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87 results for “image-base”
Data from: A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics
<p>Data and results from the Imageomics Workflow. These include data files from the Fish-AIR repository (https://fishair.org/) for purposes of reproducibility and outputs from the application-specific imageomics workflow contained in the Minnow_Segmented_Traits repository (https://github.com/hdr-bgnn/Minnow_Segmented_Traits).</p> <p>Fish-AIR:<br> This is the dataset downloaded from Fish-AIR, filtering for Cyprinidae and the Great Lakes Invasive Network (GLIN) from the Illinois Natural History Survey (INHS) dataset. These files contain information about fish images, fish image quality, and path for downloading the images. The data download ARK ID is dtspz368c00q. (2023-04-05). The following files are unaltered from the Fish-AIR download. We use the following files:</p> <p>extendedImageMetadata.csv: A CSV file containing information about each image file. It has the following columns: ARKID, fileNameAsDelivered, format, createDate, metadataDate, size, width, height, license, publisher, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>imageQualityMetadata.csv: A CSV file containing information about the quality of each image. It has the following columns: ARKID, license, publisher, ownerInstitutionCode, createDate, metadataDate, specimenQuantity, containsScaleBar, containsLabel, accessionNumberValidity, containsBarcode, containsColorBar, nonSpecimenObjects, partsOverlapping, specimenAngle, specimenView, specimenCurved, partsMissing, allPartsVisible, partsFolded, brightness, <br> uniformBackground, onFocus, colorIssue, quality, resourceCreationTechnique. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>multimedia.csv: A CSV file containing information about image downloads. It has the following columns: ARKID, parentARKID, accessURI, createDate, modifyDate, fileNameAsDelivered, format, scientificName, genus, family, batchARKID, batchName, license, source, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>meta.xml: A XML file with the metadata about the column indices and URIs for each file contained in the original downloaded zip file. This file is used in the fish-air.R script to extract the indices for column headers.</p> <p>The outputs from the Minnow_Segmented_Traits workflow are:</p> <p>sampling.df.seg.csv: Table with tallies of the sampling of image data per species during the data cleaning and data analysis. This is used in Table S1 in Balk et al. </p> <p>presence.absence.matrix.csv: The Presence-Absence matrix from segmentation, not cleaned. This is the result of the combined outputs from the presence.json files created by the rule “create_morphological_analysis”. The cleaned version of this matrix is shown as Table S3 in Balk et al.</p> <p>heatmap.avg.blob.png and heatmap.sd.blob.png: Heatmaps of average area of biggest blob per trait (heatmap.avg.blob.png) and standard deviation of area of biggest blob per trait (heatmap.sd.blob.png). These images are also in Figure S3 of Balk et al.</p> <p>minnow.filtered.from.iqm.csv: Filtered fish image data set after filtering (see methods in Balk et al. for filter categories).</p> <p>burress.minnow.sp.filtered.from.iqm.csv: Fish image data set after filtering and selecting species from Burress et al. 2017.</p>
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
1QIsaa data collection (binarized images, feature files, and plotting scripts) for writer identification test using artificial intelligence and image-based pattern recognition techniques
<p><strong>The Great Isaiah Scroll (1QIsa<sup>a</sup>) data set for writer identification</strong></p> <p>This data set is collected for the ERC project:<br> The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br> PI: Mladen Popović<br> Grant agreement ID: 640497</p> <p>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a><br> <br> <strong>Copyright (c) </strong> University of Groningen, 2021. All rights reserved.<br> <strong>Disclaimer and copyright notice for all data contained on this .tar.gz file:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article on this data set:<br> <br> <em>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 (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.</em><br> <br> BibTeX:</p> <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> <p><strong>5) </strong>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.