Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging

Overview This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information: <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> Cell lines and conditions In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions: <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul>   Data and data processing Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the Fbn2 gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb). The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations. Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”. File names are formatted follows. <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.   Structure of Data The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are: <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>

opencc-by-4.0Dec 2021View details →

Four angle fused dataset for Ascidian embryo imaged via light sheet

Original dataset imaged and published here:  <pre>DOI: 10.6084/m9.figshare.8235473.v1</pre> This dataset is provided as Raw dataset for training deep neural networks for segmentation tasks. The binary masks and the integer labels are provided separately.

opencc-by-4.0Dec 2021View details →

AUTH-OpenDR Mixed Image Annotated Dataset for Human-centric Perception Tasks

The dataset was generated through a mixed (real and synthetic) image data generation method which utilizes real background images and DL-generated human models. It contains 50000 real images depicting urban scenes, populated by synthetic human models in various positions and poses and   is suitable for training/evaluating (a) pose estimation, (b) person detection, (c) identity recognition methods. Annotations for 2D bounding boxes of the depicted humans, their  IDs and 2D keypoints etc are provided. The 133 3D human models, required by the method, were generated using the Pixel-aligned Implicit Function (PIFu) and full-body images of people from the Clothing Co-Parsing (CCP) dataset. As background images, a subset of the Cityscapes dataset was used. The Cityscapes license prohibits the distribution of any modified versions of itself. Thus, we provide code  that can re-generate the exact same dataset, given that the Cityscapes dataset is downloaded by the website of its authors. Code and instructions for re-generating the dataset are provided <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation/human_dataset_generation">here</a>. The dataset was developed by Aristotle University of Thessaloniki  (AUTH) within the H2020 OpenDR Project.

openother-openJan 2022View details →

19th Century United States Newspaper images predicted as Photographs with labels for "human", "animal", "human-structure" and "landscape"

The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (<a href="https://chroniclingamerica.loc.gov/">chroniclingamerica.loc.gov/</a>).  <blockquote> [The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project. source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a> </blockquote> One of these categories is 'photographs'. This dataset contains a sample of these images with additional labels indicating if the photograph has one or more of the following labels: "human", "animal", "human-structure" and "landscape" The data is organised as follows: <ul> <li>The images themselves can be found in `images.zip`</li> <li>`newspaper-navigator-sample-metadata.csv` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>`multi_label.csv` contains the labels for the images as a CSV file</li> <li>`annotations.csv` conains the labels for the images with additional metadata</li> </ul> This dataset was created for use in an under-review Programming Historian tutorial (<a href="http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2">http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2</a>) The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material. The data is shared here since it may be useful for others. This data documentation is a work in progress and will be updated when the Programming Historian tutorial is released publicly. The metadata CSV file contains the following columns: - filepath - pub_date - page_seq_num - edition_seq_num - batch - lccn - box - score - ocr - place_of_publication - geographic_coverage - name - publisher - url - page_url - month - year - iiif_url

Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties

This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties". It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output. In this dataset, you will find two types of files: - the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on  the simulated images at different time steps. - the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps. Finally, metadata_simulations.csv gather the information relative to the simulations.

Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"

Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat  15: 791015. https://doi.org/10.3389/fnana.2021.791015 There are 2 files in this upload: 1. The file named "2022_Liu_FrontNeuroanat.pdf" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy. 2. The file named "Liu_data_code.zip" (~1 GB) is a zipped version of a folder ‘Liu_data_code’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.

opencc-by-4.0Jun 2021View details →

Electromagnetic Calorimeter Shower Images of CaloFlow

These are the calorimeter showers that were used to train and evaluate the normalizing flows of "<a href="https://arxiv.org/abs/2106.05285">CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows</a>" and "<a href="https://arxiv.org/abs/2110.11377">CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows</a>". The training and evaluation scripts can be found in <a href="https://gitlab.com/claudius-krause/caloflow">this git repository</a>. The samples were created with the same GEANT4 configuration file as the original CaloGAN samples. Said configuration can be found at the <a href="https://github.com/hep-lbdl/CaloGAN">CaloGAN repository</a>; the original CaloGAN samples are available at <a href="https://doi.org/10.17632/pvn3xc3wy5.1">this DOI</a>. Samples for each particle (eplus, gamma, piplus) are stored in a separate .tar.gz file. Each tarball contains the following files: <ul> <li> train_particle.hdf5: 70,000 events used to train CaloFlow I and II. </li> <li> test_particle.hdf5: 30,000 events used for model selection of CaloFlow I and II. </li> <li> train_cls_particle.hdf5: 60,000 events used to train the evaluation classifier. </li> <li> val_cls_particle.hdf5: 20,000 events used for model selection and calibration of the evaluation classifier. </li> <li> test_cls_particle.hdf5: 20,000 events used for the evaluation run of the evaluation classifier. </li> </ul> Each .hdf5 file has the same structure as the <a href="https://doi.org/10.17632/pvn3xc3wy5.1">original CaloGAN data</a>.

Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy

Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021).  The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a>

opencc-by-4.0Feb 2022View details →

Within Population Variability of Coral Heat Tolerance - Images

Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164.

Scanned images of monocultures and mixtures of six grassland plant species roots, and of simulated fine roots

Soil core samples were taken from a multi-species grassland experiment with field plots of monocultures and mixtures of six grassland plant species: Lolium perenne L. (PRG), Phleum pratense L. (TIM), Trifolium pratense L. (RC), Trifolium repens L. (WC), Cichorium intybus L. (CHIC), and Plantago lanceolata L.. The multi-species plots had a two species mixture with Trifolium repens L. and Lolium perenne L. (PRGWC), and a 6 species mixture with all species mentioned above. The cores were separated into soil depths of 0-10 cm, 10-15 cm and 15-20 cm and the roots separated from the soil. A ground-truth image set was created to simulate fine roots using fishing line. The fishing line used was a clear copolymer monofilament (GreysTM Greylon Tippet Material 3 lb), measured using a scanning electron microscope (Hitachi SU8200) to be 0.14 mm in diameter. The fishing line was used in its clear colour or coloured black using a permanent marker to simulate unstained and stained fine roots respectively. The fishing line was cut into lengths of 30 cm or 5 cm.  Roots and fishing line were scanned using an Epson Perfection V800 flatbed scanner at 600 dpi.  The Roots ZIP file contains a folder for the scanned root images and the Line zip file contains a folder with the scanned fishing line. The excel spreadsheet describes the naming convention for the images. Further details about the root sampling and image acquisition can be found in the publication that analyses these images: <a href="https://doi.org/10.1002/ppj2.20034">https://doi.org/10.1002/ppj2.20034</a>

SIMPA - Example data for realistic photoacoustic images

To visually demonstrate the capabilities of the current version of SIMPA, an image of a human forearm was recorded from a volunteer using the MSOT Acuity Echo. The measurement was conducted within a healthy volunteer study that was approved by the ethics committee of the medical faculty of Heidelberg University under reference number S-451/2020, and the study is registered with the German Clinical Trials Register under reference number DRKS00023205. This dataset can be used to compare simulations based on the given segmentation with the real image.

Paired Sentinel-1 and Sentinel-2 Images for 2 Locations in Scotland and India for 2019 and 2020

The dataset contains two years of coverage (2019 and 2020) for two distant geographical areas in India and in Scotland. If using this dataset, please cite the paper where it has been introduced: <pre><code>@article{rs14061342, author = {Czerkawski, Mikolaj and Upadhyay, Priti and Davison, Christopher and Werkmeister, Astrid and Cardona, Javier and Atkinson, Robert and Michie, Craig and Andonovic, Ivan and Macdonald, Malcolm and Tachtatzis, Christos}, title = {Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery}, journal = {Remote Sensing}, volume = {14}, year = {2022}, number = {6}, article-number = {1342}, url = {https://www.mdpi.com/2072-4292/14/6/1342}, ISSN = {2072-4292}, DOI = {10.3390/rs14061342} }</code></pre>

opencc-zeroNov 2018View details →

A benchmark dataset of herbarium specimen images with label data: Summary

This landing page contains a CSV file compiling all data associated with herbarium specimens that are part of this dataset, as they could be found on GBIF, JACQ or FinBIF. A CSV file with and without Darwin Core extension data is available, as some CSV readers have trouble with the JSON format that is used for those extensions. In addition, DOI's of the individual specimens uploaded to Zenodo and direct links to the different files (JPEG, TIFF, JSON, PNG) are also included. Index of these added variables: - persistentID: Persistent Identifier of the collection specimen. Data uploaded as part of this dataset will not be kept in sync with changes at the collection's repository. Hence, this URI will always point to the most up to date information known about the herbarium specimen. - jpegURL, tiffURL, jsonURL: URL's pointing straight to the respective image and data files themselves, to facilitate (selective) batch downloads. - pngSegAllURL and pngSegSelURL: Segmented overlays of the herbarium specimens indicating the location of different labels and reference material on the sheet ("All") and their content ("Sel"). More information can be found in the paper (in prep) associated with this data publication and the individual depositions themselves. - DOI: The DOI of the deposition of images and data of these specimens on Zenodo. DOI's point to the most up-to-date version of these depositions at the time of the publication of this CSV file. As a rule, this CSV file will be updated should any changes happen to any of the depositions. - jpegURL2, tiffURL2: A few herbarium sheets had labels on the back and consisted therefore of two scans. As a rule, the label scans are in this category.

