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OpenNeuro48/100

Transposition confusability during visual word recognition

Open the record for dataset details and reuse information.

openCC0Jan 2019View details →

zenodo48/100

Named Entity Recognition Dataset for Dutch Biographical Texts

A dataset for Named Entity Recognition for Dutch biographies. The original data is available in the Biographical portal of the Netherlands (http://www.biografischportaal.nl/). The annotations are for 6 types of entities: PERSON, LOCATION, ORGANIZATION, DATE, ARTWORK, MISC. Additionally, the CoNLL formatted files were manually checked for tokenization and sentence splitting.

opencc-by-4.0Dec 2023View details →

zenodo48/100

Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes

This contains the merged dataset as described in the work "Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes". This dataset consists of 4 seperate datasets: <ul> <li><a href="../records/8224056" target="_blank" rel="noopener">MedProcNer</a></li> <li><a href="../records/7614764" target="_blank" rel="noopener">DisTEMIST</a></li> <li><a href="../records/4270158" target="_blank" rel="noopener">PharmaCoNER</a></li> <li><a href="../records/10635215" target="_blank" rel="noopener">SympTEMIST</a></li> </ul> The dataset contains two tasks: Task 1: This task is related to multi-class Named Entity Recognition. This dataset contains 5 possible classes: SYMPTOM, PROCEDURE, DISEASE, CHEMICAL and PROTEIN. Task 2: This task is related to Named Entity Linking, where each code corresponds to a code within the SNOMED-CT corpus. The exact corpus used can be obtained <a href="https://download.nlm.nih.gov/umls/kss/IHTSDO20190131/SnomedCT_SpanishRelease-es_PRODUCTION_20190430T120000Z.zip" target="_blank" rel="noopener">here</a>. Further for the MedProcNER, SympTEMIST and DisTEMIST datasets, a gazetteer is provided in the original datasets.  For more information on the construction of the dataset, aswell as dataloaders, we refer you to our <a href="https://github.com/ieeta-pt/Multi-Head-CRF" target="_blank" rel="noopener">GitHub repository</a>. Further this also contains the embeddings from the <a href="https://huggingface.co/cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large" target="_blank" rel="noopener">SapBERT</a> model. Please, cite: <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <blockquote> <div>@article{jonker2024a, title = {Multi-head {{CRF}} classifier for biomedical multi-class named entity recognition on {{Spanish}} clinical notes}, author = {Jonker, Richard A. A. and Almeida, Tiago and Antunes, Rui and Almeida, Jo{\~a}o R. and Matos, S{\'e}rgio}, year = {2024}, journal = {Database}, publisher = {Oxford University Press} }</div> </blockquote> <div>Jonker, R. A. A., Almeida, T., Antunes, R., Almeida, J. R., & Matos, S. (2024). Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes. (Submitted.) </div> <div> </div> <div> License This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

opencc-by-4.0May 2024View details →

zenodo48/100

VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"

This dataset contains required volcano-seismic waveform DBs (dec.95M.16c and dec.09U.4c) used in the article: "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition", published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>),  precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which  have made possible the monitoring of Deception Island since early 1990s. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).

