Find research datasets worth reusing

opencc-by-4.0May 2023View details →

NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection

NeSy4VRD NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the computer vision field of AI and, within that field, to the application tasks of visual relationship detection (VRD) and scene graph generation. Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind).   This is the sense in which we describe NeSy4VRD as being multipurpose: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives. The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.   NeSy4VRD on Zenodo: the NeSy4VRD dataset package This entry on Zenodo hosts the NeSy4VRD dataset package, which includes the NeSy4VRD dataset and its companion NeSy4VRD ontology, an OWL ontology called VRD-World. The NeSy4VRD dataset consists of an image dataset with associated visual relationship annotations. The images of the NeSy4VRD dataset are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations.  The NeSy4VRD dataset is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects.  A visual relationship for an image of the NeSy4VRD dataset has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them.  Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes.  For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>. Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation.  Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing. The NeSy4VRD ontology, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the NeSy4VRD dataset.  It directly describes the domain of the NeSy4VRD dataset, as reflected in the NeSy4VRD visual relationship annotations.  More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components.  There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning. Use of the NeSy4VRD ontology, VRD-World, in conjunction with the NeSy4VRD dataset is, of course, purely optional, however.  Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the NeSy4VRD ontology and use the NeSy4VRD dataset by itself. All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.   NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code The NeSy4VRD research resource incorporates additional components that are companions to the NeSy4VRD dataset package here on Zenodo.  These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of: <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the NeSy4VRD ontology, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the NeSy4VRD ontology, VRD-World, and RDF knowledge graphs.</li> </ul> The NeSy4VRD infrastructure supporting extensibility consists of: <ul> <li>open source Python code for conducting deep and comprehensive analyses of the NeSy4VRD dataset (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed NeSy4VRD protocol for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed NeSy4VRD workflow, implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the NeSy4VRD dataset in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests.  The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned. The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the NeSy4VRD ontology, VRD-World, in conjunction with the NeSy4VRD dataset. These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer.  If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained. To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between thing objects (that have well-defined shapes) and stuff objects (that are amorphous). Suppose a researcher wishes to have a greater number of stuff object classes with which to work.  Water is such a stuff object.  Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a Water class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following: <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the NeSy4VRD protocol to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the NeSy4VRD workflow to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional stuff object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul>   Information pertaining to the VRD dataset Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>.  Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021.  We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD. The original VRD visual relationship annotations are still publicly available from that location.  But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible.  They were also found to be replete with all manner of errors.  The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images.  The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion NeSy4VRD ontology, VRD-World, for those whose research requires such a companion ontology. Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the NeSy4VRD dataset on Zenodo, and access the VRD visual relationship annotations from the location in the link. A note of caution: the NeSy4VRD ontology, VRD-World, is not compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them.  The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names. However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.

opencc-by-4.0Oct 2023View details →

TAMPAR: Visual Tampering Detection for Parcels Logistics in Postal Supply Chains

TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our paper and for visual samples our <a href="https://a-nau.github.io/tampar/">project page</a>. Features are: <ul><li>>900 annotated real-world images with >2,700 visible parcel side surfaces</li><li>6 different tampering types</li><li>6 different distortion strengths</li></ul>Relevant computer vision tasks:<ul><li>bounding box detection</li><li>classification</li><li>instance segmentation</li><li>keypoint estimation</li><li>tampering detection and classification</li></ul>If you use this resource for scientific research, please consider citing our WACV 2024 <a href="https://arxiv.org/abs/2311.03124">paper</a> "TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains".

