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2,139 results for “recognition”
Ground-Truthed Data Set of Zenon Papyri for Handwritten Text Recognition
<p>Diplomatic transcription of papyri found in the Zenon archive [see <a href="https://en.wikipedia.org/wiki/Zenon_of_Kaunos">en.wikipedia.org/wiki/Zenon_of_Kaunos</a>]</p> <p>Manually prepared as PageXML with Transkribus within <a href="http://d-scribes.philhist.unibas.ch/">D-Scribes</a> project.</p> <p> </p>
Simulation Parameters for Two Cooperative Binding Sites Sensitize PI(4,5)P2 Recognition by the Tubby Domain
<p>Dataset to perform the coarse-grained MD simulations presented in "Two cooperative binding sites sensitize PI(4,5)P2 recognition by the tubby domain". The dataset includes protein structures and GROMACS simulation files such as mdp, itp, gro, and index files for the tubby domain as well as PLC-delta1 PH domain.</p>
Automatic Number Plate Recognition Data from Bristol, England for Apr-Jun 2019, 2020 and 2021
<p>These files contain data derived from Automatic Number Plate Recognition (ANPR) data collected by Bristol City Council as part of their continuous traffic monitoring work. The data was used in a project examining the impact of Covid-19 on travel behaviour in Bristol and the implications for air quality. </p> <p>Number plate data cannot be shared, therefore the files contain anonymised vehicle identifiers. Also, specific information about the locations of the ANPR cameras cannot be provided under the data sharing agreement we have with the data owners. Figures 4 and 19 in the project report (see uploaded pdf file) indicate the approximate locations of the ANPR cameras. </p> <p>The following files have been uploaded: </p> <p>- a metadata file containing descriptions of variables in the vehicle and trip files </p> <p>- a copy of the report which came out of this project. </p> <p>- BCCANPR21_REG_descrip_withID_NOVRM.csv: This file includes vehicle information for all vehicles observed in the trip data. The anonymised identification numbers can be used to merge this data with the trip files. </p> <p>- BCCANPR21_2019AprMayJun_REG_idv2.csv: Three files of this format contain trip data. Each file contains trips in April to June of the year specified in the file name. The number plate data was processed to chain together observations that were part of the same trip and this file only contains the location of the first and last ANPR camera at which this trip was recorded. </p> <p>This project was funded by the TRANSITION Clean Air Network through the first round of its Discovery & Innovation Fund in 2021. TRANSITION is a UK-wide network, led by the University of Birmingham in collaboration with nine universities and over 20 cross-sector partners, aiming to optimise the air quality and health outcomes of transport decarbonisation. The network (NERC ref. NE/V002449/1) is itself funded by UK Research & Innovation through its Clean Air Strategic Priorities Fund, administered by the Natural Environment Research Council. </p> <p>The number plate data used in this research was provided by the Bristol Traffic Control Service at Bristol City Council. The vehicle data was obtained through the DVLA’s Vehicle Enquiry Service API. </p>
Proteinoid-Polyaniline Neuromorphic Composites for Audio Recognition
<p>The data presented is in CSV format and includes potentials vs. time and the magnitude of the signal in dB vs. time. This data is related to the research titled "Proteinoid-Polyaniline Neuromorphic Composites for Audio Recognition."</p>
A Brazilian Portuguese Dataset for Offline Handwritten Text Recognition (BRESSAY)
<p>The BRESSAY dataset comprises images of handwritten essays in Brazilian Portuguese, which present a series of challenges to optical recognition models. These images were sourced from multiple online platforms, limiting our ability to standardize the capture process. Due to these varied sources and the lack of a uniform collection method, the dataset provides a realistic reflection of real-world conditions. Each essay is unique, contributed by different writers, and addresses a specific content topic. Furthermore, the constraints placed on the writers often lead to various handwriting scenarios, including hard-to-read words, connected words, noise, overwriting, and struck-through texts.</p> <h3><strong>Technical Details</strong></h3> <p>The BRESSAY dataset represents a comprehensive collection of handwritten essays in Brazilian Portuguese, offering detailed insights into various handwriting scenarios. It covers a total of 1,000 pages, each contributed by a unique writer, resulting in 1,000 distinct handwriting styles. This aspect of the dataset adds a layer of diversity, which is further emphasized by the total of 4,214 paragraphs, 30,090 lines, and 416,826 words. Regarding unique tokens, we have 41,318 unique words, and 107 unique characters.