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zenodo48/100

LYON19- Lymphocyte Detection Test Set

LYON19 The provided test set includes 441 ROIs saved in the .png files, and it is a test set of LYON grand challenge: <a href="https://lyon19.grand-challenge.org/">https://lyon19.grand-challenge.org</a>   Data Description The test set contains Region of Interests (ROIs) selected from whole-slide images (WSI) of immunohistochemistry (IHC) stained specimens of breast, colon and prostate. Data came from eight different medical centers in the Netherlands. All slides were stained with an antibody against CD3 or CD8. Slides were subsequently digitized with a Pannoramic 250Flash II scanner (3DHistech, Hungary), resulting in WSIs with a spatial resolution of 0.24μm/px. Selected ROIs were saved with full resolution in the .png files.   Selected ROIs were representative for most different types of lymphocyte distributions that occur in slides, namely (1) area with regular lymphocyte distribution, (2) clustered cells, and (3) staining or tissue artifacts.   Citation: Please reference the following paper if you use LYON19 data for a scientific publication: Swiderska-Chadaj, Zaneta, et al. "Learning to detect lymphocytes in immunohistochemistry with deep learning." Medical Image Analysis (2019): 101547. Link to the paper: <a href="https://www.sciencedirect.com/science/article/pii/S1361841519300829">https://www.sciencedirect.com/science/article/pii/S1361841519300829</a>

opencc-by-4.0Sep 2019View details →

zenodo48/100

Benchmark for the Evaluation of Lexical Semantic Change Detection for Ancient Greek

This repository contains a benchmark of Ancient Greek lemmas which underwent semantic change. It is meant as a support for the evaluation of methods detecting lexical semantic change in Ancient Greek. It was created at the University of Groningen, The Netherlands. A publication will follow soon.   1. Overview of the repository This benchmark was created by retrieving and selecting from existing scholarship cases of lexemes which underwent semantic change. The evaluation items are 44 Ancient Greek lemmas, accompanied by the following information (see the column headers in the CSV file): <ul> <li>reference: the literature source of information about the change;</li> <li>which_change: an explanation of the change in meaning. NB: the older meaning(s) do not necessarily disappear after the change, but it can happen that the new meaning(s) are added to the existing one(s), increasing the polysemy of the lemma;</li> <li>when_changed: information about the work(s) or time period in which the change was first recorded; this kind of information was not always available or precise;</li> <li>christian_change: whether the change is triggered by social, religious, or cultural changes related to the spread of Christianity, according to the scholarship.</li> </ul>   2. References The literature used to build this benchmark is the following:     BUCK, Carl Darling. A dictionary of selected synonyms in the principal Indo-European languages. University of Chicago Press, 1949.     FINKELBERG, Aryeh. "On the History of the Greek ΚΟΣΜΟΣ." Harvard Studies in Classical Philology (1998): 103-136.     GINGRICH, F. Wilbur. "The Greek New Testament as a landmark in the course of semantic change." Journal of Biblical Literature (1954): 189-196.     HORKY, Phillip Sidney. "When did Kosmos become the Kosmos." Cosmos in the Ancient World (2019): 22-41.     LURAGHI, Silvia. "The verb aréskein in Ancient Greek: Constructions and semantic change." Acta Linguistica Petropolitana. Труды института лингвистических исследований 18-1 (2022): 226-245.   These dictionaries of Ancient Greek were also used to double-check the instances of change:     LIDDELL, Henry George, and Robert Scott. A Greek-English Lexicon. revised and augmented throughout by. Sir Henry Stuart Jones. with the assistance of. Roderick McKenzie. Oxford. Clarendon Press. 1940.     ROCCI, Lorenzo. Vocabolario greco-italiano. Roma. Società editrice Dante Alighieri. 1939.     SLUITER, Ineke, and Lucien van Beek, and Ton Kessels, and Albert Rijksbaron. Woordenboek Grieks/Nederlands. 2024. <a href="https://woordenboekgrieks.nl/" target="_blank" rel="noopener">https://woordenboekgrieks.nl/</a>   3. Acknowledgements <div>This work was partially supported by the Young Academy Groningen through the PhD scholarship of Silvia Stopponi.   We acknowledge the financial support of Anchoring Innovation. Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012). For more information about the research programme and its results, see the website <a href="https://www.anchoringinnovation.nl/">www.anchoringinnovation.nl</a>.</div> <div>   4. How to cite </div> <div>Until there is no publication about this benchmark, please cite the resource as:</div> <div>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim (2024), Benchmark for the Evaluation of Lexical Semantic Change Detection Measures in Ancient Greek, DOI: 10.5281/zenodo.13364555.</div> <div> </div> <div> </div>