</p> <p><strong>Organisation of the data:</strong></p> <p>The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions.</p> <p>The 'images' directory contains NetPBM images of the columns of 1QIsa<sup>a</sup>. 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 (<em>arxiv.org/abs/1911.07930</em>) 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.</p> <p>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.</p> <p>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.</p> <p><strong>Brief description:</strong><br> 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.</p> <p><strong>Tools:</strong><br> <strong>Binarization:</strong><br> The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl)</p> <p><strong>Image Morphing:</strong><br> In the original article, data augmentation was performed using image morphing. The tool is available on GitHub:<br> https://github.com/GrHound/imagemorph.c</p> <p><strong>Features for writer identification:</strong><br> Lambert Schomaker<br> http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html<br> http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html<br> <em><strong>1. </strong>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.<br> <strong>2. </strong>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.</em><br> <br> 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)</p> <p><strong>If you have any question, please contact us:</strong><br> Maruf A. Dhali <m.a.dhali(at)rug.nl><br> Lambert Schomaker <l.r.b.schomaker(at)rug.nl><br> Mladen Popović <m.popovic(at)rug.nl></p> <p><strong>Please cite our papers if you use this data set:</strong><br> <em><strong>1.</strong> 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 (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.<br> <strong>2. </strong>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.</em></p>
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
<p>Optical coherence tomography (OCT) is a non-invasive imaging technique that has extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology.</p> <p>The dataset consists of the following categories and images:<br>- Age-Related Macular Degeneration - 1231 images;<br>- Diabetic Macular Edema - 147 images;<br>- Epiretinal Membrane- 155 images;<br>- Normal - 332 images;<br>- Retinal Artery Occlusion - 22 images;<br>- Retinal Vein Occlusion - 101 images;<br>- Vitreomacular Interface Disease - 76 images.</p> <p>This dataset is published to provide researchers and developers with access to a large set of labeled images, which contributes to the development and improvement of algorithms for the automatic processing and analysis of OCT images for early diagnosis and monitoring of eye diseases. CSV file consists of file_name, disease, subcategory, condition, patient_id, eye, sex, year, image_width, and image_height. The dataset will be updated periodically.</p> <p> </p> <p>For more information and details about the dataset see:</p> <p>https://rdcu.be/dELrE</p> <p>https://arxiv.org/abs/2312.08255</p> <pre>@article{kulyabin2024octdl, title={OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods}, author={Kulyabin, Mikhail and Zhdanov, Aleksei and Nikiforova, Anastasia and Stepichev, Andrey <br> and Kuznetsova, Anna and Ronkin, Mikhail and Borisov, Vasilii and Bogachev, Alexander <br> and Korotkich, Sergey and Constable, Paul A and Maier, Andreas}, journal={Scientific Data}, volume={11}, number={1}, pages={365}, year={2024}, publisher={Nature Publishing Group UK London},<br> doi={https://doi.org/10.1038/s41597-024-03182-7} } </pre>
Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2
<p>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.</p>
Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces
<p>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:</p> <p><strong>Templates</strong></p> <ul> <li>Template_dE00 calculation.xlsx</li> <li>Template_Skewness calculation.xlsx</li> </ul> <p><strong>Metric calculation tables</strong></p> <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> <p><strong>Extended result tables</strong></p> <ul> <li>Free liquid trial assessments.xlsx</li> <li>Cleaning score tables.xlsx</li> </ul> <p><strong>Processed images from photography, microscopy, FTIR and SEM</strong></p> <ul> <li>Processed images (VIS, FTIR, SEM).pdf</li> </ul> <p> </p>
Firemaker image collection for benchmarking forensic writer identification using image-based pattern recognition