opencc-by-4.0Jul 2018View details →

zenodo48/100

Medley-solos-DB: a cross-collection dataset for musical instrument recognition

Medley-solos-DB ============= Version 1.2 March 2019.     Created By -------------- Vincent Lostanlen (1), Carmine-Emanuele Cella (2), Rachel Bittner (3), Slim Essid (4). (1): New York University (2): UC Berkeley (3): Spotify, Inc. (4): Télécom ParisTech   Description ---------------   Medley-solos-DB is a cross-collection dataset for automatic musical instrument recognition in solo recordings. It consists of a training set of 3-second audio clips, which are extracted from the MedleyDB dataset of Bittner et al. (ISMIR 2014) as well as a test set set of 3-second clips, which are extracted from the solosDB dataset of Essid et al. (IEEE TASLP 2009). Each of these clips contains a single instrument among a taxonomy of eight: clarinet, distorted electric guitar, female singer, flute, piano, tenor saxophone, trumpet, and violin. The Medley-solos-DB dataset is the dataset that is used in the benchmarks of musical instrument recognition in the publications of Lostanlen and Cella (ISMIR 2016) and Andén et al. (IEEE TSP 2019).   [1] V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016. [2] J. Andén, V. Lostanlen, and S. Mallat. Joint time-frequency scattering. IEEE Transactions in Signal Processing, vol. 67, no. 14, pp. 3704-3718, 2019. doi: 10.1109/TSP.2019.2918992   Data Files -------------- The Medley-solos-DB contains 21571 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 32. Every audio clip has a fixed duration of 2972 milliseconds, that is, 65536 discrete-time samples. Every audio file has a name of the form: Medley-solos-DB_SUBSET-INSTRUMENTID_UUID.wav   For example: Medley-solos-DB_test-0_0a282672-c22c-59ff-faaa-ff9eb73fc8e6.wav corresponds to the snippet whose universally unique identifier (UUID) is 0a282672-c22c-59ff-faaa-ff9eb73fc8e6, contains clarinet sounds (clarinet has instrument id equal to 0), and belongs to the test set.   Metadata Files ------------------- The Medley-solos-DB_metadata is a CSV file containing 21572 rows (one for each audio clip) and five columns: 1. subset: either "training", "validation", or "test" 2. instrument: tag in Medley-DB taxonomy, such as "clarinet", "distorted electric guitar", etc. 3. instrument id: integer from 0 to 7. There is a one-to-one between "instrument" (string format) and "instrument id" (integer). We provide both for convenience. 4. song id: integer from 0 to 226. The track and artist names are anonymized. 5. UUID4: universally unique identifier. Assigned and random, and different for every row.   The list of instrument classes is: 0. clarinet 1. distorted electric guitar 2. female singer 3. flute 4. piano 5. tenor saxophone 6. trumpet 7. violin   Please acknowledge Medley-solos-DB in academic research --------------------------------------------------------------------------------- When Medley-solos-DB is used for academic research, we would highly appreciate it if  scientific publications of works partly based on this dataset cite the following publication: V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016. The creation of this dataset was supported by ERC InvariantClass grant 320959.   Conditions of Use ------------------------ Dataset created by Vincent Lostanlen, Rachel Bittner, and Slim Essid, as a derivative work of Medley-DB and solos-Db. The Medley-solos-DB dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/ The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the Medley-solos-DB dataset or any part of it.   Feedback ------------- Please help us improve Medley-solos-DB by sending your feedback to: vincent.lostanlen@nyu.edu In case of a problem, please include as many details as possible.     Acknowledgement ------------------------- We thank all artists, recording engineers, curators, and annotators of both MedleyDB and solosDb.

opencc-by-4.0Sep 2018View details →

zenodo48/100

"ICDAR2023 Competition on Detection and Recognition of Greek Letters on Papyri" Dataset