</p> <h3><strong>Data Structure</strong></h3> <p>The dataset is organized as follows:</p> <ul> <li>data/: Main folder containing segmented essay images <ul> <li>lines/: Images of individual lines <ul> <li> PNG files: Line images</li> <li> TXT files: Transcriptions of lines</li> </ul> </li> <li>pages/: Full page essay images <ul> <li> PNG files: Page images</li> <li> TXT files: Transcriptions of pages</li> </ul> </li> <li>paragraphs/: Images of paragraphs <ul> <li> PNG files: Paragraph images</li> <li> TXT files: Transcriptions of paragraphs</li> </ul> </li> <li>words/: Images of individual words <ul> <li> PNG files: Word images</li> <li> TXT files: Transcriptions of words</li> </ul> </li> </ul> </li> <li>sets/: Contains partition files <ul> <li>test.txt: Names of images in the test set</li> <li>validation.txt: Names of images in the validation set</li> <li>training.txt: Names of images in the training set</li> </ul> </li> </ul> <h3><strong>Dataset Usage and Annotations</strong></h3> <p>Each name in test.txt, validation.txt and training.txt represents the name of the page and all its content (words, lines, paragraphs) must be in the respective partition.</p> <p>Annotations used in the dataset:</p> <ul> <li> <code>##@@???@@##</code>: Superscript text that has become unidentifiable and unreadable.</li> <li> <code>$$@@???@@$$</code>: Subscript text that has become unidentifiable and unreadable.</li> <li> <code>@@???@@</code>: Text that cannot be read or identified due to its illegibility.</li> <li> <code>##--xxx--##</code>: Text that has been added as a superscript and subsequently crossed out, rendering it illegible.</li> <li> <code>$$--xxx--$$</code>: Text that has been added as a subscript and subsequently crossed out, rendering it illegible.</li> <li> <code>--xxx--</code>: Text that has been crossed out in a way that makes it unreadable.</li> <li> <code>##--text--##</code>: Text that has been added as a superscript and subsequently crossed out, but remains legible.</li> <li> <code>$$--text--$$</code>: Text that has been added as a subscript and subsequently crossed out, but remains legible.</li> <li> <code>##text##</code>: Text added as a superscript in the line, typically as a correction or additional note.</li> <li> <code>$$text$$</code>: Text added as a subscript in the line, typically as a correction or additional note.</li> <li> <code>--text--</code>: Text that has been crossed out but remains readable.</li> </ul>
FloraNER: a Named Entity Recognition Dataset for Botanical French Text
<p>FloraNER is a Named-Entity Recognition (NER) dataset for botanical french literature. The dataset covers plant species names and plant morphological terms for both plant organs/characteristics and their descriptors. the descriptors are annotated both in a coarse-grained manner as the named entity type "DESCRIPTOR" and in a fine-grained manner, categorized into the following named-entity types: Form, Measure, Surface, Color, Position, Disposition, Structure, and Development. FloraNER is distantly annotated using a specialized botanical corpus. Consequently, it's important to note that not all named entities within the text are captured and annotated for the coarse-grained and fine-grained datasets.</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>
Raw images (photographs) of urban text scenes for camera-based Thai text recognition
<p>Raw image collection of city scenes in Thailand with text content.<br> Text is photographed from diffeent angles. Also morning and evening<br> photographs were taken in order to capture different lighting<br> conditions. The material, 309 images, was photographed in 2013 by<br> Bowornrat Sriman and volunteers.</p> <p>Example EXIF:<br> JPEG image data, Exif standard: [TIFF image data, little-endian,<br> direntries=13, height=2448, manufacturer=SAMSUNG, model=GT-I9300,<br> orientation=upper-right, xresolution=220, yresolution=228,<br> resolutionunit=2, software=I9300XXEMA2, datetime=2013:03:14 18:17:49,<br> GPS-Data, width=3264], baseline, precision 8, 3264x2448, frames 3</p> <p>The images are not labeled. The orientation (landscape/portrait) is<br> not corrected yet. This material was used in preparation of the publication:</p> <p>Sriman, B. & Schomaker, L. (2015).<br> Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT,<br> Proceedings of the International Conference on Pattern Recognition Applications and<br> Methods: ICPRAM 2015. De Marsico, M., Figueiredo, M. & Fred, A.<br> (Eds.). Lisbon, Portugal: SciTePress, Vol. 1, p. 304-311 8 p.</p> <p>Please cite this publication when using these data.</p>