Control Network for Ganymede Images from JunoCam Perijove 34

Delivered is a photogrammetric control network for four images of Ganymede collected by the Juno- Cam instrument during the Juno mission’s perijove 34 encounter on 2021-06-07. The network has 184,659 control points and 369,318 control measures (2 measures per control point). The network is a PVL formatted text file for use in ISIS (Integrated Software for Imagers and Spectrometers). When control points are merged with an image distance tolerance of 1.0 pixel the resulting network has 83,096 control points and 233,598 control measures. Extracting control points with 4 or more measures yields a network with 16,709 control points and 75,197 control measures. The initial network is produced with novel code implemented in Mathematica. In the “Initial Network” 98% of residuals are less than 1.72 pixels. Median residual is .40 pixels. There are 54 control points with residuals greater than 3 pixels, and 1544 with greater than 2. See ControlNetworkJunoCamV16j.pdf for details.

Supporting Material for "Automatic Mapping of Small Lunar Impact Craters Using LROC NAC Images"

The supporting material for 'Automatic Mapping of Small Lunar Impact Craters Using LROC NAC'. This File contains: <ul> <li>Supporting Material (.pdf);</li> <li>List of True Positive detections (.csv);</li> <li>List of all ground truth and CDA detections (.csv);</li> <li>Folder (.zip) with images of the evaluation sites (.pdf); and</li> <li>Folder (.zip) with training image tiles (.png and .txt).</li> </ul> Refer to Supporting Material (.pdf) for file name and header information.

Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2

Single-cell extracted imaging features created in project "Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2". The dataset includes train (with annotations) and test features used in the manuscript. Four SARS-CoV-2 antigens (S, N, R, M) were imaged separately with serum samples presenting IgG, IgA and IgM antibodies.

Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"

These datasets accompany the article published in Remote Sensing entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing". For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included:  <ul> <li>Raw Metrics: the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li>Rankings: the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.

Zellige example dataset: synthetic image dataset

Phantom 3D image containing three distinct and superimposed synthetic surfaces. It models a typical stack of confocal images of epithelial and non-epithelial structures.The surfaces generated are of two types: “solid” surfaces, presenting a homogeneous signal over the entire surface, or surfaces presenting a signal restricted to a polygonal mesh mimicking the mesh of apical cellular junctions of an epithelium observed at its surface. This dataset contains both the ground-truth height maps and the height maps generated with Zellige. The Zellige parameters used are: \(T_{A}=16, T_{otsu}=12, S_{min}=5, \sigma_{xy}=4, \sigma_{z}=2, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.1, T_{OSE2}=0.1, R_{2}=10, C_{2}=0.8.\) Nota: the ground-truth height maps can be directly compared to Zellige height maps by subtraction. See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau et al. 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a>

Roundabout Aerial Images for Vehicle Detection

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.   <table align="center"> <tbody> <tr> <td> Roundabout (scenes) </td> <td> Frames </td> <td> Car </td> <td> Truck </td> <td> Cycle </td> <td> Bus </td> <td> Empty </td> </tr> <tr> <td> 1 (00001) </td> <td> 1,996 </td> <td> 34,558 </td> <td> 0 </td> <td> 4229 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 2 (00002) </td> <td> 514 </td> <td> 743 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 157 </td> </tr> <tr> <td> 3 (00003-00017) </td> <td> 1,795 </td> <td> 4822 </td> <td> 58 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 4 (00018-00033) </td> <td> 1,027 </td> <td> 6615 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 5 (00034-00049) </td> <td> 1,261 </td> <td> 2248 </td> <td> 0 </td> <td> 550 </td> <td> 0 </td> <td> 81 </td> </tr> <tr> <td> 6 (00050-00052) </td> <td> 5,501 </td> <td> 180,342 </td> <td> 1420 </td> <td> 120 </td> <td> 1376 </td> <td> 0 </td> </tr> <tr> <td> 7 (00053) </td> <td> 2,036 </td> <td> 5,789 </td> <td> 562 </td> <td> 0 </td> <td> 226 </td> <td> 92 </td> </tr> <tr> <td> 8 (00054) </td> <td> 1,344 </td> <td> 1,733 </td> <td> 222 </td> <td> 0 </td> <td> 150 </td> <td> 222 </td> </tr> <tr> <td> Total </td> <td> 15,474 </td> <td> 236,850 </td> <td> 2,262 </td> <td> 4,899 </td> <td> 1,752 </td> <td> 552 </td> </tr> <tr> <td> Data augmentation </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> </tr> <tr> <td> Total </td> <td> 61,896 </td> <td> 947,400 </td> <td> 9048 </td> <td> 19,596 </td> <td> 7,008 </td> <td> 2,208 </td> </tr> </tbody> </table>