<h1>Dataset description of the “ICDAR2023 Competition on Detection and Recognition of Greek Letters on Papyri”</h1> Prof. Dr. Isabelle Marthot-Santaniello, Dr. Olga Serbaeva 2024.09.16 <h2>Introduction</h2> The present dataset stems from the ICDAR2023 Competition on Detection and Recognition of Greek Letters on Papyri (original links to the competition are provided in the file “1b.CompetitionLinks.”) The aim of this competition was to investigate the performance of glyph detection and recognition in a very challenging type of historical document: Greek papyri. The detection and recognition of Greek letters on papyri is a preliminary step for computational analysis of handwriting that can lead to major steps forward in our understanding of this important source of information on Antiquity. Such detection and recognition can be done manually by trained papyrologists. It is, however, a time-consuming task that would need automatising.  We provide here the documents related to two different tasks: localisation and classification. The document images are provided by several institutions and are representative of the diversity of book hands on papyri (a millennium time span, various script styles, provenance, states of preservation, means of digitization and resolution). <h2>How the dataset was constructed</h2> In the frame of <a href="https://d-scribes.philhist.unibas.ch/en/case-studies/iliad-208/" target="_blank" rel="noopener">D-Scribes project</a> lead by Prof. Dr. Isabelle Marthot-Santaniello, 2018-2023, around 150 papyri fragments containing Iliad were manually annotated at a letter-level in <a href="https://github.com/readsoftware/read" target="_blank" rel="noopener">READ</a>. The editions were taken, for the major part, from <a href="papyri.info" target="_blank" rel="noopener">papyri.info</a>, and were simplified, i.e. the accents, editorial marks, and other additional information were removed to be as close as possible to what is to be found on papyri. When the text was not available on papyri.info, the relevant passage was extracted from the <a href="https://github.com/PerseusDL/canonical-greekLit/blob/master/data/tlg0012/tlg001/tlg0012.tlg001.perseus-grc2.xml" target="_blank" rel="noopener">Homer Iliad of Perseus</a>. From those, 150 plus papyri fragments, 185 surfaces (sides of fragments) belonging to 136 different manuscript identified by their Trismegistos numbers, (further TMs) were selected to serve as a material for Competition. These 185 surfaces were separated into the “training set” and the “test set” provided for the competition as a set of images and corresponding data in JSON format. Details on the competition summarised in "ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri", by Mathias Seuret, Isabelle Marthot-Santaniello, Stephen A. White, Olga Serbaeva Saraogi, Selaudin Agolli, Guillaume Carrière, Dalia Rodriguez-Salas, and Vincent Christlein; edited by G. A. Fink et al. (Eds.): ICDAR 2023, LNCS 14188, pp. 498–507, 2023. https://doi.org/10.1007/978-3-031-41679-8_29. After the competition ended, the decision was taken to release manually annotated dataset for the “test set” as well. Please find the description of each included document below. <h2> Dataset Structure</h2> “1. CompetitionOverview.xlsx” contains the metadata of the used images in Excel file, state 2024.09.19. Here is the structure of the Excel file:   <table> <tbody> <tr> <td> Excel columns </td> <td> Name </td> <td> Content </td> <td> Notes </td> </tr> <tr> <td> A </td> <td> TM </td> <td> Trismegistos number is internationally used for papyri identification </td> <td> With READ item name in (). </td> </tr> <tr> <td> B </td> <td> Papyri.info link </td> <td> link </td> <td>   </td> </tr> <tr> <td> C </td> <td> Fragments' Owning Institution (from <a href="http://papyri.info">papyri.info</a>) </td> <td> Institution’s name </td> <td> Institution that physically stores the papyri </td> </tr> <tr> <td> D </td> <td> Availability (of metadata, <a href="http://papyri.info">papyri.info</a>) </td> <td> link </td> <td> Metadata reuse clarification </td> </tr> <tr> <td> E </td> <td> text ID (READ) </td> <td> Number from READ SQL database that was used to link the images and the editions. </td> <td> Serves to locate the attached images and understand the JSON structure. </td> </tr> <tr> <td> F </td> <td> Test/Training </td> <td>   </td> <td> I.e. the image was originally included in the training or in the test set of the dataset. </td> </tr> <tr> <td> G </td> <td> Image Name (for orientation) </td> <td>   </td> <td> As in READ </td> </tr> <tr> <td> H </td> <td> Cedopal link </td> <td> link </td> <td> Contains additional metadata and includes the links to all available online images. </td> </tr> <tr> <td> I </td> <td> License from the Institution webpage. </td> <td> Either license or usage summary. </td> <td> If no precise licence has been given, the summary of the reuse rights is provided with a link to the regulations in column K </td> </tr> <tr> <td> J </td> <td> Image URL </td> <td> link </td> <td> Not all images are available online. Please contact the owning institution directly if the image is not available. </td> </tr> <tr> <td> K </td> <td> Information on the image usage from the institution </td> <td> link </td> <td> In case of any doubt, please contact the owning institution directly. </td> </tr> <tr> <td> L </td> <td> Notes </td> <td>   </td> <td>   </td> </tr> </tbody> </table> For the