opencc-by-4.0Aug 2024View details →

zenodo48/100

Three Annotated Anomaly Detection Datasets for Line-Scan Algorithms

<h1>Summary</h1> This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task. They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD <h1>How to Get Started</h1> All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is: <pre><code>import numpy as np # Load image file hsi_array = np.load("data/beach_hsi.npy") n_pixels, n_lines, n_bands = hsi_array.shape print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.") # Load image mask mask_array = np.load("data/beach_mask.npy") m_pixels, m_lines = mask_array.shape print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")</code></pre> <h1>Citing the Datasets</h1> If you use any of these datasets, please cite the following paper: <pre><code>@article{garske2024erx,</code> <code>  title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},</code> <code>  author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},</code> <code>  journal={arXiv preprint arXiv:2408.14947},</code> <code>  year={2024},</code> <code>}</code></pre> <div> <pre>If you use the beach dataset please cite the following paper as well (original source):</pre> </div> <pre><code>@article{mao2022openhsi, title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone}, author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy}, journal={Remote Sensing}, volume={14}, number={9}, pages={2244}, year={2022}, publisher={MDPI} }</code></pre>

opencc-by-4.0Aug 2024View details →

zenodo48/100

PEDRo: an Event-based Dataset for Person Detection in Robotics

This event-based dataset is specifically designed for person detection in robotics applications.

opencc-by-4.0Apr 2023View 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

Efficient Detection of Test Interference in C Projects (Artifact)

This record provides research artifacts for the article "Efficient Detection of Test Interference in C Projects", accepted and to be presented at <a href="https://conf.researchr.org/home/ase-2024">ASE 2024</a>. Please refer to the README.md in the tgz file for details about the artifact and how it relates to the manuscript describing our study. Please also see our related Zenodo record with the container images used in the study: <a href="https://doi.org/10.5281/zenodo.7935821" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7935821</a>.

openbsd-3-clauseSep 2024View details →

zenodo48/100

Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"

Images are from the LROC-NAC and have been cartographically controlled to each other and the Apollo 11 landing site as described in Supporting Information Text S1.  Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures.  The images are provided as .png files with .pgw ("PNG World").  The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name).  Additionally, the three e > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest. Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher.  File names have the exact same name as the image file that was used to map crater data, with two differences:  The initials of the author are appended, and the file extension is "csv" instead of "png".  The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers).  Crater data are entirely in one .zip file.

opencc-by-4.0Sep 2024View details →

zenodo48/100

Joint Microseismic Event Detection and Location with a Detection Transformer

Synthetic passive seismic data and their corresponding labels used for training and testing the network in the paper "Joint Microseismic Event Detection and Location with a Detection Transformer".

opencc-by-4.0Jul 2023View details →

zenodo48/100

Is there a non-invasive biomarker for the early detection of ovarian torsion? A systematic review and meta-analysis

We have performed a systematic review and meta-analysis and identified multiple biomarkers that warrant further study as part of a broader diagnostic panel for ovarian torsion. These include SCUBE1, s-DD, IL-6, IMA and TNF-a.

opencc-by-4.0Nov 2023View details →

zenodo48/100

Software vulnerability detection datasets - function/method level

This dataset is for software vulnerability detection and includes source code in eight programming languages (C, C++, Java, JavaScript, Go, PHP, Ruby, Python). All data is collected from GitHub.data{programming language}_vul.json: a set of vulnerable code samples in a certain programming language.data{programming language}_patch.json: a set of patching code samples in a certain programming language. Each source code sample includes the following 16 properties: index: index of code. If is_vulnerable==False, this index indicates that this code is a patch of the indexing vulnerable code.code: raw source code (may include comments).is_vulnerable: the code is vulnerable (True) or a patch (False).programming_language: programming language of the code.method_name: name of the method.file_name: name of the file where the source code is extracted.repo_url: url of the project repository.repo_owner: owner of the repository.committer: developer who pushed the commit.committer_date: date when the commit was pushed.commit_msg: the commit message.cwe_id: If is_vulnerable==True, the CWE id; otherwise None.cwe_name: If is_vulnerable==True, the name of corresponding CWE; otherwise None.cwe_description: If is_vulnerable==True, the description of corresponding CWE; otherwise None.cwe_url: If is_vulnerable==True, the url to obtain more details of corresponding CWE; otherwise None.cve_id: If is_vulnerable==True, the CVE id; otherwise None.