<p>Disclaimer and terms of use:<br> ============================</p> <p>/*****************************************************************************\<br> * *<br> * *<br> * This is the Firemaker NFI-images Distribution *<br> * *<br> * This distribution contains 1000 images of scanned handwritten text, *<br> * scanned at resolution 300dpi grey scale, containing pages of *<br> * handwritten text by 250 writers, four pages per writer, from four *<br> * writing conditions, one condition per page. The conditions are: *<br> * p1: copied, natural style, p2: copied, UPPER case, p3: copied and forged, *<br> * i.e.,"try to write in a different style than your natural style", and p4, *<br> * self generated, i.e., text produced to describe a given cartoon. *<br> * *<br> * *<br> * *<br> * Copyright The International Unipen Foundation, 2000, All rights reserved *<br> *******************************************************************************<br> * *<br> * *<br> * DISCLAIMER AND COPYRIGHT NOTICE FOR ALL DATA CONTAINED ON THIS CDROM: *<br> * *<br> * *<br> * 1) PERMISSION IS HEREBY GRANTED TO USE THE DATA FOR RESEARCH *<br> * PURPOSES. IT IS NOT ALLOWED TO DISTRIBUTE THIS DATA FOR COMMERCIAL *<br> * PURPOSES. *<br> * *<br> * *<br> * 2) PROVIDER GIVES NO EXPRESS OR IMPLIED WARRANTY OF ANY KIND AND ANY *<br> * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR PURPOSE ARE *<br> * DISCLAIMED. *<br> * *<br> * 3) PROVIDER SHALL NOT BE LIABLE FOR ANY DIRECT, INDIRECT, SPECIAL, *<br> * INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF ANY USE OF THIS *<br> * DATA. *<br> * *<br> * 4) THE USER SHOULD REFER TO THE FIRST PUBLIC ARTICLE ON THIS DATA SET: *<br> * *<br> * M. Bulacu, L. Schomaker & L. Vuurpijl (2003). *<br> * Writer identification using edge-based directional features. *<br> * ICDAR '03: Proceedings of the 7th International Conference on Document *<br> * Analysis and Recognition, pp. 937-941. *<br> * Piscataway: IEEE Computer, ISBN 0-7695-1960-1 *<br> * *<br> * 5) THE RECIPIENT SHOULD REFRAIN FROM PROLIFERATING THE DATA SET TO THIRD *<br> * PARTIES EXTERNAL TO HIS/HER LOCAL RESEARCH GROUP. PLEASE REFER INTERESTED *<br> * RESEARCHERS TO HTTP://UNIPEN.ORG FOR OBTAINING THEIR OWN COPY. *<br> \*****************************************************************************/</p> <p>BibTeX entry: </p> <p> @inproceedings{Firemaker, <br> author = {Bulacu, M. and Schomaker, L.R.B. and Vuurpijl, L.}, <br> title = {Writer Identification Using Edge-Based Directional Features},<br> booktitle = {ICDAR '03: Proceedings of the 7th International <br> Conference on Document Analysis and Recognition},<br> year = {2003},<br> isbn = {0-7695-1960-1},<br> pages = {937-941},<br> publisher = {IEEE Computer Society},<br> address = {Washington, DC, USA},<br> }</p> <p>In the project "Vergelijk", a grant obtained from the Dutch Forensic Science<br> Institute, two existing professional writer-identification systems have been <br> compared regarding usability studies and in particular recognition <br> performance (Schomaker & Vuurpijl, 2000). The results of this comparison <br> are contained in a confidential report:</p> <p> L.R.B. Schomaker and L.G. Vuurpijl (2000). <br> Forensic writer identification: A benchmark data set <br> and a comparison of two systems. Technical report, <br> Nijmegen Institute for Cognition and Information (NICI), <br> University of Nijmegen, The Netherlands.</p> <p>Informative and non-confidential details from this report are <br> given in the accompanying file: 'firemaker-dbase.pdf'</p> <p>To compare both systems, a carefully designed experiment was conducted to<br> record handwritten samples from male and female writers in several conditions:</p> <p>Condition 1: Normal constrained handwriting<br> ==============================================</p> <p>Below, the Dutch text writers had to produce in normal handwriting is given. </p> <p>--- start text ----<br> Zij bezochten veilingen en reisden met de KLM. Voor<br> korte afstanden huurden ze een auto, meestal een VW<br> of een Ford.<br> <EMPTY LINE><br> De veilingen waren van 7-4-1993 tot 3-5-1993 in New<br> York, Tokyo, Québec, Rome, Parijs, Zürich en Oslo.<br> <EMPTY LINE><br> Omdat de veilingen steeds begonnen om 12 uur en je<br> gemiddeld 200 tot 300 kilometer moest rijden,<br> stonden zij steeds om 6.30 uur op en vertrokken om<br> 8 uur uit het hotel.<br> <EMPTY LINE><br> Elke dag hadden ze vijfhonderd (f 500,-) gulden<br> nodig. Daarvoor gebruikten ze elke keer een cheque<br> van tweehonderd (f 200,-) en een cheque van<br> driehonderd (f 300,-) gulden. Aan geschenken gaven<br> ze ongeveer honderd gulden (f 100,-) uit.