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zenodo44/100

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>&nbsp;&nbsp; &nbsp;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., &amp; 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{\&#39;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 &#39;README&#39; file contains all the instructions.</p> <p>The &#39;images&#39; 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. &nbsp; The file names for the direct binarized output are of the format &#39;1QIsaa_col&lt;columnnr&gt;.pbm&#39;, for example, &#39;1QIsaa_col15.pbm&#39;. And, for the cleaned version, the format is &#39;1QIsaa_col&lt;columnnr&gt;_cleaned.pbm&#39;, for example, &#39;1QIsaa_col15_cleaned.pbm&#39;. 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 &#39;features&#39; 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, &#39;1QIsaa_col15_cleaned.hinge&#39; and &#39;1QIsaa_col15_cleaned.adjoined&#39;. They are also arranged in separate directories for ease of use.</p> <p>The &#39;plots&#39; 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 &#39;README_plot&#39; file contains examples of how-to-run in the terminal.</p> <p><strong>Brief description:</strong><br> According to ImageMagick&#39;s&#39; identify&#39; tool, the original images are in grayscale (.jpg) from Brill collection, in &#39;8-bit Gray 256c&#39;. &nbsp;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&#39; 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&#39;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.&nbsp;</strong>L. Schomaker &amp; 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. &amp; Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, &nbsp;IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.</em><br> &nbsp;<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 &lt;m.a.dhali(at)rug.nl&gt;<br> Lambert Schomaker &lt;l.r.b.schomaker(at)rug.nl&gt;<br> Mladen Popović &lt;m.popovic(at)rug.nl&gt;</p> <p><strong>Please cite our papers if you use this data set:</strong><br> <em><strong>1.</strong> Popović, M., Dhali, M. A., &amp; 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., &amp; 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>

opencc-by-4.0Jan 2021View details →
zenodo44/100

Firemaker image collection for benchmarking forensic writer identification using image-based pattern recognition

