Data in Brief

ISSN: 2352-3409

Visit Journal website

Datasets associated with articles published in Data in Brief

Filter Results
81 results
  • Dataset general description: • This dataset reports 4200 recurrent neural network models, their settings, and their relevant generated files (including prediction csv files, graphs, and metadata files, as applicable), for predicting COVID-19's daily infections in Brazil by training on limited raw data (30 and 40 time-steps). The used code is developed by the author and located in the following online data repository link: http://dx.doi.org/10.17632/yp4d95pk7n.3 Dataset content: • Models, Graphs, and csv predictions files: 1. Deterministic mode (DM): includes 1197 generated models' files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated models' files (40 time-steps), and their generated 7301 graphs and 7301 predictions files. 2. Non-deterministic mode (NDM): includes 20 generated models' files (30 time-steps), and their generated 53 graphs and 53 predictions files. 3. Technical validation mode (TVM): includes 1001 generated models' files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 349 models (out of a 358 sample but 9 models didn't achieve the accuracy threshold), which are a sample of 1001 models. Also, all data of the control group - India (1 model). 4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11. 5. The evaluation of performance for 10, 20, 30, 40, and 50 time-steps alternatives (5 models). • Settings and metadata for the above 3 categories: 1. Used settings during the training session in json files. 2. Metadata: training / prediction setup and accuracy in csv files. Raw data source used to train the models: • The used raw data [1] for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University) : https://github.com/CSSEGISandData/COVID-19 (accessed 2020-07-20) • The following raw data links were used (both accessed 2020-07-08): 1. till 2020-06-29: https://github.com/CSSEGISandData/COVID-19/raw/78d91b2dbc2a26eb2b2101fa499c6798aa22fca8/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv 2. till 2020-06-13: https://github.com/CSSEGISandData/COVID-19/raw/02ea750a263f6d8b8945fdd3253b35d3fd9b1bee/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv References: 1- Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
    • Text
    • File Set
  • General description: - This dataset comprises a Jupyter notebook that includes a Python code for sequence-to-sequence time-series forecasting by training and evaluating recurrent neural network models. - The code was developed to enable rapid and wide-scale development, production and evaluation of time-series models and predictions. - The RNN's architecture has a convolutional layer for handling inputs, within a composite autoencoder’s neural network. Instructions for usage: - The Python code is located in a Jupyter notebook that can be opened online or locally, by using a Jupyter Notebook compatible platform as: https://jupyter.org (accessed 11 July 2020). https://colab.research.google.com (accessed 11 July 2020). - In order to use the code, a data source should exist in a "csv" file extension and it should be named as 'data_input.csv' or alternatively, an online link to the data source could be entered when executing the code. The data source should have first 4 columns for metadata. The unique name or identifier for each row will be located in the 2nd column, otherwise, a change has to be made in the code in the gen_data function (line 282) and line 286 in case of the need to change metadata columns size, into less or more. The rest of the columns indicate the accumulated number or value in each column. Important parameters: - target_pred: specifies which row in the data to predict. - crop_point: specifies which data point to crop the time-series data at, ex. training data = before crop_point, evaluation data = after crop_point. - time_steps: specifies which time-steps to use, ex. 15 or 20, meaning: 15 for X and 15 for Y in the sequence-to-sequence model. - RNN parameters: ex. batch size, epochs, layer sizes, RNN architecture (GRU or LSTM). - ext: specifies the end date of predictions.
    Data Types:
    • Software/Code
    • Dataset
  • This dataset contains Wi-Fi signals that were recorded from 40 different pairs of subjects while performing twelve different human-to-human interactions in an indoor environment. Each pair of subjects performed ten trials of each of the twelve interactions and the total number of trials recorded in our dataset for all the 40 pairs of subjects is 4800 trials (i.e., 40 pairs of subjects × 12 interactions × 10 trials). The publicly available CSI tool is used to record the Wi-Fi signals transmitted from a commercial off-the-shelf access point, namely the Sagemcom 2704 access point, to a desktop computer that is equipped with an Intel 5300 network interface card. The recorded Wi-Fi signals consist of the Received Signal Strength Indicator (RSSI) values and the Channel State Information (CSI) values.