<br> --- end text ----</p> <p><br> Condition 2: Production of constrained block capital handwriting<br> ================================================================</p> <p>In this condition, the writers had to produce the following text<br> in block-capital handwriting:</p> <p>--- start text ----<br> NADAT ZE IN NEW YORK, TOKYO, QUÉBEC, PARIJS, ZÜRICH<br> EN OSLO WAREN GEWEEST, VLOGEN ZE UIT DE USA TERUG<br> MET VLUCHT KL 658 OM 12 UUR.<br> <empty line><br> ZE KWAMEN AAN IN DUBLIN OM 7 UUR EN IN AMSTERDAM OM<br> 9.40 UUR 'S AVONDS. DE FIAT VAN BOB EN DE VW VAN<br> DAVID STONDEN IN R3 VAN HET PARKEERTERREIN.<br> HIERVOOR MOESTEN ZE HONDERD GULDEN (F 100,-)<br> BETALEN.<br> --- end text ----</p> <p><br> Condition 3: Production of free-forged handwriting<br> ==================================================</p> <p>Below, the text writers had to produce in the free-forged handwriting<br> condition is given. No example of handwriting is given which they have to<br> mimick (forge), the condition concerns a self-conceived distorted <br> handwriting style.</p> <p>--- start text ----<br> Nog dezelfde avond reden ze naar hun vrienden<br> Chris, Emile, Jan, Irene en Henk, nadat ze hun<br> vriendinnen Greta en Maria hadden opgehaald.<br> <EMPTY LINE><br> Samen hadden ze vijfhonderd (500) zeldzame<br> postzegels gekocht, Bob driehonderd (300) en David<br> tweehonderd (200).<br> <EMPTY LINE><br> De reis was de moeite waard geweest.<br> --- end text ----</p> <p><br> Condition 4: Production of unconstrained handwriting<br> ====================================================</p> <p>The final text writers had to produce is unconstrained handwriting.<br> The cartoon, a series of pictures concerning a 'UFO' landing had<br> to be described in their own words, in at least six lines of text.<br> See image file "space.gif".</p> <p><br> Thruth labels and writer identifications<br> ========================================</p> <p>Each writer has a unique id, specified as:</p> <p> id: {num}{set}<br> num: a three-digit number<br> set: either 01, 02, 03 or 04, identifying one of the 4 experiments</p> <p>The vast majority of the writers producing sets 01, 02 and 03 mimicked the<br> content and layout (empty lines) of the constrained texts they had to copy<br> sufficiently accurately, such that the example texts are a good indication of<br> the contents. However, as set 04 ("describe cartoon story") contains<br> unconstrained self-generated handwriting, the corresponding thruth labels had<br> to be extracted manually. The resulting label files are contained in the<br> directory ./300dpi/p4-self-natural/labels/</p> <p>Note: no letter, word, line or paragraph segmentation is provided with this<br> data set. The main text can be cropped easily. Since the orientation is<br> horizontal, projection techniques can be used to extract lines, using<br> a line-spacing parameter (~94 pixels line height) as an additional check. </p> <p><br> Overview of directories:</p> <p>300dpi/<br> p1-copy-normal/ Copying task, normal writing style <br> p2-copy-upper/ Copying task, UPPER-case <br> p3-copy-forged/ Copying task, instructed to mimic another script style<br> p4-self-natural/ Self-generated text, natural writing condition</p> <p>Note: the original raw collection contained writer #155, who has been removed<br> from this data set, as his first condition (p1) was started in upper case and<br> the page was not completed. Deleted files were 15501.tif, 15502.tif, 15503.tif<br> and 15504.tif.</p> <p>Note: the name of this data set (Firemaker) is a contraction of the names<br> Vuurpijl and Schomaker.</p> <p>Note b: Example of a cutout of essential handwritten text using NetPBM tools: <br> tifftopnm 15201.tif | pnmcut -left 50 -right 2400 -top 700 -bottom 3250 > handwriting.pgm</p> <p> For an experiment, the upper and lower halves of the resulting image were<br> usually used in the Schomaker & Bulacu studies to obtain two samples of <br> handwriting for a writer.</p> <p> http://www.ai.rug.nl/~lambert<br> http://www.ai.rug.nl/~bulacu</p> <p>Our features for writer identification:</p> <p>Lambert Schomaker<br> http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html<br> L. Schomaker & M. Bulacu (2004). <br> Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. <br> IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.