<p>Disclaimer and terms of use:<br> ============================</p> <p>/*****************************************************************************\<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; This is the Firemaker NFI-images Distribution &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; This distribution contains 1000 images of scanned handwritten text, &nbsp; &nbsp; &nbsp; *<br> * &nbsp; scanned at resolution 300dpi grey scale, containing pages of &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;*<br> * &nbsp; handwritten text by 250 writers, four pages per writer, from four &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; writing conditions, one condition per page. The conditions are: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; p1: copied, natural style, p2: copied, UPPER case, p3: copied and forged, *<br> * &nbsp; i.e.,&quot;try to write in a different style than your natural style&quot;, and p4, *<br> * &nbsp; self generated, i.e., text produced to describe a given cartoon. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;*<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; Copyright The International Unipen Foundation, 2000, All rights reserved &nbsp;*<br> *******************************************************************************<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp;DISCLAIMER AND COPYRIGHT NOTICE FOR ALL DATA CONTAINED ON THIS CDROM: &nbsp; &nbsp; &nbsp;*<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp;1) PERMISSION IS HEREBY GRANTED TO USE THE DATA FOR RESEARCH &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; PURPOSES. IT IS NOT ALLOWED TO DISTRIBUTE THIS DATA FOR COMMERCIAL &nbsp; &nbsp; &nbsp;*<br> * &nbsp; &nbsp; PURPOSES. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp;2) PROVIDER GIVES NO EXPRESS OR IMPLIED WARRANTY OF ANY KIND AND ANY &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR PURPOSE ARE &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; DISCLAIMED. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp;3) PROVIDER SHALL NOT BE LIABLE FOR ANY DIRECT, INDIRECT, SPECIAL, &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF ANY USE OF THIS &nbsp; &nbsp; &nbsp;*<br> * &nbsp; &nbsp; DATA. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp;4) THE USER SHOULD REFER TO THE FIRST PUBLIC ARTICLE ON THIS DATA SET: &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; M. Bulacu, L. Schomaker &amp; L. Vuurpijl (2003). &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; Writer identification using edge-based directional features. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;*<br> * &nbsp; &nbsp; ICDAR &#39;03: Proceedings of the 7th International Conference on Document &nbsp;*<br> * &nbsp; &nbsp; Analysis and Recognition, pp. 937-941. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;*<br> * &nbsp; &nbsp; Piscataway: IEEE Computer, ISBN 0-7695-1960-1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> * &nbsp;5) THE RECIPIENT SHOULD REFRAIN FROM PROLIFERATING THE DATA SET TO THIRD &nbsp; *<br> * &nbsp;PARTIES EXTERNAL TO HIS/HER LOCAL RESEARCH GROUP. PLEASE REFER INTERESTED &nbsp;*<br> * &nbsp;RESEARCHERS TO HTTP://UNIPEN.ORG FOR OBTAINING THEIR OWN COPY. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; *<br> \*****************************************************************************/</p> <p>BibTeX entry: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</p> <p>&nbsp; @inproceedings{Firemaker, &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br> &nbsp; &nbsp; author = {Bulacu, M. and Schomaker, L.R.B. and Vuurpijl, L.}, &nbsp; &nbsp;<br> &nbsp; &nbsp; title = {Writer Identification Using Edge-Based Directional Features},<br> &nbsp; &nbsp; booktitle = {ICDAR &#39;03: Proceedings of the 7th International&nbsp;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Conference on Document Analysis and Recognition},<br> &nbsp; &nbsp; year = {2003},<br> &nbsp; &nbsp; isbn = {0-7695-1960-1},<br> &nbsp; &nbsp; pages = {937-941},<br> &nbsp; &nbsp; publisher = {IEEE Computer Society},<br> &nbsp; &nbsp; address = {Washington, DC, USA},<br> &nbsp; &nbsp;}</p> <p>In the project &quot;Vergelijk&quot;, a grant obtained from the Dutch Forensic Science<br> Institute, two existing professional writer-identification systems have been&nbsp;<br> compared regarding usability studies and in particular recognition&nbsp;<br> performance (Schomaker &amp; Vuurpijl, 2000). The results of this comparison&nbsp;<br> are contained in a confidential report:</p> <p>&nbsp; L.R.B. Schomaker and L.G. Vuurpijl (2000).&nbsp;<br> &nbsp; Forensic writer identification: A benchmark data set&nbsp;<br> &nbsp; and a comparison of two systems. Technical report,&nbsp;<br> &nbsp; Nijmegen Institute for Cognition and Information (NICI),&nbsp;<br> &nbsp; University of Nijmegen, The Netherlands.</p> <p>Informative and non-confidential details from this report are&nbsp;<br> given in the accompanying file: &nbsp;&#39;firemaker-dbase.pdf&#39;</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.&nbsp;</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> &lt;EMPTY LINE&gt;<br> De veilingen waren van 7-4-1993 tot 3-5-1993 in New<br> York, Tokyo, Qu&eacute;bec, Rome, Parijs, Z&uuml;rich en Oslo.<br> &lt;EMPTY LINE&gt;<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> &lt;EMPTY LINE&gt;<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, QU&Eacute;BEC, PARIJS, Z&Uuml;RICH<br> EN OSLO WAREN GEWEEST, VLOGEN ZE UIT DE USA TERUG<br> MET VLUCHT KL 658 OM 12 UUR.<br> &lt;empty line&gt;<br> ZE KWAMEN AAN IN DUBLIN OM 7 UUR EN IN AMSTERDAM OM<br> 9.40 UUR &#39;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&nbsp;<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> &lt;EMPTY LINE&gt;<br> Samen hadden ze vijfhonderd (500) zeldzame<br> postzegels gekocht, Bob driehonderd (300) en David<br> tweehonderd (200).<br> &lt;EMPTY LINE&gt;<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 &#39;UFO&#39; landing had<br> to be described in their own words, in at least six lines of text.<br> See image file &quot;space.gif&quot;.</p> <p><br> Thruth labels and writer identifications<br> ========================================</p> <p>Each writer has a unique id, specified as:</p> <p>&nbsp; &nbsp;id: &nbsp; {num}{set}<br> &nbsp; num: &nbsp; a three-digit number<br> &nbsp;set: &nbsp; &nbsp;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 (&quot;describe cartoon story&quot;) &nbsp;contains<br> unconstrained self-generated handwriting, the corresponding thruth &nbsp;labels had<br> to be extracted manually. The resulting label files are contained in &nbsp;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.&nbsp;</p> <p><br> Overview of directories:</p> <p>300dpi/<br> &nbsp; &nbsp;p1-copy-normal/ &nbsp; &nbsp; &nbsp;Copying task, normal writing style &nbsp;<br> &nbsp; &nbsp;p2-copy-upper/ &nbsp; &nbsp; &nbsp; Copying task, UPPER-case&nbsp;<br> &nbsp; &nbsp;p3-copy-forged/ &nbsp; &nbsp; &nbsp;Copying task, instructed to mimic another script style<br> &nbsp; &nbsp;p4-self-natural/ &nbsp; &nbsp; 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 &nbsp;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: &nbsp;<br> &nbsp;tifftopnm 15201.tif | pnmcut -left 50 -right 2400 -top 700 -bottom 3250 &gt; handwriting.pgm</p> <p>&nbsp;For an experiment, the upper and lower halves of the resulting image were<br> &nbsp;usually used in the Schomaker &amp; Bulacu studies to obtain two samples of&nbsp;<br> &nbsp;handwriting for a writer.</p> <p>&nbsp;http://www.ai.rug.nl/~lambert<br> &nbsp;http://www.ai.rug.nl/~bulacu</p> <p>Our features for writer identification:</p> <p>Lambert Schomaker<br> &nbsp;http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html<br> &nbsp;L. Schomaker &amp; M. Bulacu (2004).&nbsp;<br> &nbsp;Automatic writer identification using connected-component contours and edge-based features of upper-case Western script.&nbsp;<br> &nbsp;IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.</p> <p>Marius Bulacu<br> &nbsp;http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html<br> &nbsp;Bulacu, M. &amp; Schomaker, L.R.B. (2007).&nbsp;<br> &nbsp;Text-independent Writer Identification and Verification Using Textural and Allographic Features,&nbsp;<br> &nbsp;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> &nbsp;http://www.ai.rug.nl/~axel/ &nbsp;&#39;Quill&#39; feature<br> &nbsp;A.A. Brink, J. Smit, M.L. Bulacu, and L.R.B. Schomaker (2011).&nbsp;<br> &nbsp;Writer identification using directional ink-trace width measurements,&nbsp;<br> &nbsp;Pattern Recognition (July 2011), doi: 10.1016/j.patcog.2011.07.005<br> &nbsp;<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 &#39;Firemaker-writer-info.dat&#39; contains some<br> writer information:&nbsp;<br> Column 1: writer identification code<br> Column 2: sex<br> Column 3: handedness,&nbsp;<br> Column 4: age in years<br> Column 5: major Western script group (print,cursive or mixed)<br> &nbsp;</p>

