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.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “Manually annotated dataset”
An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlines
<p>Here we present a benchmarking dataset of fluorescence microscopy images with EGFP-Galectin-3-stained cells together with annotations of their outlines. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. </p> <p>The dataset contains 60 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging2.</p> <p>For most of the images, nuclear staining and annotations have been published previously in the dataset Aitslab_bioimaging1 (https://doi.org/10.5281/zenodo.6657260). The conversion script to produce the png images from the C01 images was published together with this dataset.</p>
Manually Annotated Drone Imagery (RGB) Dataset for automatic coastline delineation of Southern Baltic Sea, Poland with polyline annotations (0.1.1)
<p><strong>Overview:</strong></p> <p>The Manually Annotated Drone Imagery Dataset (MADRID) consists of hand annotated high resolution RGB images taken in two different types of coasts in Poland, Miedzyzdroje - cliff coast and in Mrzezyno - dune coast in 2022-2023. All images were converted into a uniform format of 1440x2560 pixels, polyline annotated and set into file structure format suited for semantic segmentation tasks (See "Usage" notes below for more details).</p> <p>The raw images of our dataset were captured Zenmuse L1 Sensor (RGB) mounted on a DJI Matrice 300 RTK Drone. Total of 4895 images were captured, however the dataset contains 3876 images with each image annotated with coastline. The dataset only include images with coastlines that are visually identifiable with the human eye. For the annotations of the images, CVAT v2.13 open-source software was utilized.</p> <p><strong>Usage:</strong></p> <p>The compressed RAR file contains two folders train and test. Each folder contains the file that represents the date at which the image was captured in the format of (year, month, day), number of the image and the name of the drone utilized to capture the image. For example, DJI_20220111140051_0051_Zenmuse-L1-mission and DJI_20220111140105_0053_Zenmuse-L1-mission. Additionally, the test folder contains annotations (one per image) which are extracted from the original XML annotation file provided in the CVAT 1.1 image format.</p> <p>Archives were compressed using RAR compression. They can be decompressed in a terminal by opening and extracting Madrid_v0.1_Data.zip.</p> <p>The subset of the data with the name Madrid_subset_data.zip has been added which contains a small portion of train and test images for purpose of inspecting the dataset without downloading the entire dataset.</p> <p>The training images for both training data and testing data are structured as follows.</p> <pre><code>Train/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...<br><br>Test/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...</code></pre> <p> </p>
Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells
<p>This dataset includes open-source projects written in C# programing language, annotated for the presence of Long Method and God Class code smells. Each instance was manually annotated by at least two annotators. We explain our motivation and methodology for creating this dataset in our <a href="https://www.techrxiv.org/articles/preprint/Towards_a_systematic_approach_to_manual_annotation_of_code_smells/14159183/1">preprint</a>:</p> <p>Luburić, N., Prokić, S., Grujić, K.G., Slivka, J., Kovačević, A., Sladić, G. and Vidaković, D., 2021. Towards a systematic approach to manual annotation of code smells. </p> <p>The dataset contains two excel datasheets:</p> <ul> <li><em>DataSet_Large Class.xlsx</em> – C# classes annotated for the Large Class code smell severity.</li> <li><em>DataSet_Long Method.xlsx</em> – C# methods annotated for the Long method code smell severity.</li> </ul> <p> The columns in the datasheet represent:</p> <ul> <li><em>Code Snippet ID</em> – the full name of the code snippet. <ul> <li>For classes, this is the package/namespace name followed by the class name. The full name of inner classes also contains the names of any outer classes (e.g., <em>namespace.subnamespace.outerclass.innerclass</em>).</li> <li>For methods, this is the full name of the class and the methods’s signature (e.g., <em>namespace.class.method(param1Type, param2Type)</em> ).</li> </ul> </li> <li><em>Link </em>– The GitHub link to the code snippet, including the commit and the start and end LOC.</li> <li><em>Code Smell </em>– code smell for which the code snippet is examined (Large Class or Long Method).