opencc-by-nc-sa-4.0Feb 2024View details →

The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection

See the official website: <a href="https://autovi.utc.fr">https://autovi.utc.fr</a> Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified. On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required. Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the Automotive Visual Inspection Dataset (AutoVI), the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines. The dataset contains 3950 images, split into 1530 training images and 2420 testing images. The evaluation code can be found at <a href="https://github.com/phcarval/autovi_evaluation_code">https://github.com/phcarval/autovi_evaluation_code</a>. Disclaimer All defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting. License Copyright © 2023-2024 Renault Group This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. For using the data in a way that falls under the commercial use clause of the license, please contact us. Attribution Please use the following for citing the dataset in scientific work: Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. <a href="https://doi.org/10.5281/zenodo.10459003">https://doi.org/10.5281/zenodo.10459003</a> Contact If you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr. Changelog <ul> <li>v1.0.0 <ul> <li>Cropped engine_wiring, pipe_clip and pipe_staple images</li> <li>Reduced tank_screw, underbody_pipes and underbody_screw image sizes</li> </ul> </li> <li>v0.1.1 <ul> <li>Added ground truth segmentation maps</li> <li>Fixed categorization of some images</li> <li>Added new defect categories</li> <li>Removed tube_fastening and kitting_cart</li> <li>Removed duplicates in pipe_clip</li> </ul> </li> </ul>

opencc-by-4.0Mar 2022View details →

Hazards&Robots: A Dataset for Visual Anomaly Detection in Robotics

This is the final version of our dataset; we further expand the Corridor scenario. This new version of Corridor includes 20 anomalies and the total frames are 324,408. In this version, we release feature embeddings extracted using a CLIP ViT-B/32 model. This dataset is part of a Data in Brief paper submission. For more information check https://github.com/idsia-robotics/hazard-detection

openmit-licenseJul 2022View details →

Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Oxford-IIIT Pet)

Preprocessed dataset for Oxford-IIIT Pet in YOLOv5 format..  Ground truth labels for head bounding boxes, body bounding boxes (derived from segmentation mask).

openmit-licenseJul 2022View details →

Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Tsinghua Dogs)

Preprocessed dataset for Tsinghua Dogs in YOLOv5 format..  Ground truth labels for head bounding boxes, body bounding boxes

opencc-by-4.0Jul 2017View details →

BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli

In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0o , 90o , 180o , 270o ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, Cz, P1, P2, Pz, PO3, PO4, PO7, PO8, POz, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2nd group, the 180-degree stimuli the 3rd group and the 270-degree stimuli the 4th group. Thus, it is made possible to test the developed features in more datasets.

opencc-by-4.0Dec 2017View details →

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"

opencc-by-4.0Dec 2017View details →

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"

opencc-by-4.0Jan 2018View details →

Visual Genome - Visual Relationship Detection - Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks

This repository contains a processed version of Visual Genome for Visual Relationship Detection, from the Diploma (MSc) thesis Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks by Miltiadis Kofinas, supervised by Christos Diou and Anastasios Delopoulos. The original thesis is written in Greek <blockquote> Νευρωνικά Δίκτυα Ανταλλαγή&sigmaf; Μηνυμάτων και Συνελικτικά Δίκτυα Γράφων για Εξαγωγή Γράφου Σκηνή&sigmaf; Εικόνων Μιλτιάδη&sigmaf; Κοφινά&sigmaf; <a href="https://ikee.lib.auth.gr/record/300900">https://ikee.lib.auth.gr/record/300900</a> </blockquote> A summarized English version of the thesis can be accessed <a href="https://www.dropbox.com/s/m87ixw8c8ecrswm/mkofinas_thesis_english_scene_graph_generation.pdf?dl=0">here</a>. It contains region proposals for VGG-16 for all images, and metadata about the bounding box distribution and the predicate classes.

dryad40/100

Data and code from: Prey detection by a stepwise visual template matching mechanism

Open the record for dataset details and reuse information.