purpose of an easy overview, the items with special problems, i.e. images not online or missing links, have been marked in red. 2. There are three data subsets: 2a. “Training file”  (containing 150 papyri images separated into 108 texts and HomerCompTraining.json). The images are those of papyri containing Iliad of Homer in JPG-format. These were processed in READ, namely, each visible letter on a given papyri was linked to the edition of the Iliad, through this process, each linked letter of the edition was linked to its coordinates in pixels on the HTML-surface of the image. All that information is provided in the JSON-file. The JSON file contains the “annotations” (b-boxes of each letter/sign), “categories” (Greek letters), “images” (Image IDs), and “licenses”. The links between image and bboxes is defined via the “id” in the “images” part (for example,  "id": 6109). This same id is encoded as “"image_id": 6109” in the “annotations”. Alternatively, “text_id” which can be found in the “images” URL and in the file-names provided here and containing images, can be used for data linking. Let us now describe the content of each part of the JSON file: Each “annotation” contains “area" characterised as “bbox" with coordinates,  “category_id”, that allows to identify which Greek letter in categories is represented by the number; “id”, which is a unique number of the cliplet, i.e. area;  “image_id”, that links cliplet to the surface of the image having the same id;  “iscrowd" and “seg_id" are useful to find the information back in READ database;  and, finally, “tags”. In tags, “BaseType" was used to annotate quality as described below. “FootMarkType”, ft1, etc., was used for clustering tests, but played no role for the Competition. “BaseType” ot bt-tags were assigned to the letters to mark the quality of preservation:  bt-1: well-preserved letter that should allows easy identification for both human eyes and the Computer-vision; bt-2: Partially preserved letter that might also have some background damage (holes, additional ink, etc), but remains readable, and has one interpretation. bt-3: Letters damaged to such an extant that they cannot be identified without reading an edition. These are treated as traces of ink. bt-4: The letters that have some damage, but this damage is of such kind that it makes possible multiple interpretations. For example, missing/defaced horizontal stroke makes alpha indistinguishable from damaged delta or lambda. Each “category” contains  “id”, this is a number references also in “annotations” and it allows to identify which Greek letter was in the bbox;  ”name”, for example, “χ”;  and “supercategory”, i.e. “Greek”. Each “image” contains the following sub fields:  “bln_id" is an internal READ number of the html surface;  "date_captured": null - is another READ field;  "file_name": “./images/homer2/txt1/P.Corn.Inv.MSS.A.101.XIII.jpg", allows to link easy image and text, i.e. for the image in question the JPG will be in the file called “txt1”, it is very similar by structure and function to "img_url": "./images/homer2/txt1/P.Corn.Inv.MSS.A.101.XIII.jpg";  each image has “height" and “width" expressed in pixels.  Each image has “id”, and this id is referenced in the “annotations” under “image_id”.  Finally, each image contains a link to “license”, expressed as a number.  Each “licence” lists a license as it was found during the time of competition, i.e. in February 2023. 2b. “Test file”  contains 34 papyri image sides separated into 31 TMs and HomerCompTesting.json The JSON file here only allows to connect the images with the “categories”, “images”, “licenses”, but without the “annotations”. The structure and logic is otherwise the same like in “Training” JSON. 2c. “Answers file”  Containing the “annotations” and other information for the 34 papyri of the “Testing” dataset. The structure and logic is the same like in “Training” JSON. 3. “Additional files”  Containing lists of duplicate segments id (multiple possible readings or tags), respectively 6 items for “Training”, 17 for “Testing” and 15 for “Answers”. 4. “Dataset Description” This same description included for completeness. <h2>References</h2> The Dataset was reused or mentioned in a number of publications (state September 2024) Mohammed, H., Jampour, M. (2024). "From Detection to Modelling: An End-to-End Paleographic System for Analysing Historical Handwriting Styles". In: Sfikas, G., Retsinas, G. (eds) Document Analysis Systems. DAS 2024. Lecture Notes in Computer Science, vol 14994. Springer, Cham, pp. 363–376. https://doi.org/10.1007/978-3-031-70442-0_22 De Gregorio, G., Perrin, S., Pena, R.C.G., Marthot-Santaniello, I., Mouchère, H. (2024). "NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval". In: Mouchère, H., Zhu, A. (eds) Document Analysis and Recognition – ICDAR 2024 Workshops. ICDAR 2024. Lecture Notes in Computer Science, vol 14936. Springer, Cham, pp. 71–86. https://doi.org/10.1007/978-3-031-70642-4_5 <div> Vu, M. T., Beurton-Aimar, M. "PapyTwin net: a Twin network for Greek letters detection on ancient Papyri". HIP '23: 7th International Workshop on Historical Document Imaging and Processing, San Jose, CA, USA, August 2023. https://doi.org/10.1145/3604951.3605522 https://dl.acm.org/doi/fullHtml/10.1145/3604951.3605522 Turnbull, R., Mannix, E. "Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR". (Preprint). arXiv:2401.12513 https://doi.org/10.48550/arXiv.2401.12513 </div>