Dataset for ICFHR2018 Competition on Automated Text Recognition on a READ Dataset
<p>The main idea of this dataset is to analyse the impact of training data. How many training data specific to the document, you are transcribing, is necessary? </p> <p><strong>general data: </strong>This is a collection of heterogeneous documents to train an initial system. For each text line there is an image file of that line, a file with the ground truth text and an information file containing an automatically generated surrounding polygon.</p> <p><strong>specific data: </strong>The specific data contains documents related to the test data. For the specific systems only the images of the train list may be used. The file are of the same type as the general data.</p> <p><strong>test data: </strong>The test data contains only the images and the information files.</p> <p>More Information, some published results and an evaluation procedure at https://scriptnet.iit.demokritos.gr/competitions/10/</p>
Sensor-based Pallet Activity Recognition in Logistics (SPARL Version 2) - A multi-modal Dataset
<p>SPARL is a freely accessible data set for sensor-based activity recognition of pallets in logistics. The data set consists of 20 recordings from three scenarios. A description of the scenarios can be found in the protocol file.</p> <p>Four different sensors were used simultaneously for all recordings:</p> <ul> <li>MSR Electronics MSR 145 <ul> <li>Sampling rate 50 Hz</li> </ul> </li> <li>MBIENTLAB MetaMotionS <ul> <li>Sampling rate 100 Hz</li> </ul> </li> <li>Kistler KiDaQ Module 5512A <ul> <li>Sampling rate 100 kHz</li> <li>the raw data is also downsampled to 5 kHz and 20 kHz for easier processing </li> </ul> </li> <li>Holybro Flightcontroller PX4FMU <ul> <li>The board uses two accelerometers and two gyroscopes, all with a sampling rate of 1000 Hz <ul> <li>Accelerometer 1: IvenSense MPU6000 </li> <li>Accelerometer 2: STMicroelectronics LSM303D </li> <li>Gyroscope 1: IvenSense MPU6000</li> <li>Gyroscope 2: STMicroelectronics L3GD20</li> </ul> </li> </ul> </li> </ul> <p>The recordings were accompanied by three logitech Mevo Start cameras, of which all recordings are included anonymously in the data set. </p> <p>The videos were annotated by one person in each frame. For this purpose, the annotation tool SARA was used, which can be found <a href="../records/8189341">here</a>. The JSON schema used for annotation is also included in the SPARL dataset. The R code used our evaluation can be found in <a title="https://github.com/bommert/WGTL24" href="https://github.com/bommert/WGTL24">GitHub</a>.</p> <p>If you have any questions about the dataset, please contact: sven.franke@tu-dortmund.de</p> <p><strong>If you use this dataset for research, please cite the following paper: “Data-driven, sensor-based taxonomy for environmental life cycle assessment of pallets”, Nr. 20 (2024): Logistics Journal: Proceedings, DOI: <a href="http://dx.doi.org/10.2195/lj_proc_franke_en_202410_01" target="_blank" rel="noopener">10.2195/lj_proc_franke_en_202410_01</a></strong></p>
The DARRL dataset: Demonstrations for Action Recognition and Robot Learning
<p>The DARRL dataset (Demonstrations for Action Recognition and Robot Learning) is a collection of 760 RGB-D videos of humans performing various manipulation tasks. It is provided with object and action annotations (in the COCO format) for 30 of those videos; segmentation masks are also provided.</p> <p>It can also be used as a basis for learning from demonstrations for a robotic arm, for instance.</p> <p> </p> <p>This work is supported by Région Pays de la Loire.</p>
Named-Entity Recognition for Modern Tibetan Newspapers: Tagset, Guidelines and Training Data
<p>This dataset, tagset and guidelines were the output of a six-month incubator project on the feasibility of developing Named-Entity Recognition (NER) for modern Tibetan, primarily for use with contemporary Tibetan-language newspapers and media published inside the PRC. The project was carried out by the Mongolian and Inner Asian Studies Unit at Cambridge University’s Department of Social Anthropology. It was funded by an incubator grant from Cambridge Language Sciences. The project title was “Named-Entity Recognition in Tibetan and Mongolian Newspapers.” The Project PI was Dr Hildegard Diemberger (Cambridge), the Coordinator and Lead Author was Dr Robert Barnett (SOAS), and Senior Advisers were Dr Nathan Hill (SOAS), Dr Marieke Meelen (Cambridge), and Dr Thomas White (Cambridge). <br> <br> Although some forms of NER and other NLP procedures have been