openmit-licenseSep 2024View details →

zenodo48/100

Nephrops (Nephrops norvegicus) Burrow object detection simple training dataset from Irish Underwater TV surveys

<div> <div> <div> <div> <h1>Training dataset</h1> Norway prawns (Nephrops norvegicus), also known as the Dublin Bay prawn, are common around the Irish coast. They are found in distinct sandy/muddy areas where the sediment is suitable for them to construct their burrows. Nephrops spend a great deal of time in their burrows and their emergence from these is related to time of year, light intensity and tidal strength. The Irish Nephrops fishery is extremely valuable with landings recently worth around €55m at first sale, supporting an important Irish fishing industry.  Nephrops are managed in Functional Units (FUs). The Marine Institute has conducted under water television surveys since 2002 to independently estimate abundance, distribution and stock sizes of Nephrops norvegicus for: <ul> <li>Irish Sea Nephrops Grounds (FU 14 and 15) in collaboration with <a title="Link to 'Fisheries and Aquatic Ecosystems' work in AFBI Northern Ireland" href="https://www.afbini.gov.uk/area-of-expertise/fisheries-and-aquatic-ecosystems">AFBI</a> an <a title="Link to Cefas (the Centre for Environment, Fisheries, and Aquaculture Science) in the UK" href="https://www.cefas.co.uk/">CEFAS</a>.</li> <li>Porcupine Bank Nephrops Grounds (FU16)</li> <li>Aran, Galway Bay and Slyne Head Nephrops Grounds (FU17)</li> <li>South and South west Ireland Nephrops Grounds (FU19)</li> <li>Labadie, Jones and Cockburn Nephrops Grounds (FU20 and 21)</li> <li>“Smalls” Nephrops Grounds (FU22)</li> </ul> Each year during the summer months, on average 300 stations are surveyed each year, in three survey legs, covering all the FUs in depths from 20 to 650 metres. A high definition camera system is towed over the sea bed for 10 minutes travelling approx. 200m at 0.8 knots on a purpose built sledge. The UWTV survey follows survey protocols available <a title="Link to survey protocols" href="https://doi.org/10.17895/ices.pub.8014">here</a> agreed by International Council for the Exploration of the Sea (ICES) Working Group on Nephrops surveys (WGNEPS).  As part of the iMagine project a selection of images from the Underwater TV survey Functional Units were annotated with bounding boxes and labels in YOLOv8 format to train an YOLOv8 Object Detection Models. The training dataset is saved in YOLOv8 format.  It is intended to train a YOLOv8 Nephrrops burrow object detection model to assess the utility of an Object Detection model is assisting Prawn Survey work in the semi automated annotation of prawn burrow imagery. </div> </div> </div> </div>

opencc-by-4.0Jul 2024View details →

zenodo48/100

Dataset for Website Personality Detection

This dataset supports research on identifying the personality of websites. It contains data from 3,000 websites, covering five distinct website categories, and provides quantitative elements extracted from these sites. Additionally, the dataset includes information about the selected website categories, as well as details on website personality "Facets" and "Items." The dataset is accompanied by survey results related to the research.

opencc-by-4.0Nov 2024View details →

zenodo48/100

Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset

This dataset was published with the dataset descriptor "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset". ACKNOWLEDGEMENTS The project ”ZUKIPRO” is funded as part of the ”Future Centers” program by the Federal Ministry of Labour and Social Affairs and the European Union through the European Social Fund Plus (ESF Plus).Roles and Contributions.

opencc-by-4.0Feb 2024View details →

zenodo48/100

Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023

Bacteria of the genus Salmonella pose a major risk to livestock, the food economy, and public health. Salmonella infections are one of the leading causes of food poisoning. The identification of serovars of Salmonella achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by in silico serotyping has been established as an alternative method for serotyping and the detection of genetic markers for Salmonella. Until now, WGS data generated with Illumina sequencing are used to validate in silico serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 Salmonella strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the in silico serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, in silico serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for Salmonella in silico serotyping and genetic marker detection.