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

zenodo48/100

Not So Weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation

EBM-PICO is a widely used dataset with PICO annotations at two levels: span-level or coarse-grained and entity-level or fine-grained. Span-level annotations encompass the full information about each class. Entity-level annotations cover the more fine-grained information at the entity level, with PICO classes further divided into fine-grained subclasses. For example, the coarse-grained Participant span is further divided into participant age, gender, condition and sample size in the randomised controlled trial. This dataset comes pre-divided into a training set (n=4,933) annotated through crowd-sourcing and an expert annotated gold test set (n=191) for evaluation. The <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174533/bin/NIHMS988059-supplement-Appendix.pdf">EBM-PICO annotation guidelines</a> caution about variable annotation quality. <a href="http://ceur-ws.org/Vol-2429/paper1.pdf">Abaho et al.</a> developed a framework to post-hoc correct EBM-PICO outcomes annotation inconsistencies. <a href="https://arxiv.org/pdf/1904.09557.pdf">Lee et al.</a> studied annotation span disagreements suggesting variability across the annotators. Low annotation quality in the training dataset is excusable, but the errors in the test set can lead to faulty evaluation of the downstream ML methods. We evaluate 1% of the EBM-PICO training set tokens to gauge the possible reasons for the fine-grained labelling errors and use this exercise to conduct an error-focused PICO re-annotation for the EBM-PICO gold test set. The file 'test_ebm_correctedlabels.tsv' has error corrected EBM-PICO gold test set.   The upload also contains two zip files containing labelling sources mentioned in the Distant-PICO paper.  <ol> <li>ds_cto_dict.zip: contains the four distant supervision dictionaries (P:  participant.txt, I = intervention.txt, intervetion_syn.txt, O: outcome.txt) generated from clinicaltrials.gov using the methodology described in Distant-CTO. </li> <li>handcrafted_dictionaries.zip: contains three files <ul> <li>gender_sexuality.txt: contains a list of possible genders and sexual orientations found across the web. The list is not comprehensive.</li> <li>endpoints_dict.txt: contains outcome names and the names of questionnaires used to measure outcomes assembled from PROM questionnaires and PROMs.</li> <li>comparator_dict: contains a list of idiosyncratic comparator terms like a sham, saline, placebo, etc., compiled from the literature search. The list is not comprehensive.</li> </ul> </li> </ol>

opencc-by-4.0Oct 2022View details →

zenodo44/100

An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

The problem of training a deep neural network with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications are those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research in this area, results with two baseline systems (one trained from scratch and another based on transfer learning), are presented.

opencc-by-4.0Feb 2020View details →

zenodo44/100

Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface

Video-documentation of the <a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a> Volcano-Independent Seismic Recognition (VI.VSR) software, supported by the <a href="https://cordis.europa.eu/project/id/749249">VULCAN.ears</a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the "Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - (Cortés et al., Frontiers in Earth Sciences, 2021) article. VI.VSR aim is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios. The material includes 2 VDs: <ol> <li>"VI.VSR+geoStudio_intro.mp4" -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting geoStudio and its role in the whole VULCAN.ears platform.</li> <li>"VI.VSR.by.geoStudio_case.studies.mp4" -> running the VI.VSR case studies presented in the (Cortés et al., 2021) manuscript.</li> </ol> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).