</li> <li><em>Project Link </em>– the link to the version of the code repository that was annotated.</li> <li><em>Metrics </em>– a list of metrics for the code snippet, calculated by our <a href="https://github.com/Clean-CaDET/platform#readme">platform</a>. Our dataset provides 25 class-level metrics for Large Class detection and 18 method-level metrics for Long Method detection The list of metrics and their definitions is available <a href="https://github.com/Clean-CaDET/platform/blob/c4acff95ec00ff6c25fa62dde4818c1f40e39d39/CodeModel/CaDETModel/CodeItems/CaDETMetrics.cs">here</a>.</li> <li><em>Final annotation </em>– a single severity score calculated by a majority vote. </li> <li><em>Annotators </em>– each annotator's (1, 2, or 3) assigned severity score.</li> </ul> <p>To help guide their reasoning for evaluating the presence and the severity of a code smell, three annotators independently annotated whether the considered heuristics apply to an evaluated code snippet. We provide these results in two separate excel datasheets:</p> <ul> <li><em>LargeClass_Heuristics.xlsx </em>- C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> <li><em>LongMethod_Heuristics.xlsx </em>- C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> </ul> <p>The columns of these two datasheets are:</p> <ul> <li><em>Code Snippet ID </em>- the full name of the code snippet (matching the IDs from <em>DataSet_Large Class.xlsx </em>and <em>DataSet_Long Method.xlsx</em>)</li> <li><em>Annotators</em> – heuristics labelled by each of the annotators (1, 2, or 3).</li> <li><em>Heuristics </em>– whether the heuristic is applicable to the examined code snippet or not (Section 1.2.4 lists heuristics relevant for the Large Class detection, and Section 1.2.5 lists the heuristics relevant for the Long Method detection).</li> </ul>
An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines
<p>Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert.</p> <p>The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A brief article describing the dataset is also available (Arvidsson M, Kazemi Rashed S, Aits S. <a href="https://doi.org/10.1016/j.dib.2022.108769">10.1016/j.dib.2022.108769</a> )</p> <p><strong>Dataset description:</strong></p> <p>Fluorescence microscopy images: original .C01 files and files converted to 8-bit .png format (Grayscale)</p> <p>Annotations: 24-bit .png format (RGB)</p> <p>Script used to convert C01 to png images: C01_to_png.py file with python code and readme.md file with instructions to run it</p>
TweetC19SR-Eng - Manually annotated dataset of English language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
TweetC19SR-Spa - Manually annotated dataset of Spanish language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Manually annotated RNA-focused scientific articles -- Experimental dataset
<p>In this repository, we store 100 <a href="../records/11393776/files/annotations.json?download=1">paragraphs sourced from RNA-focused scientific articles that have been manually annotated</a> (both for entities and relations) according to a <a href="../records/11393776/files/RNA-KG.yaml?download=1">LinkML template</a> that represent associations among genes, proteins, RNAs, chemicals, variants (SNPs), GO terms, and diseases. The template reflects a <a href="../records/11393776/files/meta-graph.pdf?download=1">subportion of RNA-KG's meta-graph</a>. <a href="https://doi.org/10.48550/arXiv.2312.00183">RNA-KG</a> data are available at: <a href="../records/10078876">https://zenodo.org/records/10078876</a>.</p>
Manual 4D annotations of Micro X-ray CT time-series (4D dataset)
<p>The 4D (3D+time) manual annotations of https://doi.org/10.5281/zenodo.4293394. For the annotation the SuRVoS workbench was used (https://doi.org/10.5281/10.5281/zenodo.247547) and our proposed hidden Markov model (HMM-T, https://doi.org/10.5281/zenodo.4416013 ) designed to refine 4D semantic segmentations made by a 3D semantic segmentation CNN after its applied on 4D data. Only slices 740-742 and 747-749 (refining to the first axis) are partially annotated. We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Kludt et al. Cell Reports Medicine - Dataset (manual annotations)
<p>Dataset of patches created from manually annotated whole-slide images of non-small cell lung cancer from the publication:</p> <p>"Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms"</p> <p>in Cell Reports Medicine 2024</p> <p>The pixel-level ground truth information is included (classes.txt).</p> <p><br>The dataset can be used for academic research purposes only.</p> <p> </p> <p>(c) Yuri Tolkach </p>
Stroke - Tasks Walking Total With Manual Annotation Bracelet Dataset