developed within China for modern Tibetan (see Liu, Nuo <em>et al</em>, 2011), the data underlying those initiatives have not been made publicly available and their findings cannot be tested or reproduced. Significant work on developing NLP for Tibetan has been carried out outside China, but has focused largely on classical Tibetan and religious texts (see Hill & Garrett, Edward, 2017). </p> <p>The Cambridge incubator project therefore produced a tagset, guidelines and training data for developing NER for modern Tibetan, with a focus on historical and political analysis of contemporary newspapers, media and other public documents in Tibetan. We compiled 3.11m syllables of data in Tibetan extracted from articles downloaded from Chinese-language news aggregator sites within China, primarily tibet.cpc.people.com.cn and tibet.people.com.cn. From this data, we selected texts containing 280,000 syllables in Tibetan, grouped in 26,000 utterances/sentences (available on request). Using Lighttag, an online annotation site, we developed a tagset for NER consisting of 17 tags (and one for wrong segmentation if using segmented data). We annotated approximately 186,000 syllables, leading to 9,884 annotations. Of these, after discounting flawed data, we produced training data containing c.6,700 annotations. We carried out the secondary, manual review offline (for our method of converting Lighttag data for offline review, see the attached report “Using Spreadsheets to Review Annotations Offline.pdf”), and found an error rate of 3.6%. The final total of reviewed annotations was 6,624. </p> <p>The dataset, tagset, guidelines and reports were developed and documented by Robert Barnett, with assistance from Tsering Samdrup, Dr Hill and Dr Meelen. Primary annotation was by Tsering Samdrup, assisted by Dr Barnett.<br> <br> The datasets published here include: </p> <ol> <li>The <strong>tagseet guidelines and annotation manual</strong>, including the 17-tag tagset, guidelines, and recommendations ("NER for Modern Tibetan-tagset and guidelines.pdf").</li> <li>The <strong>tagged training data </strong>in .csv format ("Tibetan NER Training Data-tagged, reviewed wth context-v10-UTF-8.csv") and .xls format ("Tibetan NER Training Data-tagged with context-v10-UTF-8.xlsx"). This includes 6,624 reveiwed annotations, arranged according to the Tibetan alphabet together with the tags and context (utterance) for each annotation.</li> <li>The <strong>raw annotation results </strong>downloaded from Lighttag as .json files ("Raw Training Data for NER in Modern Tibetan -Jobs2-11-JSON.zip") and as .xls files ("Training Data for NER in Modern Tibetan -Jobs2-11-XLS.zip"). These include 10 "tasks" or datasets of articles scraped from Tibetan-language websites within Tibet. </li> <li>A <strong>guide to preparing Lighttag annotation results for manual review offline </strong>(“Using Spreadsheets to Review Annotations Offline.pdf”).</li> </ol> <p>The project's findings regarding the status of NER and NLP for vertical Mongolian are available at DOI: 10.5281/zenodo.5103499.</p>
Qualitative dataset - Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation (Smaal et al., 2021)
<p>This qualitative dataset contains the English translations of the plain texts of the urban food strategy documents or webpages of 16 European medium-sized cities: Basel [CH]; Bristol [UK]; Bruges [BE]; Cordoba [ES]; Donostia - San Sebastián [ES]; Ede [NL]; Geneva [CH]; Ghent [BE]; Grenoble [FR]; Groningen [NL]; Montpellier [FR]; Nantes [FR]; Rennes [FR]; Tours [FR]; Uppsala [SE]; and Vitoria-Gasteiz [ES]. The search for and translation of the urban food strategy documents and webpages have been performed in early 2019. The files have been analysed in NVivo (qualitative data analysis software). The upload also includes figures and a table with the authors' assessments connected to the resources and services codes and radar diagram visualisations presented in the following paper: </p> <p>Smaal, S. A. L., Dessein, J., Wind, B. J., & Rogge, E. (2021). Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation. <em>Agriculture and Human Values</em>, 38(3), 709–727. <a href="http://doi.org/10.1007/s10460-020-10179-6">https://doi.org/10.1007/s10460-020-10179-6</a> </p> <p><strong>Abstract: </strong>More and more cities develop urban food strategies (UFSs) to guide their efforts and practices towards more sustainable food systems. An emerging theme shaping these food policy endeavours, especially prominent in North and South America, concerns the enhancement of social justice within food systems. To operationalise this theme in a European urban food governance context we adopt Nancy Fraser’s three-dimensional theory of justice: economic redistribution, cultural recognition and political representation. In this paper, we discuss the findings of an exploratory document analysis of the social justice-oriented ambitions, motivations, current practices and policy trajectories articulated in sixteen European UFSs. We reflect on the food-related resource allocations, value patterns and decision rules these cities propose to alter and the target groups they propose to support, empower or include. Overall, we find that UFSs make little explicit reference to social justice and justice-oriented food concepts, such as food security, food justice, food democracy and food sovereignty. Nevertheless, the identified resources, services and target groups indicate that the three dimensions of Fraser are at the heart of many of the measures described. We argue that implicit, fragmentary and unspecified adoption of social justice in European UFSs is problematic, as it may hold back public consciousness, debate and collective action regarding food system inequalities and may be easily disregarded in policy budgeting, implementation and evaluation trajectories. As a path forward, we present our plans for the RE-ADJUSTool that would enable UFS stakeholders to reflect on how their UFS can incorporate social justice and who to involve in this pursuit.</p> <p><em>This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765389. </em></p> <p>Project webpage: <a href="https://recoms.eu/">https://recoms.eu/</a></p>
Indian Art Music Raga Recognition Dataset (features)
<p>The <strong>Rāga Recognition Datasets (features)</strong> comprise two sizable datasets, one for each music tradition: the <strong>Carnatic Music Dataset (CMD)</strong> and the <strong>Hindustani Music Dataset (HMD)</strong>. Each dataset entry includes features such as <strong>pitch</strong>, <strong>tonic</strong>, and <strong>nyas</strong> and <strong>tani</strong> segments. These datasets can be used to develop and evaluate approaches for automatic rāga recognition in Indian art music. To the best of our knowledge, they are the largest and most comprehensive datasets (in terms of available metadata) ever used for studying this task.</p> <p>This repository only contains the metadata and computed features for the dataset, and shared in open access. To get the audio, please refer <a href="https://zenodo.org/records/7278511" target="_blank" rel="noopener">to this zenodo entry</a> and submit your request.</p> <p> </p> <p>Please cite the following publications if you use the material shared here in your research work.</p> <blockquote> <p>Gulati, S., Serrà, J., Ganguli, K. K., ¸Sentürk, S., & Serra, X. (2016). Time-delayed melody surfaces for raga recognition. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pp. 751–757. New York, USA. [<a href="http://hdl.handle.net/10230/33117">Postprint PDF</a>]</p> </blockquote> <blockquote> <p>Gulati, S., Serrà, J., Ishwar, V., ¸Sentürk, S., & Serra, X. (2016). Phrase-based raga recognition using vector space modeling. In Proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 66–70. Shanghai, China. [<a href="http://hdl.handle.net/10230/32879">Postprint PDF</a>]</p> </blockquote> <p> </p> <h2>Annotation Format</h2> <p>We provide both tsv files and json files that contain information about each audio recording in terms of its mbid, the path of the audio/feature files and the associated rāga identifier. Each rāga is assigned a unique identifier by Dunya, which is similar to the mbid in terms of purpose. We also provide a mapping of the rāga id to its transliterated name.</p> <h2>Mirdata</h2> <p>This dataset is included in <a href="https://github.com/mir-dataset-loaders/mirdata">mirdata</a>. Use the following code snippet to access the dataset in mirdata.</p> <pre><code># Import midata import mirdata # Initialize dataset dataset_name = 'compmusic_raga' data_home = 'mirdata/dataset' dataset = mirdata.initialize(dataset_name, data_home=data_home) # Download dataset dataset.download() # Validate dataset dataset.validate() # Load dataset as a dictionary with track ids as keys and track objects as values data = dataset.load_tracks()</code></pre> <p>In order to load the audio files in mirdata, they must be requested beforehand and placed in the data home directory.</p> <h2>Contact </h2> <p>If you have any questions or comments about the dataset, please feel free to email:</p> <p><a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a></p> <p> </p>
The e-NDP project : collaborative digital edition of the Chapter registers of Notre-Dame of Paris (1326-1504). Ground-truth for handwriting text recognition (HTR) on late medieval manuscripts.