opencc-by-4.0Dec 2020View details →

zenodo44/100

Transkribus - Handwritten Text Recognition for Premodern Documents (SIMS 2020 Lightning Talk)

Transkribus is a platform for text recognition and can be used via the Transkribus Expert Software (available after registration: transkribus.eu). Through Transkribus different tools for document analysis and text recognition can be directly applied. The intro demonstrates very briefly how Transkribus can help with regards to premodern documents especially since a variety of pre-trained models are already available: for Latin (prints and handwriting), for early modern vernaculars in French, Dutch, English, and German. For more information go to transkribus.eu. Presented as a Schoenberg Symposium 2020 Lightning Talk

opencc-by-4.0Nov 2020View details →

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

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

opencc-by-4.0Jan 2021View details →

zenodo44/100

Data and Computer scripts for 'Place recognition using batlike sonar' (eLife)

The paper ‘Place recognition using batlike sonar’ can be freely accessed online at http://dx.doi.org/10.7554/eLife.14188. Contents, including text, figures, and data, are free to reuse under a CC BY 4.0 license. The uploaded files contain all data (including raw data), scripts and supporting files used in preparing the manuscript <ul> <li>360panoramas.tar: 360 panoramic pictures taken at the locations at the St Andrews site.</li> <li>PhotosSites.tar: Additional pictures taken at the different ensonification sites</li> <li>ProcessData.tar: This contains all Matlab code for processing and visualizing the data. Also, it contains the templates for all locations as used in the paper.</li> <li>RawData.7z.0xx: These files contain the raw acoustic data as recorded from the microphones for all locations at each of the three sites. This data is provided as Matlab arrays. Due to the file size limitation of Zenodo, the archive has been split into 17 parts. Your archive manager should be able to open all data by accessing RawData.7z.001.</li> <li>Latex.tar: The latex source code and images for the final manuscript.</li> </ul>

opencc-zeroJul 2016View details →

zenodo44/100

Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"

The file Data_Marxetal2014_AB.csv contains the data to the paper Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4. as comma-separated value (csv) file Each row contains the data of one trial, represented by the following columns 1 - number of the line 2 - subject ID 3 - experiment number 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original) 5 - number of targets 6 - SOA in ms 7 - serial position of first target (0 if absent) 8 - serial position of second target (0 if absent) 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine) 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine) 11 - response to "How many animals?" (detection) 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine) 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)

opencc-by-4.0Dec 2014View details →

zenodo44/100

Inertial Dataset for Posture Recognition in Agriculture and Construction Tasks

This dataset, manually labeled, contains 10 hours and 40 minutes (60 Hz) of 8 typical working posture classes (standing, reaching, stooping, squatting, kneeling, lifting/lowering, carrying, and others), acquired with 16 subjects in three distinct scenarios in a lab environment: <ol> <li>Isolated postures or short sequences without any associated task;</li> <li>Agriculture task (bricklaying) circuit;</li> <li>Construction task (harvesting) circuit.</li> </ol> Two full-body inertial motion caption systems (17 Xsens MTw Awinda IMUs each, from Xsens Technologies, B.V., The Netherlands) were used, connected to, respectively: <ol> <li>Xsens MT Manager, providing raw inertial data (acceleration, angular velocity, and magnetic field data - csv files);</li> <li>Xsens MVN Analyze, providing processed data (quaternions, Euler angles, position, linear velocity, acceleration, angular velocity, angular acceleration, joint angles, ergonomic angles, center of mass, and magnetic field - xlsx files).</li> </ol> <div> More information about the dataset acquisition and organization is detailed in readme.pdf file. For any questions, please contact Diogo R. Martins at <a href="mailto:diogo-martins-9@live.com.pt">diogo-martins-9@live.com.pt</a> or Sara M. Cerqueira at <a href="mailto:saracerqueira1996@gmail.com">saracerqueira1996@gmail.com</a>. </div>