<p>The tasks_walking_total_with_manual_annotations.csv file contains accelerometer data and task labels of the pilot stroke patients that are given in folder datasets/bracelet/stroke and include only walking activities. It includes 11 patients that are manually annotated and consists of 7 columns. Those columns are:</p> <ol> <li> <p>x, y, z, which represent the accelerometer values of the bracelets sensors used on either left or right wrist of the patients</p> </li> <li> <p>T and time, which represent the timestamp of the activity (time) and the period (T).</p> </li> <li> <p>Patient ID column, which is the number id of the patients.</p> </li> <li> <p>Task column, which represents the task performed by the patient.</p> </li> </ol> <p>This file contains the walking tasks that are described below. Inside of each parenthesis, is given the name of each task, based on the annotations that were provided on datasets/annotations/raw_medical_tracking/stroke/<strong>intense-monitoring_with_manual_annotations_v2.xlsx</strong> file and on the accelerometer data that were available on stroke pilot folder mentioned above. Those tasks are:</p> <ol> <li> <ol> <li> <p>normal_walk (Normal walking)</p> </li> <li> <p>tandem_walk (Tandem walking)</p> </li> <li> <p>bicycle_walk (Bycicle walking)</p> </li> <li> <p>walk_with_knees_raised (Walking with the knees raised)</p> </li> </ol> </li> </ol> <p> </p> <p>The features used to recognize walking activities in stroke patients are x,y,z and Task.</p>
TRACES Telegram and Twitter Dataset with Bulgarian Journalists Manual Annotations of True/Untrue and Disinformation/Not and Automatic Annotations for Markers of Lies
<p>TRACES dataset of 4083 Twitter and Telegram posts automatically annotated for markers of lies and manually by Bulgarian journalists for containing true/untrue information and disinformation or not.</p> <p>Each message has been annotated by usually 3 (in under 10 cases by 2 annotators). The annotators came from different media, in order to obtain various views. They were asked to not get biased and were assured that their identities will not be revealed. </p> <p><strong>The dataset is a subset of these other datasets:</strong></p> <p>https://zenodo.org/record/7614247</p> <p>https://zenodo.org/record/7614318</p> <p>https://zenodo.org/record/7614357</p> <p>https://zenodo.org/record/7614294</p> <p> </p> <p><strong>It has been annotated following these Annotation Guidelines:</strong></p> <p>https://zenodo.org/record/7706743</p>
VETO: Vessel topology manual annotation dataset
<p>We have labeled the vessel topology on four public-available retinal datasets:</p> <p>INSPIRE [1]<br>VICAVR [2]<br>IOSTAR [3]<br>DRIVE [4]<br>Two experts were asked to manually label the topological information of the retinal vascular structure by using a graph editing software we developed for this task. Expert one and two independently labeled each vessel segment or centerline for all datasets, based on the types of available manual annotations of the vessel structure. Then the consensus between them was released for public use.</p> <p>It is worth noting that the vessel segments or vessel centerlines were used for topology estimation were extracted either by human grader or automatic vessel segmentation method, i.e. the DRIVE and IOSTAR datasets include the manual annotations of retinal vessel for each image, so the topology reconstruction were performed at manual annotated vessel patterns; for VICAVR datasets, the topology estimation were performed at the automatic segmented vessels by using the automated segmentation method [5]; for INSPIRE dataset, the human expert graded the topology at the vessel centerline which were provided by [6].</p> <p>[1] M. Niemeijer, X. Xu, A. Dumitrescu, B. van Ginneken, J. Folk, and M. Abràmoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med.Imaging, vol. 30, no. 11, pp. 1941–1950, 2011.</p> <p>[2] S. G. Vázquez, B. Cancela, N. Barreira, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl., vol. 24, no. 5, pp. 919– 930, 2013.</p> <p>[3] J. Zhang, B. Dashtbozorg, E. J. Bekkers, J. P. W. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging, vol. 35, pp. 2631–2644, 2016.</p> <p>[4] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, vol. 23, pp. 501–509, 2004.</p> <p>[5] Y. Zhao, L. Rada, K. Chen, , and Y. Zheng, “Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images,” IEEE Trans. Med. Imaging, vol. 34, no. 9, pp. 1797–1807, 2015.</p> <p>[6] R. Estrada, C. Tomasi, S. Schmidler, and S. Farsiu, “Tree topology estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 8, pp. 1688–1701, 2015.</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.