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  • This a data about the corona virus COVID-19. It contains the actual reported data. Also, it includes the predicted COVID-19 data in the future based on a model developed to predict in the future. The model used will be published in one of the journals later and will be found on my profile with title "Optimistic Prediction Model For the COVID-19 Coronavirus Pandemic based on the Reported Data Analysis". The daily folder contains the daily data. The predicted folder contains the predicted data for each country. The total cases folder contains the total cases for each country. he section folder contains a latex code for plotting the figures for each country. Also the source file from European Centre for Disease Prevention and Control is included. More updated files available in the website of European Centre for Disease Prevention and Control.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
    • Text
  • The ground truth data used in this paper are obtained from three different areas to verify the effectiveness and robustness of the proposed methods. The detailed data information is described as follows: 1) The ground truth data from the Pecan Street dataset are collected from real households in the Muller project in Austin, TX, USA. Muller project funded by the U.S. Department of Energy and the U.S. National Science Foundation are located on the site of the Austin’s former municipal airport, close to central Austin.The selected homes in the project received monitoring equipment that captures electricity use on less than or equal to 1 min intervals for the whole home and 6 to 22 major appliances. Data over one year from August 2015 to July 2016 are analyzed, which contain the application-level and the whole-house energy consumption data. The corresponding 1-hour level temperature data are collected from the nearest Mueller weather station. We down-sample the energy consumption data to the 1-hour level to maintain consistency with the resolution of the temperature data. After data cleaning, customers without air conditioners or with missing readings are omitted, and the data of 119 residential customers are selected for accuracy analysis. 2) The ground truth data from smart home dataset are collected from real households in the Smart Home project in the Western Massachusetts, USA. The goal of this project is to optimize home energy consumption. The project involves several different types of dataset, including apartment dataset of 114 single-family, home dataset of 7 household and solar panel dataset, etc. However, the apartment dataset only contains the aggregated electrical data which can not be used to verify the accuracy of the load decomposition. Therefore, data over one year from January 2016 to December 2016 of home B and home G with individual ACLs monitor are selected for robustness analysis. 3) The ground truth data from low voltage distribution area are collected from low voltage distribution boxes in a developed city, Jiangsu province, China. Power and corresponding temperature data over one year from 2017 to 2018 are used for local DR programs. The dataset involves different distribution areas (i.e., different aggregated DR customers), including garment factory, hotel, rural neighborhoods, etc. However, the sub-meter data of all the ACLs are unavailable, thus it will only be used for aggregated DR potential analysis.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
    • Text
  • # Identifier Grammar Pattern Data This data includes six files. Five of these files contain 267 grammar patterns sampled from open source systems and the last file contains an agglomeration of data from the other five files. Each file has several columns: Identifier Type, Identifier Name, FINAL GRAMMAR PATTERN, POSSE, SWUM, STANFORD, system, language. Each of these is self-explanatory, but the function grammar patterns file contains one additional column: STANFORD_WITH_I, due to our use of the stanford+I technique for improving certain POS taggers. # Please cite the paper! 1. Christian D. Newman, Reem S. AlSuhaibani, Michael J. Decker, Anthony Peruma, Dishant Kaushik, Mohamed Wiem Mkaouer, Emily Hill, On the generation, structure, and semantics of grammar patterns in source code identifiers, Journal of Systems and Software, 2020, 110740, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2020.110740. (http://www.sciencedirect.com/science/article/pii/S0164121220301680) Keywords: Program comprehension; Identifier naming; Software maintenance; Source code analysis; Part-of-speech tagging # Feel free to contribute! We would love to keep this repository growing with more grammar patterns and are happy to credit anyone who decides to add to our current set here in the README (we will credit your name/username/webapge or whatever you like). If you'd like to contribute more grammar patterns, just make a pull request either adding your own file from a system we do not include above or modifying the .csv files already included. Just be sure to follow the format. # Interested in our research? **Check out https://scanl.org/**
    Data Types:
    • Other
    • Tabular Data
    • Dataset
    • Text
  • A large collection of synthetic, path-traced renderings for use in multi-view stereopsis and 3D reconstruction applications. The material properties are primarily non-Lambertian (reflective metals). Ground truth depth maps, model geometry, object masks and camera extrinsic and intrinsic data is provided together with the rendered images. A total of 18,000 samples are provided (45 camera views for 400 scenes), varying the illumination using five, high-definition HDR environemnt textures, four models (teapot, bunny, armadillo and dragon), ten material properties (bricks, brushed metal, ceramic, checkerboard, concrete, copper, grunge metal, marble, piano/ivory, and steel) and two camera forcal lengths (35 mm and 50 mm). File descriptions: + ShinySMVS_sample.jpg - a visual collage of 8 scenes, illustrating the variability introduced by using different models, illumination, material properties and camera focal lengths. + sample_armadillo1bricks50mm.zip - A single ShinySMVS scene sample for easy viewing and download (31Mb). + ShinySMVS_576p.tar.xz - The full ShinySMVS dataset containing all 400 scenes (768x576 pixel resolution). Note that tar.xz archive used due to file size limitations (8.6Gb). + PFMdemo.zip - Python example source code for loading ground truth depth map (PFM file format) as a numpy array. + modelSTL - Stereolithography (STL) file for the models used in the dataset.