opencc-by-4.0Dec 2023View details →

zenodo44/100

Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest

<div> <div> <div> This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios. The data were recorded using an Ergoneers Dikablis head-mounted eye tracker. Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a>   Version 2 is up-to-date. In Version 1: <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>

opencc-by-4.0Apr 2024View details →

zenodo44/100

RGB pixels VALUES FOR APPLES/LETTUCE AI OPTICAL RECOGNITION - 5 categories of Freshness

The Datasets include RGB color pallete per  pixel values for optical recognition on apples/lettuce and freshness categorized using AI Algorithm . Those Datasets are for AI Training projects . It will be used on the stage of creation, verification or optimization for new optical AI models. The tables can be used direclty on the AI tools, inserted and using the pixels colors number for every category. The freshness categories are 5, from the highest- crop day (5) to the lowest - not for eating (1).

opencc-by-4.0Nov 2024View details →

zenodo44/100

Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition

This repository contains the Wallhack1.8k dataset for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the CAD models (of 3D-printable parts) of the WiFi systems proposed in [2]. PyTroch Dataloader A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a> Dataset Description The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets). To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly: <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A. To extract the 52 L-LTF subcarriers used in [1], the following indices of A are to be selected: <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> Additional 56 HT-LTF subcarriers can be selected via: <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>. Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the spectrograms/ directory. The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label] <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> Dataset Overview: Table 1: Raw WiFi packet sequences. <table> <tbody> <tr> <td>Scenario</td> <td>System</td> <td>"no presence" /  label 0</td> <td>"walking"  / label 1</td> <td>"walking + arm-waving" /  label 2</td> <td>Total</td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td>44</td> </tr> </tbody> </table> Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k. <table> <tbody> <tr> <td>Scenario</td> <td>System</td> <td> "no presence" /  label 0 </td> <td> "walking"  / label 1 </td> <td>"walking + arm-waving" /  label 2</td> <td>Total</td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td>1,806</td> </tr> </tbody> </table>   Download and Use This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2]. [1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 42-56). Cham: Springer Nature Switzerland, doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>. [2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>. BibTeX citations: <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}} @INPROCEEDINGS{10647666,   author={Strohmayer, Julian and Kampel, Martin},   booktitle={2024 IEEE International Conference on Image Processing (ICIP)},    title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition},    year={2024},   volume={},   number={},   pages={3594-3599},   keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},   doi={10.1109/ICIP51287.2024.10647666}} </pre>

opencc-by-4.0Oct 2024View details →

zenodo44/100

Dataset: WiFi-based Human Activity Recognition using Raspberry Pi

This dataset contains 980 802.11 Channel State Information captures for 11 activities performed in a small apartment by 1 subject. For full description, check README.md.

openmit-licenseOct 2021View details →

zenodo44/100

Context-Aware Activity Recognition in Logistics (CAARL) – A optical marker-based Motion Capture Dataset

CAARL is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end). The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request. CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper: <ul> <li>Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: <a href="https://doi.org/10.5281/zenodo.3862782">10.5281/zenodo.3862782</a></li> <li>Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a></li> </ul>   If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: <a href="https://doi.org/10.3390/s22010134">10.3390/s22010134</a>

opencc-by-nc-4.0Nov 2021View details →

zenodo44/100

HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction

A thorough analysis of the existing human action recognition datasets demonstrates that only a few HRI datasets are available that target real-world applications, all of which are adapted to home settings. Therefore, given the shortage of datasets in industrial tasks, we aim to provide the community with a dataset created in a laboratory setting that includes actions commonly performed within manufacturing and service industries. In addition, the proposed dataset meets the requirements of deep learning algorithms for the development of intelligent learning models for action recognition and imitation in HRI applications.

opencc-by-4.0Jan 2022View details →

ScienceDex guides

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

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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