opencc-by-4.0Dec 1999View details →
zenodo44/100

Convergent approaches to AI Explainability for HEP muonic particles pattern recognition Dataset

<p>Dataset associated to the publication &quot;Convergent approaches to AI Explainability for HEP muonic particles pattern recognition&quot;,&nbsp;Leandro Maglianella, Lorenzo Nicoletti, Stefano Giagu*, Christian Napoli,&nbsp;and Simone Scardapane, submitted to&nbsp;Computing and Software for Big Science.</p> <p>*corresponding author:&nbsp;stefano.giagu [AT]&nbsp;uniroma1.it</p> <p>Description:</p> <p>provided as a compressed zip file. Contains 7 numpy .npy files:</p> <ul> <li>train_images_with_noise.npy: numpy array containing 850003 &quot;images&quot; of muonic tracks with detector noise (shape&nbsp;(850003, 9, 384)). Each image contains 1 muonic track.</li> <li>train_images_without_noise.npy: numpy array containing 850003 &quot;images&quot; of muonic tracks w/o detector noise (shape&nbsp;(850003, 9, 384)).&nbsp;Each image contains 1 muonic track.</li> <li>train_labels.npy: labels associated to each image (shape&nbsp;(850003, 5)), corresponding to&nbsp;&nbsp;(pT, eta, phi, 0, nhits) of the muonic track, with pT: transverse momentum, eta: pseudo-rapidity,&nbsp;&nbsp;phi: azimuthal angle, and&nbsp;nhits:&nbsp;the number of pixels turned on by the muon</li> <li>test_images_with_noise.npy: same as above for a&nbsp;94445 images test set</li> <li>test_images_without_noise.npy:&nbsp;same as above for a&nbsp;94445 images test set</li> <li>test_labels.npy:&nbsp;same as above for a&nbsp;94445 images test set</li> <li>images_only_noise.npy:&nbsp;numpy array containing 944448 &quot;images&quot; w/o muons, containing&nbsp;detector noise only (shape&nbsp;(944448, 9, 384))</li> </ul>

opencc-by-4.0Dec 2022View details →
zenodo40/100

Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"

<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> &ldquo;Collection of kinematic and kinetic data of young &amp; adult, male &amp; female subjects performing periodic and transient gait tasks for gait pattern recognition&rdquo;<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: &ldquo;database_ISEA2020.mat&rdquo;</p>

opencc-by-4.0Jan 2020View details →
zenodo40/100

Datasets used in the thesis "Contributions to High-Dimensional Pattern Recognition"