publicNov 2024View details →

opencc-by-4.0Nov 2020View details →

Data Visualization of Weight Sensor and Event Detection of Aifi Store

<a href="https://www.aifi.com/">Aifi</a> Store is an autonomus store for cashier-less shopping experience which is achieved by multi modal sensing (Vision modality, weight modality and location modality). Aifi Nano store layout (Fig 1) (Image Credits: <a href="https://dl.acm.org/doi/10.1145/3360322.3361018">AIM3S</a> research paper). Overview: The store is organized in the gondola's and each gondola has shelfs that holds the products and each shelf has weight sensor plates. These weight sensor plates data is used to find the event trigger (pick up, put down or no event) from which we can find the weight of the product picked. Gondola is similar to vertical fixture consisting of horizontal shelfs in any normal store and in this case there are 5 to 6 shelfs in a Gondola. Every shelf again is composed of weight sensing plates, weight sensing modalities, there are around 12 plates on each shelf. Every plate has a sampling rate of **60Hz**, so there are 60 samples collected every second from each plate The pick up event on the plate can be observed and marked when the weight sensor reading decreases with time and increases with time when the put down event happens. Event Detection: The event is said to be detected if the moving variance calculated from the raw weight sensor reading exceeds a set threshold of (10000gm^2 or 0.01kg^2) over the sliding window length of 0.5 seconds, which is half of the sampling rate of sensors (i.e 1 second). There are 3 types of events: <ol> <li>Pick Up Event (Fig 2)= Object being taken from the particular gondola and shelf from the customer</li> <li>Put Down Event (Fig 3)= Object being placed back from the customer on that particular gondola and shelf</li> <li>No Event = (Fig 4)No object being picked up from that shelf</li> </ol> NOTE: <ol> <li>1.The python script must be in the same folder as of the weight.csv files and .csv files should not be placed in other subdirectories.</li> <li>2.The videos for the corresponding weight sensor data can be found in the "Videos folder" in the repository and are named similar to their corresponding ".csv" files.</li> <li>3.Each video files consists of video data from 13 different camera angles.</li> </ol> Details of the weight sensor files: These weight.csv (Baseline cases and team particular cases ) files are from the AIFI CPS IoT 2020 week.There are over 50 cases in total and each file has 5 columns (Fig 5) (timestamp, reading (in grams), gondola, shelf, plate number). Each of these files have data of around 2-5 minutes or 120 seconds in the form of timestamp. In order to unpack date and time from timestamp use datetime module from python. Details of the product.csv files: There are product.csv files for each test cases and these files provide the detailed information about the product name, product location (gondola number, shelf number and plate number) in the store, product weight(in grams), also link to the image of the product. Instruction to run the script: To start analysing the weigh.csv files using the python script and plot the timeseries plot for corresponding files. <ol> <li>Download the dataset.</li> <li>Make sure to place the python/ jupyter notebook file is in same directory as the .csv files.</li> <li>Install the requirements <code>$ pip3 install -r requirements.txt</code></li> <li>Run the python script Plot.py <code>$ python3 Plot.py</code></li> </ol> After the script has run successfully you will find the corresponding folders of weight.csv files which contain the figures (weight vs timestamp) in the format Instruction to run the Jupyter Notebook: Run the Plot.ipynb file using Jupyter Notebook by placing .csv files in the same directory as the Plot.ipynb script. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------                                                                          gondola_number,shelf_number.png                                                                             Ex: 1,1.png (Fig 4) (Timeseries Graph)

dryad36/100

Data from: Nest structure affects auditory and visual detectability, but not predation risk, in a tropical songbird community

