Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Training and test dataset of STED images of microtubules in fixed cells

Training and test dataset of microtubule used in the manuscript "Denoising diffusion models for high-resolution microscopy image restoration".

opencc-by-4.0Mar 2022View details →

Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines

This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo. File contents: scan_001_RT27_001_ROI_converted_decon_ch00.tif     barcode 27, fiducial     scan_001_RT27_001_ROI_converted_decon_ch01.tif     barcode 27 scan_001_RT29_001_ROI_converted_decon_ch00.tif     barcode 29, fiducial           scan_001_RT29_001_ROI_converted_decon_ch01.tif     barcode 29      scan_001_RT37_001_ROI_converted_decon_ch00.tif     barcode 37, fiducial              scan_001_RT37_001_ROI_converted_decon_ch01.tif     barcode 37        scan_006_DAPI_001_ROI_converted_decon_ch00.tif     DAPI      scan_006_DAPI_001_ROI_converted_decon_ch01.tif     DAPI, fiducial         scan_006_DAPI_001_ROI_converted_decon_ch02.tif     RNA   To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.

opencc-by-4.0Jun 2022View details →

Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol

Image 1.tif is a Brain slice. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.

openmit-licenseJun 2021View details →

Testing whole slide image for OpenPhi - Open Pathology Interface

An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.

opencc-by-4.0Jan 2021View details →

1QIsaa data collection (binarized images, feature files, and plotting scripts) for writer identification test using artificial intelligence and image-based pattern recognition techniques

The Great Isaiah Scroll (1QIsaa) data set for writer identification This data set is collected for the ERC project: The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls PI: Mladen Popović Grant agreement ID: 640497 Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a> Copyright (c)     University of Groningen, 2021. All rights reserved. Disclaimer and copyright notice for all data contained on this .tar.gz file: 1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes. 2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed. 3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data. 4) the user should refer to the first public article on this data set: Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476. BibTeX: <pre>@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }</pre> 5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy. Organisation of the data: The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions. The 'images' directory contains NetPBM images of the columns of 1QIsaa. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (arxiv.org/abs/1911.07930) system, and the other one is the manually cleaned version of the binarized output.   The file names for the direct binarized output are of the format '1QIsaa_col<columnnr>.pbm', for example, '1QIsaa_col15.pbm'. And, for the cleaned version, the format is '1QIsaa_col<columnnr>_cleaned.pbm', for example, '1QIsaa_col15_cleaned.pbm'. Note: the image files are not in a separate directory; they will be extracted in the same place. However, due to the unique naming, there is no problem extracting them in one single directory. The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use. The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal. Brief description: According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'.  These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way. Tools: Binarization: The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl) Image Morphing: In the original article, data augmentation was performed using image morphing. The tool is available on GitHub: https://github.com/GrHound/imagemorph.c Features for writer identification: Lambert Schomaker http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html 1. L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798. 2. Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features,  IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.   The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl) If you have any question, please contact us: Maruf A. Dhali <m.a.dhali(at)rug.nl> Lambert Schomaker <l.r.b.schomaker(at)rug.nl> Mladen Popović <m.popovic(at)rug.nl> Please cite our papers if you use this data set: 1. Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476. 2. Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.

opencc-by-4.0Jun 2021View details →

Cu dataset – A copper ore labeled images dataset for segmentation training and testing

This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255). The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished. Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images. Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012). This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights. For further questions and suggestions, please do not hesitate to contact us.   Contact email: ogomes@gmail.com   If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566   Please also cite this paper, which provides additional details about the dataset: Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images. Minerals Engineering, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.

opencc-by-4.0Mar 2022View details →

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.

opencc-by-4.0Sep 2022View details →

hiPSC 3D immunofluorescence images, test data set 2x2, 10Z

Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a small subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal 3 Channels were imaged: - C01: DAPI, nuclear stain - C02: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog - C03: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1   This dataset contains 10 Z levels for 4 field of views for those 3 channels, as well as (manually adjusted) metadata files from the Yokogawa CV7000.   The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless).

opencc-by-nc-4.0Jun 2024View details →

Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces

Data spreadsheets, templates, and supporting image data associated to the article "Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces", forming part of the doctoral thesis of Jan Dariusz Cutajar (University of Oslo). The following file types and files are included: Templates <ul> <li>Template_dE00 calculation.xlsx</li> <li>Template_Skewness calculation.xlsx</li> </ul> Metric calculation tables <ul> <li>Metrics_CIELAB plots.xlsx</li> <li>Metrics_dE00 plots.xlsx</li> <li>Metrics_FTIR plots.xlsx</li> <li>Metrics_Gloss plots.xlsx</li> <li>Metrics_HSI (NDI) plots.xlsx</li> <li>Metrics_HSI (Supervised) plots.xlsx</li> <li>Metrics_HSI (Unsupervised) plots.xlsx</li> <li>Metrics_SEM-EDX particle count plots.xlsx</li> <li>Metrics_Skewness plots.xlsx</li> </ul> Extended result tables <ul> <li>Free liquid trial assessments.xlsx</li> <li>Cleaning score tables.xlsx</li> </ul> Processed images from photography, microscopy, FTIR and SEM <ul> <li>Processed images (VIS, FTIR, SEM).pdf</li> </ul>

opencc-by-4.0Aug 2017View details →

Aegean v2.0 Simulated Test Image

A radio sky image simulated for the purposes of testing v2.0 of the Aegean source finding algorithm.