<p>The <a href="https://endp.hypotheses.org/">e-NDP project</a>, funded by the ANR, is led by the <a href="https://lamop.hypotheses.org/6870">LaMOP</a> (Julie Claustre and Darwin Smith).</p> <p>The project's partners are the Archives nationales, the Bibliothèque nationale de France (Department of Manuscripts, Bibliothèque de l'Arsenal), the École nationale des chartes and the Bibliothèque Mazarine.</p> <p>The e-NDP project aims at renewing our knowledge on <strong>Notre-Dame de Paris cathedral</strong> through the creation of a collaborative digital edition of the registers of its Chapter (1326-1504, <em>AN LL 105-128</em>), the community of 51 canons meeting three times a week on set days to take all administrative, financial and practical decisions pertaining to the cathedral, its estate and the society living in its cloister. This corpus has never been the object of a comprehensive study to understand the workings and history of this urban enclave and powerful community. The collaborative digital edition is based on a process of<strong> handwriting text recognition (HTR)</strong>, tested and supervised by scholars, researchers and engineers combining expertise in Medieval history, paleography, philology and digital humanities. The edition shall allow a better insight into the Chapter’s administration, into its economical and political power within Paris, and the relationships it maintained with other institutions in the city.</p> <p> </p> <p><strong>Section 1 : The e-NDP ground-truth dataset for Handwriting text recognition.</strong></p> <p>The full e-NDP corpus kept today in the French National Archives and was entirely digitized and described in its <a href="https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/consultationIR.action?formCaller=GENERALISTE&irId=FRAN_IR_059635">catalog</a> in 2022.</p> <p>The first major goal of the e-NDP projet is to propose a first automatic transcription of the 14k pages composing the 26 chapter registers. To achieve this goal representative samples from each one of the volumes were selected and transcribed in order to train a specialized HTR model able to propose a high quality automatic transcription. The collected ground-truth released on this repository currently has <strong>512 pages from the 26 registers</strong> of the cathedral chapter preserved in the National Archives (LL105 - LL128, <strong>1326-1504</strong>). The transcriptions were manually completed in <strong>two rounds</strong> by a group of 12 contributors, historians and paleographers, over the course of 2021-2022 using <a href="https://escriptorium.paris.inria.fr/">eScriptorium </a>as annotation environment. </p> <p> </p> <p><strong>Ground-truth features :</strong></p> <p><br> <em>Number of hands </em>: according to our estimates no fewer than 18 main hands were involved in the writing of the registers during the medieval period. </p> <p><em>Language</em> : More than 98% of the content of the registers was written in Latin, the rest in French. The exact percentage is hard to estimate because the vernacular language is often used in formulae, notes and comments. It is rare to find entire pages or blocks written in French. </p> <p><em>Script family</em> : The registers were written using a Cursive script (ca. late XIIIe - XVIe).</p> <p><em>Documental typology</em> : The volumes containing the chapter conclusions were conceived to serve as memorial records, but above all as documents for regular use and consultation in the daily practice of administration and management. In diplomatics the notion of "documentary manuscripts" is used to describe this kind of sources also by opposition to books and litterary or normative manuscripts.</p> <table align="center"> <caption><strong>Ground truth statistics</strong></caption> <tbody> <tr> <th>Text units</th> <th>Count</th> </tr> <tr> <td>Pages</td> <td>512</td> </tr> <tr> <td>Annotated regions (see section 2)</td> <td>2448</td> </tr> <tr> <td>Lines of text</td> <td>34231</td> </tr> <tr> <td>Tokens</td> <td>205083</td> </tr> <tr> <td>Characters</td> <td>3320407</td> </tr> </tbody> </table> <p> </p> <p><strong>Rules of transcription :</strong></p> <ul> <li>The abbreviations have been resolved, both those by suspension (<code>facimꝰ</code> ---> <code>facimus</code>) and by contraction (<code>dñi</code> --> <code>domini</code>). Likewise, those using conventional signs (<code>⁊</code> --> <code>et</code> ; <code>ꝓ</code> --> <code>pro</code>) have been resolved. </li> <li>The named entities (names of persons, places and institutions) have been <code>capitalized</code>. The beginning of a block of text as well as the original capitals used by the notary are also capitalized.