opencc-by-4.0Feb 2023View details →

zenodo48/100

OpenForensics: Multi-Face Forgery Detection And Segmentation In-The-Wild Dataset [V.1.0.0]

OpenForensics is the first large-scale dataset posing a high level of challenges. This dataset is designed with face-wise rich annotations explicitly for face forgery detection and segmentation. With its rich annotations, OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. Project Page:  https://sites.google.com/view/ltnghia/research/openforensics

opencc-by-4.0Oct 2021View details →

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River Feshie, Scotland - Geomorphic Change Detection - Example Dataset

A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection from five years of repeat monitoring of the Feshie from 2003 to 2007. Used in Tutorials (e.g. <a href="https://gcd.riverscapes.net/Tutorials/ChangeDetection/GCDwithFIS.html">FIS Error Modelling</a>) and appears in: <ol> <li>Wheaton JM, Brasington J, Darby SE, Sear DA, Vericat D&Dagger;., and Kasprak A*. 2013. <a href="https://www.researchgate.net/publication/242653748_Morphodynamic_signatures_of_braiding_mechanisms_as_expressed_through_change_in_sediment_storage_in_a_gravel-bed_river">Morphodynamic signatures of braiding mechanisms as expressed through change in sediment storage in a gravel-bed river</a>. Journal of Geophysical Research - Earth Surface. DOI: <a href="http://dx.doi.org/10.1002/jgrf.20060">10.1002/jgrf.20060</a>.</li> <li>Wheaton JM, Brasington J, Darby SE, Merz JE, Pasternack GB, Sear DA and Vericat D&Dagger;. 2010. <a href="https://www.researchgate.net/publication/227526758_Linking_Geomorphic_changes_to_Salmonid_habitat_at_a_scale_relevant_to_fish">Linking Geomorphic Changes to Salmonid Habitat at a Scale Relevant to Fish. River Research and Applications</a>.26: 469-486. DOI: <a href="http://dx.doi.org/10.1002/rra.1305">10.1002/rra.1305</a>.</li> </ol> . Dataset is from: <ul> <li>700m braided gravel bed river in the  <a href="https://www.google.com/maps/place/57%C2%B000'41.4%22N+3%C2%B054'16.1%22W/@57.0099348,-3.9000104,6821m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d57.01149!4d-3.90446">Scottish Cairngorm mountains</a>.</li> <li>5 annual surveys</li> <li>Mix of RTKGPS and Total Station</li> <li>1m cell resolution</li> </ul> Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened.

opencc-by-4.0May 2018View details →

zenodo48/100

Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the dataset used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case. This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Solutions and Genetic algorithm dataset of the Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )

This dataset contains the solution of the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case (link). The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside, one can find: -One archive, "GA_Scenario_Solution_Dataset.zip", containing 10 couple of files (so 20 files). Each couple of file "sol_X_1.csv" and "sols_X_1.csv" are reciprocally the solutino given by the Genetic Algorithm to scenario X, and all the candidate solution explroed by the GA while solving scenario X. This archive also contain other versions of the solutions made by the GA with other parameters. -One archive, "GA_Toy_Dataset.zip" , containing solution to random scenarios, used to develop the first interfaces. Those solutions are used to developp the heatmatrix and heatmaps of the project (link), and visualisations for the validation (link).

opencc-by-4.0Dec 2021View details →

zenodo48/100

Heatmatrix and Heatmap Layers and Alternatives used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the visualisations of the solutions of Conflict Detection and Resolution (CD&R) use case. The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside, one can find: -One archive, "Heatmatrix.zip" , containing the heatmatrix creating using the solutions dataset. -One archive, "Heatmaps_Layer_Alternatives.zip", containing all the layers created and used to created the heatmaps, the heatmaps, and alternative heatmaps (with other candidate solutions). Those layers and heatmaps are used to develop other visualisation used in the validation.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Validation Videos and eXplainable levels used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the explaination levels and the video used in the validation of the Conflict Detection and Resolution (CD&R) use case. The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside the dataset, one can find: -One archive, " Validation_Videos_Traffic.zip ", containing the video of traffic of every scenario. -One archive, " Validation_XAI_levels.zip", containing the Blackbox, Heatmap, and Storyboard eXplainable levels for each scenario. The videos and XAI levels are used in the validation exercice.

opencc-by-4.0Dec 2021View details →

ScienceDex guides

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

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record