Annotated image dataset of household objects from the RoboFEI@Home team

This data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.

The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.

Representative, normalized and flattened axial particle displacement fields of surface acoustic wave (SAW) propagating in in-vivo human skin at different sites used to generate Fig. 5 in Zhou's study "A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo".

Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately.

The dataset has Gaussian Blobs of varying samples, centers and features.  The number of samples ranges from 500 to 50,000. Similarly, the number of centers varies from 2 to 100, while the number of features varies from 2 to 2048. These different sets of Gaussian blobs can be used for testing clustering algorithms for their scalability and effectiveness. There are two kinds of files inside the compressed sets. Files ending with "_X.csv" consist of datapoints, while the files ending with "_y.csv" represent respective class data.

This India-specific COVID-19 tweets dataset has been curated using the large-scale Coronavirus (COVID-19) Tweets Dataset. This dataset contains tweets originating from India during the first week of each of the four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.

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EmoSurv is a dataset containing keystroke data along with emotion labels. Timing and frequency data is recorded while participants are typing free and fixed texts before and after being induced specific emotions. These emotions are: Anger, Happiness, Calmness, Sadness, and Neutral state.

First, data is collected while the participant is in a neutral state. Then, the participant watches an eliciting video. Once the emotion is induced in the participant, he types another fixed and free text.

This dataset contains facial expressions from different sides. the top-level videos are shot on Logitech c270 and the bottom ones are shot with an LG g6. The videos are continuous shots at 480p from different angles. 
This is meant to serve as a dataset for facial expression recognition under different angles and poses. 

An overview of a real-world Chinese mathematics dataset removed duplicated questions and simple questions.

dateset of Research on Optimization for LogGP Data Transmission Evaluation Model 

This dataset is a set of eighteen directed networks that represents message exchanges among Twitter accounts during eighteen crisis events. The dataset comprises 645,339 anonymized unique user IDs and 1,396,709 edges that are labeled with respect to Plutchik's basic emotions (anger, fear, sadness, disgust, joy, trust, anticipation, and surprise) or "neutral" (if a tweet conveys no emotion).

The simulated InSAR building dataset contains 312 simulated SAR image pairs generated from 39 different building models. Each building model is simulated at 8 viewing-angles. The sample number is 216 of the train set and is 96 of the test set. Each simulated InSAR sample contains three channels: master SAR image, slave SAR image, and interferometric phase image. This dataset serves the CVCMFF Net for building semantic segmentation of InSAR images.

The dataset is genrated by the fusion of three publicly available datasets: COVID-19 cxr image (https://github.com/ieee8023/covid-chestxray-dataset), Radiological Society of North America (RSNA) (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge), and U.S.  national  library  of  medicine  (USNLM) collected  Montgomery  country - NLM(MC) (http

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"The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface''. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural nondeformed ridge pattern itself.

There are two datasets: Drebin4000 and AMD6000.

When producing bolts in a cold forging process, the pressure signals are recorded per cycle of forming a bolt. The dataset is collected from experiments of different failure modes of a forming machine. Two experiments were recorded in csv format for providing four failure modes, including core broken, cavity block, insufficient lubrication, and material out-of-specification, as well as one normal mode. The two experiments were performed in the same machine with different cavities and cores, and saved in Experimental Data for Modeling and Testing.

This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:

Recently, Temporal Information Retrieval (TIR) has grabbed the major attention of the information retrieval community. TIR exploits the temporal dynamics in the information retrieval process and harnesses both textual relevance and temporal relevance to fulfill the temporal information requirements of a user Ur Rehman Khan et al., 2018. The focus time of document is an important temporal aspect which is defined as the time to which the content of the document refers Jatowt et al., 2015; Jatowt et al., 2013; Morbidoni et al., 2018, Khan et al., 2018.

Data for the study has been retrieved from a publicly available data set of a leading European P2P lending platform, Bondora (https://www.bondora.com/en). The retrieved data is a pool of both defaulted and non-defaulted loans from the time period between 1st March 2009 and 27th January 2020. The data comprises demographic and financial information of borrowers and loan transactions. In P2P lending, loans are typically uncollateralized and lenders seek higher returns as compensation for the financial risk they take.