</li> <li>The consonantal <code>i</code> and <code>u</code> characters have been transcribed as <code>j</code> and <code>v</code> in both French and Latin.</li> <li>The punctuation marks used in the text: <code>.</code> and <code>/</code> have been transcribed, but the transcription has not been standardized with modern punctuation.</li> <li>Corrections and words that appear cancelled in the manuscript have been transcribed surrounded by the sign <code>$</code> at the beginning and at the end.</li> <li>More specific transcription rules can be found into the file <code>transcription_guidelines.pdf</code></li> </ul> <p> </p> <p><strong>Section 2. e-NDP Layout Segmentation.</strong></p> <p>Layout segmentation is a compulsory step before HTR recognition in order to distinguish sections and regions inside a document. This process intend to separate interdependant page zones to produce a recognition in a section-sequence order and not in a line-sequence order which mix textual and peri-textual content.</p> <p>The regions of 364 pages (see <code>GT-layout_list</code>) of the e-NDP corpus were annotated using a 5 sections vocabulary (see <code>endp_layout_regions</code>) in order to describe the page distribution in all the 26 volumes :</p> <ol> <li><em>Block</em> : All the central text blocks, that normally corresponds to the main content called "conclusions" in registers.</li> <li><em>Liste</em> : List of names of the canons who were present during the meeting. Normally located before the <em>conclusions</em>.</li> <li><em>Entrée</em> : Marginal notes or entries to inform about the content of <em>conclusions</em>.</li> <li><em>Date</em> : Paragraph contending the date. Normally at the head of a <em>conclusion</em>, but separate of the main body.</li> <li><em>Numérotation</em> : Page numbers in roman or arabic. Usually appear in the top corners of the pages.</li> </ol> <table align="center"> <caption><strong>Layout GT statistics</strong></caption> <tbody> <tr> <th>Region</th> <th>Count</th> </tr> <tr> <td>block</td> <td>833</td> </tr> <tr> <td>liste</td> <td>431</td> </tr> <tr> <td>date</td> <td>448</td> </tr> <tr> <td>entrée</td> <td>205</td> </tr> <tr> <td>numérotation</td> <td>531</td> </tr> </tbody> </table> <p> </p> <p><strong>Section 3. The e-NDP HTR modeling.</strong></p> <p>The e-NDP project has progressively trained several HTR models adapted to work on late medieval cursive in order to accelerate the production of ground truth. Currently the best model delivers an average <strong>CER (Character error ratio) of 9.7%</strong> in handwriting recognition on the 26 registers (see <code>endp_learning_curve</code>) and can serve as generalist model for other manuscripts of the same period and similar script family. These models and their training implementation details can be found in the project's github <a href="https://github.com/chartes/e-NDP_HTR">repository</a>. </p> <p>Additionally, the automatic HTR transcriptions of the 26 registers (14k pages, 4.5M tokens) enriched with lexical and semantical information has been the subject of a first <a href="https://nosketch-engine.lamop.fr/#dashboard?corpname=endp">online publication</a> using the NoSketch engine that allows advanced data mining based on the combination of data, metadata and NLP features. </p> <p> </p> <p><strong>Section 4. Dataset content.</strong></p> <p>This zip dataset contains :</p> <p>- <code>HTR_ground_truth</code> : Two folders containing the jpg / jpeg images and their curated transcriptions in PAGE XML format.</p> <p>- <code>images_docs</code> : 4 files illustrating the different phases of the project (list of GT for layout segmentation, layout ontologie, transcription guideline and HTR evaluation curves)</p>
The ICDAR 2003 Informal Competition for the Recognition of On-line Words: The Unipen-ICROW-03 benchmark set - Version 0.0