1. Offspring mortality varies dramatically among species with critical demographic and evolutionary ramifications, yet the causes of this variation remain unclear. Nests are widely used for breeding across taxa and thought to influence offspring mortality risk. Traditionally, more complex, enclosed nest structures are thought to reduce offspring predation by reducing the visibility of nest contents and muffling offspring sounds compared to open nests. Direct tests of the functional bases for nest structure influences on predation risk are lacking. 2. We used experiments and 10 years of observational data to examine how nest structure influences nest predation risk in a diverse community of tropical songbirds. First, we examined how nest size was related to nest structure and nest predation rates across species. Second, we assessed how nest structure influences the detectability of nestling begging calls both in field and laboratory settings. Finally, we examined how the acoustic properties of different nest structures influence nest predation risk. Specifically, we experimentally broadcast begging calls from open and enclosed nests to determine how auditory cues and nest structure interact to affect predation on plasticine and quail eggs. We also tested whether nest structure was associated with differences in nest predation rates between the incubation (no begging cues) and nestling (begging cues) stages. 3. We found that enclosed nests are larger than open nests after accounting for adult size, and larger nests had increased predation rates. Moreover, enclosed nests did not consistently alter nestling begging calls in ways that reduce the likelihood of predation compared to open nests. Indeed, begging cues increased predation rates for enclosed but not open cup nests in our playback experiment, and nest predation rates showed greater increases after hatching in enclosed than open cup nests. 4. Ultimately, enclosed nests do not necessarily provide greater predation benefits than open nests in contrast to long standing theory.

opencc-zeroJul 2019View details →

opencc-by-4.0Feb 2022View details →

The Tensor Brain - Entity Level Visual Relationship Detection Challenge

This dataset contains images and annotations from real-world scenarios. It is aimed at testing a machine's perception performance in terms of object recognition, relationship detection, and instance and semantic memory retrieval.

opencc-by-4.0Oct 2024View details →

A Deep-Learning Approach for Visual Detection of an AUV Docking Station - Dataset

<div>This dataset was used to train the models from the paper "A Deep-Learning Approach for Visual Detection of an AUV Docking Station" published by Ahmad et al. at Oceans 2024 Conference in Halifax.</div> <div> </div> <div># Dataset</div> <div> </div> <div>The dataset contains:</div> <div> </div> <div>1. images from Abisko Lake in Sweden[1]</div> <div>2. images recorded in the Maritime Hall basin at DFKI</div> <div> </div> <div># File contents</div> <div>Each of these datasets are put into seperate directories. The images were annotated using CVAT[2].</div> <div> </div> <div>The dataset has been exported into the following formats:</div> <div> </div> <div>1. YOLO</div> <div>2. PascalVOC</div> <div>3. COCO</div> <div> </div> <div>The exported datasets does not contain raw images, rather they are places into a seperate zip folder.</div> <div> </div> <div> </div> <div>           </div> <div># References</div> <div>[1]: <a href="https://zenodo.org/records/7035132">https://zenodo.org/record/7035132#.ZDfKE5FBzJU</a></div> <div>[2]: https://www.cvat.ai/</div>

opencc-by-4.0Jun 2023View details →

Supplemental data for: Visualization of rank-citation curves for fast detection of possible manipulations with the h-index of the university

This dataset consists of papers of universities in the top 30 Scopus Ranking of Ukrainian Universities (May 2023). The data was obtained from Scopus using the search query "AF-ID (“university name”) AND PUBYEAR < 2023 AND PUBYEAR > 2002". Rank-citation curves were also generated for the publications of each university. In this analysis, the rank of publications was plotted along the horizontal axis, while the corresponding citation counts were depicted on the left axis. All types of documents were included in the dataset.

New Visual Acuity and Crowding Tests for Better Detection of Amblyopia

ClinicalTrials.gov study NCT03505606. IPD Sharing: NO. Countries: 1. Publications: 12.

Real-time Lymphatic Channel Visualization Improves Bilateral Sentinel Lymph Node Detection in Endometrial Cancer

ClinicalTrials.gov study NCT05191212. IPD Sharing: NO. Countries: 1. Publications: 3.

Detecting an Early Response to Donepezil With Measures of Visual Attention

ClinicalTrials.gov study NCT03073876. IPD Sharing: NO. Countries: 1. Publications: 1.

How Accurately Does the Diopsys Visual Evoked Potential (VEP) Vision Testing System Detect Glaucoma?

ClinicalTrials.gov study NCT02622178. IPD Sharing: NO. Countries: 1. Publications: 1.