opencc-by-nc-sa-4.0Feb 2019View details →

3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images

3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT). It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers. The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known. All the images are of blocky 3D shapes floating in space in front of a black background. Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a> The dataset consists of: <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> (Training Dataset) <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> (Test Dataset) <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]. Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> (Validation Dataset) <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul>   Important: Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "unxz 3diqtt-v2-train.h5.xz" on Unix machines. If you use this dataset, please cite it.

opencc-by-4.0Nov 2022View details →

OME-Zarr hiPSC 3D immunofluorescence images, tiny test set

This dataset is intended to be used for automated testing of OME-Zarr processing.   Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for the <a href="https://fractal-analytics-platform.github.io/">Fractal platform</a> and others experimenting with OME-Zarrs. 1 Channel is included: <ul> <li>C01: DAPI, nuclear stain</li> </ul> It is generated from this raw data: <a href="../records/8287221">https://zenodo.org/records/8287221</a> This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000. The data was acquired in the Pelkmans lab in August 2020. The images have been processed using Fractal, the workflow is attached as a json file. It ran with fractal-server==2.3.6, fractal-client==2.0.1, fractal-web==1.4.0 and fractal-tasks-core==1.2.1. Two versions of the OME-Zarr are added here: A 3D version with both Z planes. And a 2D version (MIP of the Z-planes) which also contains label images from cellpose segmentation, measurements and output ROI tables.

opencc-by-4.0Jun 2021View details →

FeM dataset – An iron ore labeled images dataset for segmentation training and testing

This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255). The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished. Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images. Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012). This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights. For further questions and suggestions, please do not hesitate to contact us.   Contact email: ogomes@gmail.com   If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700   Please also cite this paper, which provides additional details about the dataset: Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images. Minerals Engineering, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.

opencc-by-4.0Jan 2023View details →

Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.

This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA.  All data is from 2015-2021 The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format.  All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022). The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays.  All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays.  All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).

opencc-by-4.0Sep 2022View details →

hiPSC 3D immunofluorescence images, tiny test set

Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal It is a subset of this dataset: https://zenodo.org/record/7057076 1 Channel is included: - C01: DAPI, nuclear stain   This dataset also contains a small Fractal workflow for standard processing   This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000.   The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless).

opencc-by-4.0Nov 2023View details →

Hyperspectral imager acquisitions from SMART Soils Test Bed, 2022-04-18

Hyperpsectral imager data retrieved over the Lawrence Berkeley National Lab SMART Soils Test Bed on 2022-04-18 at three times (11:20, 12:34, 13:44). Radiance data (mW cm−2 μm−1 sr−1) subset to bands of interest over the Headwall Hyperspec Imager's 680-800nm range (680-682, 757-652, 769-772, and 778-780 nm) to retrieve RED, NIR, NDVI and SIF while minimizing file size. Data associated with Ruehr et al. 2023, 'Quantifying seasonal and diurnal cycles of solar-induced fluorescence with a novel hyperspectral imager,' submitted to Geophysical Research Letters in November 2023. Code for processing these data and descriptions of the files are available at https://github.com/sruehr/SIFretrieval.

opencc-by-4.0Mar 2024View details →

Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies. The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) 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 546 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 of 28.5 km * 18.5 km and features binary road labels. 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. The structure of the information shared in this repository is as follows: 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. The material is distributed under a CC-BY 4.0 license.

opencc-by-4.0Jan 2019View details →

→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera

→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.

opencc-by-4.0Oct 2021View details →

Annotated images from yeast cell lifespans - Training & Test sets - DetecDiv (id01)

This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~250 000 images from 250 cellular lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 250 timeseries of classes corresponding to the lifespan of the 200 cells.   The dataset used for training (200 cellular lifespans) is in the folder /trainingset, while the dataset used for testing (50cellular lifespans) is in the folder /testset It has been used to train the network <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a> <a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a>   Data type: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) Microscopy data type: Brightfield images with 3 stacks, each stack representing a color of RGB. Imaging: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS Cell type: Budding yeast wild type cell (BY4742) File format: .tif (16-bit RGB, 1 color per z-stack) + .mat Image size: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)   Author(s): Théo, ASPERT Contact email: theo.aspert@gmail.com Affiliation: IGBMC, Université de Strasbourg Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.