that contains a virion. (Ex: {"name":"rect","x":1022,"y":357,"width":225,"height":228})</p> </li> <li> <p>region_attributes: Classification of the virion enclosed in this region (eccentric/mature/immature). (Ex: {"particle_class":"mature"})</p> </li> </ul> </li> </ul> <p>The images are divided into training (46 images) and validation (13 images) sets and each of these contains folder for each micrograph:</p> <ul> <li> <p>HIV-1_rcnn_dataset_full/</p> <ul> <li> <p>train/</p> <ul> <li> <p>0131001/</p> <ul> <li> <p>0131001.png</p> </li> <li> <p>region_data_0131001.csv</p> </li> </ul> </li> <li> <p>0131004/</p> <ul> <li> <p>0131004.png</p> </li> <li> <p>region_data_0131004.csv</p> </li> </ul> </li> <li> <p>…</p> </li> </ul> </li> <li> <p>val/</p> <ul> <li> <p>0131002/</p> <ul> <li> <p>0131002.png</p> </li> <li> <p>region_data_0131002.csv</p> </li> </ul> </li> <li> <p>0131003/</p> <ul> <li> <p>0131003.png</p> </li> <li> <p>region_data_0131003.csv</p> </li> </ul> </li> <li> <p>…</p> </li> </ul> </li> </ul> </li> </ul> <p>The first dataset (HIV-1_virion_classification_backbone_dataset.zip) is intended for viral classification algorithms while the second dataset (HIV-1_rcnn_dataset_full) is intended for detection and segmentation algorithms (for example RCNN).</p> <p>Applications of this dataset as well as source code can be found at <a href="https://github.com/Perilla-lab/TEMNet">https://github.com/Perilla-lab/TEMNet</a> .</p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
Open the record for dataset details and reuse information.
Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers
<p>1: Machine learning-based behaviour classification using acceleration data is a powerful tool in bio-logging research. Deep learning architectures such as convolutional neural networks (CNN), long short-term memory (LSTM), and self-attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration-based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached, and complexity in data due to complex animal-specific behaviours, which may have limited the application of deep learning techniques in this area.</p> <p>2: To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup, and pre-training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state-of-the-art deep learning model architectures.</p> <p>3: Data augmentation improved the overall model performance when one of various techniques (none, scaling, jittering, permutation, time-warping, and rotation) was randomly applied to each data during mini-batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre-training with unlabelled data did not improve model performance. The state-of-the-art deep learning models, including a model consisting of four CNN layers, an LSTM layer, and a multi-head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features.</p> <p>4: Our experiments showed that deep learning techniques are promising for acceleration-based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning, and self-supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time-series sensor data.</p> <p>This abstract is cited from the original article "Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers" in Methods in Ecology and Evolution (Otsuka et al., 2024).<br><br>Please see README for the details of the datasets.</p>
Zircon classification from cathodoluminescence images using deep learning
<p>The dataset contains a total of 4282 original and processed zircon CL images collected from published papers and unpublished personal collections. Igneous, metamorphic, and hydrothermal zircon were classified and labeled manually. The dataset was used for a deep learning classification task which was submitted to "Geoscience Frontiers", and was entitled as "Zircon classification from cathodoluminescence images using deep learning".</p>
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
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.