<p>Proposal for an informal benchmark on word recognition. See for the related ImUnipen collection<br> of word images from on-line vectorial handwriting data: https://zenodo.org/record/1195059</p> <p>At the time (ICDAR 2003) there was not a lot of interest so the project was not pursued.</p> <p>Lambert Schomaker - February 2023</p> <p>_______________________________________________________________________________</p> <p>The ICDAR 2003 Informal Competition for the Recognition of On-line Words:<br> The Unipen-ICROW-03 benchmark set <br> Version 0.0</p> <p>Lambert Schomaker / International Unipen Foundation</p> <p>The ICROW suite of test files for the recognition of isolated on-line<br> free-style (handprint, mixed and cursive) words has been<br> composed. Different tablets, nationalities and languages<br> are involved. Only the ASCII set is used within word labels.</p> <p>The set contains:</p> <p> 13119 written words<br> 884 unique lexical word entries<br> 72 writers </p> <p>Language: Dutch, English, Italian.<br> Nationalities: Dutch, Irish, Italian, + mixed</p> <p>The benchmark test is a good estimator for <br> "walk-up" recognition performance.</p> <p>[Note: some of the writers (NIC-Pc95*.dat set) are present in the<br> UNIPEN R01/V07 distribution, but the actual words are unseen <br> outside of the Int. Unipen Foundation.]</p> <p>Please note the Copyright notice in the <br> accompanying file 'Copyright'</p> <p>Wed Jul 16 21:20:10 CEST 2003</p> <p>Lambert Schomaker</p> <p>---------------------------------------------------------------------------</p> <p>Instructions for the ICDAR 2003 informal competition for<br> the recognition of on-line words.</p> <p>1 - unpack the .tgz file<br> 2 - use the UNIPEN files as input for your recognizer.<br> 3 - report, for each writer, a file <writer-id>.res</p> <p> Example: do-my-recognizer < NIC-Hi93b-marc.dat > NIC-Hi93b-marc.res</p> <p>Format of the .res file.</p> <p>No XML for this moment: simplicity does it.</p> <p>We assume that the recognizer is able to produce a top-10 list<br> of likely words, sorted from most likely to least likely.<br> The output for each word is on a single line. The correct<br> target word is in the first column.</p> <p><targetword 1> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp><br> <targetword 2> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp></p> <p>Example with two words:</p> <p>summertime slumbertime slipknot summertime somatome spumante simulative semitone schoolmate sermonette semimature<br> Aberdeen Adamson Aberdeen Addison Armageddon Abyssinian Araban Albanian Alabamian Abraham Adelaide</p> <p><br> 4 - pack the *.res files in a .tgz or .zip file and send them<br> to schomaker@ai.rug.nl<br> All *.dat files need to be processed.</p> <p>LS.<br> </p> <p> </p>
Line-level Named Entity Recognition annotation for the George Washington and IAM datasets
<p>Line-level Named Entity annotation for the George Washington and IAM datasets. The word-level annotations from Oliver Tüselmann [3] were extended to line-level to enable experimentation with line-level coupled HTR+NER models. We also publish the line-level partition files that result from the partition proposed by [3].</p>
Convergent approaches to AI Explainability for HEP muonic particles pattern recognition Dataset
<p>Dataset associated to the publication "Convergent approaches to AI Explainability for HEP muonic particles pattern recognition", Leandro Maglianella, Lorenzo Nicoletti, Stefano Giagu*, Christian Napoli, and Simone Scardapane, submitted to Computing and Software for Big Science.</p> <p>*corresponding author: stefano.giagu [AT] 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 "images" of muonic tracks with detector noise (shape (850003, 9, 384)). Each image contains 1 muonic track.</li> <li>train_images_without_noise.npy: numpy array containing 850003 "images" of muonic tracks w/o detector noise (shape (850003, 9, 384)). Each image contains 1 muonic track.</li> <li>train_labels.npy: labels associated to each image (shape (850003, 5)), corresponding to (pT, eta, phi, 0, nhits) of the muonic track, with pT: transverse momentum, eta: pseudo-rapidity, phi: azimuthal angle, and nhits: the number of pixels turned on by the muon</li> <li>test_images_with_noise.npy: same as above for a 94445 images test set</li> <li>test_images_without_noise.npy: same as above for a 94445 images test set</li> <li>test_labels.npy: same as above for a 94445 images test set</li> <li>images_only_noise.npy: numpy array containing 944448 "images" w/o muons, containing detector noise only (shape (944448, 9, 384))</li> </ul>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
ScienceDex guides
Understand access before you commit
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.