</p> <p>Marius Bulacu<br> http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html<br> Bulacu, M. & Schomaker, L.R.B. (2007). <br> Text-independent Writer Identification and Verification Using Textural and Allographic Features, <br> IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.</p> <p>Axel Brink<br> http://www.ai.rug.nl/~axel/ 'Quill' feature<br> A.A. Brink, J. Smit, M.L. Bulacu, and L.R.B. Schomaker (2011). <br> Writer identification using directional ink-trace width measurements, <br> Pattern Recognition (July 2011), doi: 10.1016/j.patcog.2011.07.005<br> <br> These three feature groups (hinge, fraglets, quill) have been combined in<br> a single MS Windows application, GIWIS which is available for scientific<br> use upon request (schomaker@ai.rug.nl)</p> <p>Note c.</p> <p>The accompanying file 'Firemaker-writer-info.dat' contains some<br> writer information: <br> Column 1: writer identification code<br> Column 2: sex<br> Column 3: handedness, <br> Column 4: age in years<br> Column 5: major Western script group (print,cursive or mixed)<br> </p>
LIDAR scan, photos and image-based reconstruction of sofa corner
<p>This data set provides photographs and a continuous LIDAR scan (using the Velodyne VLP-16) of a sofa corner at Simula Research Laboratory that were created for the BUISAR project funded by the researcher council of Norway (project number 270951). A 3D reconstruction of the sofa corner was created using Meshroom (http://github.com/alicevision/meshroom) with support from the LADIO project.</p> <p>The LIDAR scan takes the form of a network trace (.pcap file). This file can be read and interpreted as a point cloud by the Point Cloud Library (PCL, https://github.com/PointCloudLibrary/pcl). Since the VLP-16 comes without motion sensors, the user of the LIDAR scan can either interpret the data as a series of frames, or attempt to align the recorded voxels in a global space. No ground truth for this is provided.</p>
Image-based Many-language Programming Language Identification - Replication Package
<p>This dataset contains the data, software, and instructions needed to replicate the findings of the paper:</p> <p>Francesca Del Bonifro, Maurizio Gabbrielli, Antonio Lategano, and Stefano Zacchiroli. Image-based Many-language<br> Programming Language Identification. <a href="https://peerj.com/computer-science/"><em>PeerJ Computer Science</em></a>, 2021 (to appear). DOI: <a href="https://dx.doi.org/10.7717/peerj-cs.631">10.7717/peerj-cs.631</a></p> <p>After retrieving the full dataset, extract the replication-package.zip archive and follow the instructions described in the README.md file.</p>
Image-based Classification of Intense Radio Bursts from Spectrograms: An Application to Saturn Kilometric Radiation
<p>A catalogue of 4874 of the Low Frequency Extensions (LFEs) of Saturn Kilometric Radiation (SKR) detected by Cassini/RPWS from the beginning of 2004 until mission end in 2017. The LFEs presented in this catalogue were identified using a modified U-Net architecture that applied semantic segmentation to spectrogram images in order to extract the exact frequency-time coordinates of the LFE. The files consist of a .json file with the coordinates of each LFE in Time Frequency Catalogue (TFCat) format (Cecconi et. al. 2023). We also include a .csv file with the start and stop times of each LFE in the form of python datetime timestamps, with the average predicted probability per LFE as an accompanying column. </p>
Single cell analysis by Quantitative image-based cytometry (QIBC)
<p>Quantitative image-based cytometry (QIBC): Employing automated multichannel wild-field microscopy using the Olympus ScanR screening system. This system includes an inverted motorized Olympus IX83 microscope, a motorized stage, IR-laser hardware autofocus, a fast emission filter wheel with single band emission filters. </p> <p>Images were analyzed and processed using ScanR analysis software and TIBCOSpotfire software was used to plot total nuclear pixel intensities and mean (total pixel intensities divided by nuclear area) nuclear intensities.</p>
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