<p>Datasets used in the thesis &quot;Contributions to High-Dimensional Pattern Recognition&quot; and related publications.</p>

opencc-by-4.0May 2011View details →
zenodo40/100

Matching Network of Ontologies: a Pattern Recognition Approach

<p>Networks of Ontologies research deals with the need to combine several ontologies at the same time. In a world of integrated systems (system of systems), isolated systems are increasingly rare in the near future, and their integration creates opportunities to change, validate information and add more value to an information system. This system of systems can contain ontologies to support the corresponding knowledge model. Consequently, new integration requirements may have to deal with network alignment rather than single ontologies. This work delves into the area of network alignment and proposes new ways to approach a particular case of alignment of large ontologies. The contribution of the work is the use of algebraic operations on networks to eliminate candidates before alignment and to use a stochastic search method to discover the relevant nodes. These nodes should be retained as they increase accuracy and final alignment retrieval even though they are identical and removed by the algebraic operation. To find out the particular relevance of each node, we propose a random walk combined with a frequent itemsets approach that overcomes the force brute approaches in processing time, as the size of networks grows, and have close precision. The approach was validated using networks of ontologies created from the OAEI ontologies. The approach selected the entities to send to the matcher without losing significant preexisting alignments. Finally, two different matchers were used to get metrics and compare the results with the pairwise force brute approach.</p>

opencc-by-4.0Mar 2022View details →
zenodo40/100

Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)

Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain

opencc-by-4.0Jul 2024View details →
zenodo40/100

Рис. 8. ОтноситеΛьная преΑставΛенность транскриптов патогенраспознающих рецепторов в гемоцитах моΛΛюсков Planorbarius corneus, заражённых трематоΑами Bilharziella polonica (I) и незаражённых особей (N) Fig. 8. Relative number of transcripts of pattern recognition receptors from hemocytes of Planorbarius corneus molluscs infected with Bilharziella polonica trematodes (I) and uninfected individuals (N) in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)

Рис. 8. ОтноситеΛьная преΑставΛенность транскриптов патогенраспознающих рецепторов в гемоцитах моΛΛюсков Planorbarius corneus, заражённых трематоΑами Bilharziella polonica (I) и незаражённых особей (N) Fig. 8. Relative number of transcripts of pattern recognition receptors from hemocytes of Planorbarius corneus molluscs infected with Bilharziella polonica trematodes (I) and uninfected individuals (N)

opencc-by-4.0Jul 2024View details →
zenodo40/100

Qualitative Interview Data: Users and therapists perceptions of myoelectric multi-function upper limb prostheses with direct and pattern recognition control

<p>The data uploaded here were collected in 2016/2017 through semi-structured&nbsp;interviews with prosthesis users and hand therapists. Participants were mainly asked about satisfaction with their prosthetic device and about activities which they perform with the prosthesis. Interviews were conducted in Dutch and German language.</p> <p>All interview data are made publicly available, except for data of prosthesis users who were experienced with pattern recognition control (n=4). Due to the small number of these participants, their interview data is only available upon reasonable request to not compromise participant privacy.&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Mar 2019View details →
dryad40/100

Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition

<p>With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide the greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.</p>

opencc-zeroFeb 2023View details →
dryad40/100

Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition

Open the record for dataset details and reuse information.

publicFeb 2023View details →
zenodo36/100

Dataset for "A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps"

<p>This is the dataset used in the paper, Ezzat, Liu, Hochbaum, and Ding, 2021, &ldquo;A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps,&rdquo; <em>IEEE Transactions on Semiconductor Manufacturing</em>, Vol. 34, pp. 194-206.</p>

opencc-by-4.0Sep 2021View details →
dryad36/100

Patterns of genetic variation and morphology support the recognition of five species in the Gaultheria leucocarpa Blume (Ericaceae) group from mainland China

