Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

opencc-by-4.0Sep 2020View details →

Data for: Physics-based Reconstruction Methods for Magnetic Resonance Imaging

Magnetic Resonance Imaging measurement data used in our paper about 'Physics-based Reconstruction Methods for Magnetic Resonance Imaging' (DOI: 10.1098/rsta.2020.0196). (In version 2 the IR-FLASH data set was replaced with one which is from the same volunteer and slice as the ME-SE data set.)  The data is acquired from healthy volunteers and stored in the format of the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>). The acquisition parameters are shown in the following table: flip angle[◦] TR/TE/ Delta TE[ms] bandwidth [Hz/px] matrix spokes TA[s] FOV[mm] slice[mm] IR-FLASH 6 4.10/2.58 630 256 × 256 1020 4 192 5 ME-SE 90/180 2500/9.9/9.9 390 256 × 256 25 × 16 80 192 3 ME-FLASH 5 10.60/1.37/1.34 960 200× 200 33 × 7 0.35a 320 5 PC-FLASH 10 4.46/2.96 1250 210 × 210 2 × 7 15 320 5 fmSSFPb 15 4.5/2.25 840 192× 192 4 × 101 × 40 137 192 1

opencc-by-4.0Dec 2020View details →

Spatially Resolved Infrared Radiofluorescence (SR IR-RF) Image Data

This dataset contains measurement sequences and data output  of spatially resolved infrared radiofluorescence (SR IR-RF) measurements on K-feldspar samples carried out at the IRAMAT-CRP2A, UMR 5060, CNRS-Université Bordeaux Montaigne (France) in 2019. The data analysis was performed in 2020.  The data may serve as reference data and allow detailed inspection by others to  verify or advance the used analysis procedures.  Along with the raw image data (TIF-files), the datasets also contain documented R scripts used for data processing and partly treated data as an example. To reproduce the full data analysis, additional software is needed; not part of this repository.  Further details can be found in the README.md (README.html), which is part of the dataset.

opencc-by-4.0Nov 2020View details →

Autofluorescence-Free In Vivo Imaging Using Polymer-Stabilized Nd3+-Doped YAG Nanocrystals

Neodymium-doped yttrium aluminum garnet (YAG:Nd3+) has been widely developed during roughly the last sixty years and has been an outstanding fluorescent material. It has been considered as the gold standard among multipurpose solid-state lasers. Yet, the successful downsizing of this system into the nano regimen has been elusive, so far. Indeed, the synthesis of a garnet structure at the nanoscale, with enough crystalline quality for optical applications was found to be quite challenging. Here, we present an improved solvothermal synthesis method producing YAG:Nd3+ nanocrystals of remarkably good structural quality. Adequate surface functionalization using asymmetric double-hydrophilic block copolymers, constituted of a metal-binding block and a neutral water soluble block, provides stabilized YAG:Nd3+ nanocrystals with a long term colloidal stability in aqueous suspensions. These newly stabilized nanoprobes keep the spectroscopic quality (long lifetimes, narrow emission lines, and large Stokes shift) characteristic of bulk YAG:Nd3+. The narrow emission lines of YAG:Nd3+ nanocrystals are exploited by differential infrared fluorescence imaging, thus achieving an autofluorescence-free in vivo readout. In addition, nanothermometry measurements, based on the ratiometric fluorescence of the stabilized YAG:Nd3+ nanocrystals, are demonstrated. The progress here reported paves the way for the implementation of this new stabilized YAG:Nd3+ system in the preclinical arena.

Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data

The dataset contains raw imaging data from the work: "Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis" The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de). Authors Lotta Hof1*, Till Moreth1*, Michael Koch1, Tim Liebisch2, Marina Kurtz3, Julia Tarnick4, Susanna M. Lissek5, Monique M.A. Verstegen6, Luc J.W. van der Laan6, Meritxell Huch7, Franziska Matthäus2, Ernst H.K. Stelzer1, Francesco Pampaloni1§ 1Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 2Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 3Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany 4Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom 5Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany 6Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands 7The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany *contributed equally §corresponding author: fpampalo@bio.uni-frankfurt.de Abstract Background Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days. Results We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures. Conclusion Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.

Image captioning dataset for human activities

An image captioning dataset including images of humans performing various activities. The included images include the following activities: <code>walking, running, sleeping, swimming, sitting, jumping, riding, climbing, drinking and reading.</code>

opencc-by-4.0Jul 2020View details →

X-ray diffraction images for PDB 6Z5G: The RSL - sulfonato-calix[8]arene complex, I23 form, citrate pH 4.0, solved by S-SAD

Anomalous diffraction data collected at 5.975 KeV at Swiss Light Source beam line X06DA using a Pilatus 2M-F detector.

Mock Image Cube

A mock image cube made from the ALMA logo, for testing purposes with radio interferometers. Total flux is comparable to a bright protoplanetary disk in 12CO J=2-1.

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-4.0Jul 2020View details →

Base images for the article "Optimization of a frosting process for soda lime silicate glass based on phosphoric acid"

Raw dataset of the optimization of a frosting process for soda lime silicate glass based on phosphoric acid. Naming scheme: <ul> <li>Images starting with HGr are frosted using the industrial process. These files represent the reference frosting.</li> <li>Images starting with HG are frosted manually following the industrial process.</li> <li>In all other images, the solution concentrations within the preliminary bath are noted als follows: <ul> <li>[cH3PO4]-[cNH4HF2]_[specimen]_[position].jpg</li> <li>For example 10-2_e_1.jpg: This specimen was treated with a preliminary bath with 10 M-% H3PO4 and 20 g/L NH4HF2. It originates from the fith specimen (e) and is the first image of this series.</li> </ul> </li> </ul>

Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.

Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX

Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.

Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX

Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.

Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX

Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.

Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX

Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.

Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX

Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.

Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX

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-sa-4.0Oct 2014View details →

Mouse olfactory bulb intrinsic signal imaging for NNMF decomposition

This dataset supplements our recent paper about automatic image segmentation using Non-negative Matrix Factorisation (NMF): Jan Soelter, Jan Schumacher, Hartwig Spors, Michael Schmuker (2014). Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization. NeuroImage 98:279-288. http://dx.doi.org/10.1016/j.neuroimage.2014.04.041

opencc-zeroOct 2015View details →

Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"

Abstract Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies. We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy. Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species. The outcomes of this project include a library of feature extraction algorithms called ImgPheno, a collection of scripts for neural network training called NBClassify, a library for evolutionary optimization of artificial neural network construction called AI::FANN::Evolving and a planned web application called OrchID for identification of user-submitted images. All project outcomes are open source and freely available. About this release This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.