    Data Types:
    • Image
    • Dataset
    • File Set
  • Ab initio calculation files
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
  • In the study, a high-performance controlled short-circuiting metal transfer process at a wire feed rate of 12 m/min for WAAM with an Al-Mg-Mn alloying system was developed. The arc current and voltage waveforms were studied by oscillograms processing and then modified to reduce energy input in comparison with a self-regulated gas metal arc welding process. The newly developed process was implemented in manufacturing of the sample parts at a travel speed of up to 150 cm/min. Modified waveforms in a combination with an increased travel speed led to a decrease in heat input, which appeared to be 16% lower than that of a conventional self-regulated process. Decreased heat input lead to an improvement of the geometry preservation stability at high process rates (up to 2.2 kg/h). The mechanical properties study showed that the elongation of the tensile samples was up to 41%, the increase in elongation was explained through macro- and microstructure analysis. The provided data shows remarkable tensile test results and remarkable macro- and microstructure images, which were used to evaluate porosity and grain size in the study of the deposited metal properties. High-speed video of a metal transfer during deposition using the newly developed process along with a high-speed video of a wire feeding process are also presented in this dataset.
    Data Types:
    • Image
    • Video
    • Dataset
  • This dataset contains accelerometer data collected by wearing Empatica E4 device. The onboard three-axis accelerometer was used to capture six motion-based activities (i.e. Washing Hands, Brushing Teeth, Brushing Hair, Dusting, Ironing and Washing Dishes) at 32 Hz for 5 minutes. A total of 36 signals were collected performing the activities without any instructions, in a real-world scenario, obtaining a realistic description of involved activities. The data files are organized in table format: -rows report the sample time; -columns show the GENDER, AGE, Acceleration X, Acceleration Y and Acceleration Z and ACTIVITY performed. Please read the README.txt for further details on all the features of Dataset. The main contribution of this dataset is the data collection getting out of the lab. This means that data includes the spontaneous conduction of daily behaviours in the living environment. This dataset can be suitable for several investigations, such as in computing science, measurement and instrumentations and healthcare context.
    Data Types:
    • Dataset
    • Text
    • File Set
  • Retinal blood vessel segmentation(Retinopathy of Prematurity).
    Data Types:
    • Software/Code
    • Image
    • Dataset
  • Differential Cross section for 1000 eV electrons in N2 gas determined in the paper: "Method to correct ambient pressure XPS for the distortion caused by the gas" by SvenTougaard and Mark Greiner published in Applied Surface Science 2020: https://doi.org/10.1016/j.apsusc.2020.147243 Data structure: Energy loss T (eV), IMFP*K(T) (eV^-1) (where IMFP is the inelastic mean free path)
    Data Types:
    • Dataset
    • Text
  • In 2018, a new Mercosur license plate standard was published, unifing the identification of vehicles and replacing the present license plate standards of five South America countries. In this new scenario, automatic license plate recognition (ALPR) systems built upon supervised learning algorithms could not be trained due to the lack of available data in real scenarios. So, in order to create a dataset without real samples of the new license plates standard, enabling the trainment of these models, a Mercosur license plate generator was developed to generate artificial license plates images with shadow, occlusion and other variations to mimetize real conditions, and a embeding system with license plate detection (LPD) that detects old 3-letter license plates in images of real scenarios and overwrite it with an artificially generated license plates. The dataset contains images of real scenarios where 3-letter license plates were detected using YOLOv3 (http://arxiv.org/abs/1804.02767) and overwritten by artificially generated images of the new mercosur license plates. It is organized in two folders: images - containing the images (JPEG) of the dataset; and labels - containing text files with the class identification number and the coordinates of the detected license plates in the image, following the Yolo_mark annotation specification (https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects). The images are separated in five classes, identified by a prefix in the filename: 1) monitoring_system_ - 2925 images acquired by a license plate detection model applied in a public videomonitoring system; 2) parking_lot1_ - 566 images of cars in a parking lot acquired using a smartphone camera; 3) parking_lot2_ containing 23 images of the same as parking_lot1_ but acquired using a tablet camera; 4) parking_lot3_ 11 images same as parking_lot1_ but acquired using a different smartphone model; and 5) cropped_parking_lot containing 315 images cropped in the license plate area from parking_lot_ images; Also, there is a CSV file listing all license plates detected in all images, organized in seven columns: image, label, class, x_center, y_center, width and height. The image column is the filename of the image containing the license plate, label is the filename of its recpective annotation, class is the class of the object (in this case, always zero, the index of the license plate object), and x_center, y_center, width and height, the coordinates of the rectangle of the set of pixels representing the license plate in the image, following YOLO annotation standard.
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Text