    Data Types:
    • Dataset
    • File Set
  • In the current data article, we present detailed characteristics of voids in carbon/epoxy composite laminates as well as the original image stacks, obtained via X-ray micro-Computed Tomography (micro-CT) . Five different lay-ups are produced with altering the recommended cure cycle in order to intentionally induce voids in the material. For each lay-up, an image stack (consisting of tomographic slices) and a dataset are provided. The image slices are in 8-bit TIF format. The datasets (spreadsheets) include the volume, size parameters, shape parameters, orientation, and location of all the detected voids in the specimen. The segmentation of the images and quantification of voids are performed in VoxTex, an in-house software for processing of micro-CT results. The data is linked to a Data in Brief article "Mehdikhani et al., A dataset of voids’ characteristics in multidirectional carbon fiber/epoxy composite laminates, obtained using X-ray micro-computed tomography, DIB 27 (2019) 104686" and linked to the article "Mehdikhani et al. Detailed characterization of voids in multidirectional carbon fiber/epoxy composite laminates using X-ray micro-computed tomography. Comp Part A 125 (2019) 105532".
    Data Types:
    • Dataset
    • File Set
  • In the current data article, we present detailed characteristics of voids in carbon/epoxy composite laminates as well as the original image stacks, obtained via X-ray micro-Computed Tomography (micro-CT) . Five different lay-ups are produced with altering the recommended cure cycle in order to intentionally induce voids in the material. For each lay-up, an image stack (consisting of tomographic slices) and a dataset are provided. The image slices are in 8-bit TIF format. The datasets (spreadsheets) include the volume, size parameters, shape parameters, orientation, and location of all the detected voids in the specimen. The segmentation of the images and quantification of voids are performed in VoxTex, an in-house software for processing of micro-CT results. The data is linked to a Data in Brief article "A dataset of voids’ characteristics in multidirectional carbon fiber/epoxy composite laminates, obtained using X-ray micro-computed tomography" and linked to the article "Mehdikhani et al. Detailed characterization of voids in multidirectional carbon fiber/epoxy composite laminates using X-ray micro-computed tomography. Comp Part A. in press.".
    Data Types:
    • Dataset
    • File Set
  • This dataset represents face experience coded frame-by-frame from nearly 170 hours of infant-perspective head-mounted-camera video, recorded during their daily life by 40 3-month-old infants. It includes information about the identity of the face (e.g., caregiver, relative), length of time the face was in the field of view, location in which the face occurred, and descriptions of the situation in which the infant experienced the face. Demographic information (e.g., age, gender) about the infants who recorded the videos is also provided. For elaboration on data collection methodology, interpretation, analysis, and discussion of early face experience captured by this dataset, please see our paper These are the people in your neighbourhood: Consistency and persistence in infants’ exposure to caregivers’, relatives’, and strangers’ faces across contexts [1].
    Data Types:
    • Tabular Data
    • Dataset
  • The dataset consisted of raw and analysed data over airport location in Europe, Larger Urban Zones (LUZ), land cover, road network betweenness, and airport typologies - obtained by the employment of hierarchical cluster analysis over the kernel density of the aboved-mentioned datasets.
    Data Types:
    • Dataset
    • File Set
  • All raw data has been presented in tables.
    Data Types:
    • Dataset
    • Document
  • The experimental data reported in this article presents radio frequency (RF) measurements and evaluation of key performance indicators (KPIs) of a commercial Fourth Generation (4G) Long Term Evolution (LTE) network.
    Data Types:
    • Tabular Data
    • Dataset
  • Data was collected through multi stage sampling technique
    Data Types:
    • Software/Code
    • Dataset
    • Document
1