<p>Code: https://github.com/srijitseal/BioMorph_Space<br> <br> Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict <em>in vitro</em> and <em>in vivo</em> drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation.</p> <p> </p> <p>The following datasets are released:<br> </p> <p>Cell_Health_median_357_profiles_70_labels.csv :<br> The Cell Heath dataset for CRISPR perturbations. Contains median consensus signatures for the 357 consensus profiles (119 CRISPR perturbations × 3 cell lines) Ref: Way et al.</p> <p>Cell_Painitng_CRISPR_Perturbations_357_profiles_827_features_scaled.csv:<br> The Cell Painting dataset for CRISPR perturbations. Contains 827 morphology features (and metadata annotation) for 357 consensus profiles (119 CRISPR perturbations × 3 cell lines). Ref: Way et al.</p> <p>Cell_Painting_data_658_compounds_827_Features_scaled.csv<br> The Cell Painting dataset for compound perturbations. Contains 658 structurally unique compounds with 827 Cell Painting features. Ref: Bray et al</p> <p>Endpoints_9_Mitotox_biological_activities_658_compounds.csv<br> The biological assay activity labels for compound perturbations. Contains 658 structurally unique compounds with 9 biological activity consensus hit calls. Ref: ToxCast/MoleculeNet</p> <p>BioMoprh_pvalue_658_compunds_398_BioMorph_terms.csv:<br> The dataset of standardised BioMorph term p-values. Contains 398 BioMorph terms for the 658 compounds in the biological activity dataset. <br> <br> References: <br> Way et al. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021;32(9):995-1005.<br> Bray et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience. 2017;6(12):1-5. <br> MoleculeNet: Wu et al. MoleculeNet: A benchmark for molecular machine learning. Chem Sci. 2018;9(2):513-530. <br> ToxCast: Exploring ToxCast Data | US EPA https://www.epa.gov/chemical-research/exploring-toxcast-data (accessed Jul 9, 2023).</p>
Dataset of the paper "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)"
<p>This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species used to test the performance of a CNN in species recognition. All jpg files are 1920 pixels on the long size and additionally available as an archive file to facilitate download of the entire dataset. </p> <p>Bar code labels (unique specimen identifiers or USIs) were attached to all examined specimens used for this study. Further information such as additional photographs of habitus and genitalic structures, georeferenced coordinates of each locality, specimens dissected, notes, collecting method can be obtained from the Heteroptera Species Pages (http://research.amnh.org/pbi/heteropteraspeciespage/) which assembles available data from a specimen database and are also provided as an Excel spreadsheet (file _Adelphocoris_CNN_label_data.xlsx).</p>
Data for: Image-based evaluation of beers at an online Pint of Science festival using Projective Mapping, Check-All-That-Apply and Acceptability
<p>Data obtained from n=67 untrained attendants at an outreach Pint of Science festival, online because of the COVID-19 pandemic but usually held at bars. The participants used images of brand logos to evaluate eight beers among the most commonly consumed in Spain. Three sensory analysis techniques were used: Projective Mapping, Acceptability and Check-All-That-Apply (CATA).</p>
FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection
<p>This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.</p> <p>In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from <a href="https://www.firenet.xyz/">FireNet</a> have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.</p> <p>The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:</p> <ol> <li> <p><strong>Service 1: FSE Detection</strong> - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.</p> </li> <li> <p><strong>Service 2: FSE Marking Detection</strong> - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.</p> </li> <li> <p><strong>Service 3: Condition Check - Modal</strong> - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>3_1_FSE Condition Check_modal_train_data (containing training images and annotations) </em>and <em>3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 4: Condition Check - Amodal</strong> - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) </em>and <em>4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 5: Details Extraction - Inspection Tags</strong> - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.</p> </li> <li> <p><strong>Service 6: Details Extraction - Fire Classes Symbols</strong> - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.</p> </li> </ol> <p>This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.</p>
Linked collectors and determiners for: Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning.