<p><em>Gaultheria</em> <em>leucocarpa</em> and its varieties form a clade of aromatic shrubs that is widely distributed in subtropical and East Asian tropical regions. The group is taxonomically difficult and is in need of thorough taxonomic investigation. This study focused on taxonomic delimitation within the <em>G. leucocarpa </em>group from mainland China. Field surveys covering the distributional range of <em>G. leucocarpa</em> in mainland China were conducted, wherein four populations from Yunnan and one from Hunan were found bearing visibly morphological and habitat differences. A 63-species phylogenetic tree of <em>Gaultheria</em> based on one nuclear and three chloroplast markers that included samples from the <em>G. leucocarpa</em> group was reconstructed with maximum likelihood to clarify the monophyly of the <em>G. leucocarpa</em> group. Taxonomic relationships among populations of the <em>G. leucocarpa</em> group were investigated with morphology and population genetics, the latter by using two chloroplast genes and two low-copy nuclear genes. Based on the sum of morphological and genetic analyses, we described three species of <em>Gaultheria</em> as new to science, clarified the taxonomic status of <em>G. leucocarpa</em> var. <em>pingbienensis</em>, elevating it to the species level, and resurrected <em>G</em>. <em>crenulata</em> and treated the varieties <em>G. leucocarpa </em>var<em>. crenulata</em>, and <em>G. leucocarpa </em>var<em>. yunnanensis</em> as synonyms of this species. We provide a key to the five species now recognized, along with descriptions and photographs.</p>

opencc-zeroMay 2023View details →
ClinicalTrials.gov36/100

Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning

ClinicalTrials.gov study NCT05738954. IPD Sharing: YES. Countries: 1. Publications: 4.

controlledIPD-YESFeb 2026View details →
dryad36/100

Patterns of genetic variation and morphology support the recognition of five species in the Gaultheria leucocarpa Blume (Ericaceae) group from mainland China

Open the record for dataset details and reuse information.

publicMay 2023View details →
dryad32/100

Tolerant pattern recognition: Evidence from phonotactic responses in the cricket Gryllus bimaculatus (de Geer)

<p>When the amplitude modulation of species-specific acoustic signals is distorted in the transmission channel, signals become difficult to recognise by the receiver. Tolerant auditory pattern recognition systems, which after having perceived the correct species-specific signal transiently broaden their acceptance of communication signals, would be advantageous for animals as an adaptation to the constraints of the environment. Using a well-studied cricket species, <i>Gryllus bimaculatus</i>, we analysed tolerance in auditory steering responses to non-attractive "Odd" and "Silent" chirps by employing a fine-scale open-loop trackball system. Odd chirps on their own did not elicit a phonotactic response. However, when inserted into a calling song pattern with attractive Normal chirps, the females' phonotactic response towards these patterns was significantly larger than to patterns with Silent chirps. Moreover, females actively steered towards non-attractive Odd chirps when these were presented within a sequence of attractive chirps. Our results suggest that crickets employ a tolerant pattern recognition system that, once activated, transiently allows responses to non-attractive sound patterns. As pattern recognition modulates how crickets process non-attractive acoustic signals, the finding is also relevant for the interpretation of two-choice behavioural experiments.</p>

opencc-zeroDec 2021View details →
dryad32/100

Genotyping-by-sequencing-based identification of Arabidopsis pattern recognition receptor RLP32 recognizing proteobacterial translation initiation factor IF1

<p>Activation of plant pattern-triggered immunity (PTI) relies on the recognition of microbe-derived structures, termed patterns, through plant-encoded surface-resident pattern recognition receptors (PRRs). We show that proteobacterial translation initiation factor 1 (IF1) triggers PTI in <em>Arabidopsis thaliana</em> and related Brassicaceae species. Unlike for most other immunogenic patterns, IF1 elicitor activity cannot be assigned to a small peptide epitope, suggesting that tertiary fold features are required for IF1 receptor activation. We have deployed natural variation in IF1 sensitivity to identify <em>Arabidopsis</em> leucine-rich repeat (LRR) receptor-like protein 32 (RLP32) as IF1 receptor using a restriction site-associated DNA sequencing approach. RLP32 confers IF1 sensitivity to rlp32 mutants, IF1-insensitive <em>Arabidopsis</em> accessions and IF1-insensitive <em>Nicotiana benthamiana</em>, binds IF1 specifically and forms complexes with LRR receptor kinases SOBIR1 and BAK1 to mediate signaling. Similar to other PRRs, RLP32 confers resistance to <em>Pseudomonas syringae</em>, highlighting an unexpectedly complex array of bacterial pattern sensors within a single plant species.</p>

opencc-zeroSep 2022View details →
zenodo32/100

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

<p>All data used in the study https://doi.org/10.1101/2023.01.12.523643 &nbsp;can be found here.&nbsp;</p>

opencc-by-4.0May 2024View details →
ClinicalTrials.gov32/100

IOTA Versus Pattern Recognition Method in Diagnosis of Ovarian Masses

ClinicalTrials.gov study NCT02800031. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Comparison of Pattern Recognition Control and Direct Control in TMR

ClinicalTrials.gov study NCT03097978. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record