Natural history specimen data linked to collectors and determiners held within, "Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f1ddda5a-ac85-46a3-955c-b75277c6a600">https://bionomia.net/dataset/f1ddda5a-ac85-46a3-955c-b75277c6a600</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f1ddda5a-ac85-46a3-955c-b75277c6a600">https://gbif.org/dataset/f1ddda5a-ac85-46a3-955c-b75277c6a600</a>. Formatted as a Frictionless Data package.
Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading
<p>This data set contains all the images, DIC data and python scripts and FE input files that allows the reproduction of all results in the paper "Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading" that this data is linked to.</p>
Automatic image-based identication and biomass estimation of invertebrates
<p>Dataset containing images of macroinvertebrates from updated BIODISCOVER (2019). The task of the image collection was to determine optimal camera settings, from 2 parameters: Exposure time and Aperture.</p> <p>A combination of 3 exposure time and aperture settings was tested (total of 9 combinations of settings):</p> <p>Exposure time: 1000, 1500, 2000</p> <p>Aperture: 3.8, 8, 16</p> <p> </p> <p>All images contained within the dataset are separated by camera settings, species and sample number. Samples containing several specimen within one sample number are indexed (example: 10023_1, 10023_2, 10023_3). Further labeling and information can be found within "setup data.csv".</p> <p> </p>
Dataset for: Image-based screen capturing misfolding status of Niemann-Pick type C1 identifies potential candidates for chaperone drugs
<p>Niemann-Pick disease type C is a rare, fatal neurodegenerative disorder characterized by massive intracellular accumulation of cholesterol. In most cases, loss-of-function mutations in NPC1 gene that encodes for a lysosomal cholesterol transporter NPC1 are responsible for the disease, and more than half of the mutations are considered to interfere with biogenesis or folding of the protein. Previously we have identified a series of oxysterol derivatives and phenanthridine-6-one derivatives as pharmacological chaperones, small molecules that rescue folding defective phenotypes of a mutated NPC1, and opened an avenue to develop chaperone therapy for Niemann-Pick disease type C. Here, we established an improved image-based screen for NPC1 chaperones and performed drug-repurposing screening to identify some azole antifungals, including itraconazole and posaconazole, and a kinase inhibitor lapatinib as probable pharmacological chaperones. Photo-crosslinking probes of the compounds allowed us to detect direct binding of itraconazole to a representative folding-defective mutant, NPC1-I1061T. Competitive photo-crosslinking experiments suggested that oxysterol-based chaperones and itraconazole share the same or nearby binding site(s), and sensitivity of the crosslinking to P691S mutation on the sterol-sensing domain supported currently proposed hypothesis that their binding sites are located near the domain. Although the azoles were less effective in reducing cholesterol accumulation than the oxysterol-derived chaperone or an HDAC inhibitor LBH-589, our findings should offer new starting points for developing better pharmacological chaperones for NPC1 through medicinal chemistry efforts.</p>
Flow-Guided Warping for Image-Based Shape Manipulation
<p>Dataset relative to the following publication:</p> <p>Vergne, R., Barla P., Bonneau, G.-P. and R. W. Fleming (2016). Flow-Guided Warping for Image-Based Shape Manipulation. <em>ACM Transactions on Graphics (Proceedings of SIGGRAPH 2016), 35(4)</em>: 93.</p> <p>Each folder contains *.mat files with the data from the experiments presented in Figure 12 of the article (Experiment 1: cg results, Experiment 2: photo results) and a text file with comments.</p>
ScienceDex guides
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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.