Data for a Fully 3D Modeling of Single-Phase Fluid Flow in Fractured-Vuggy Carbonate Formations Using the Transient Brinkman Equation Coupled with the Second-order Gradient Rock Mechanics Equation Including Porosity Evolution study
Transcranial alternating current stimulation (tACS) can affect perception, learning and cognition, but the underlying mechanisms are not well understood. A promising strategy to elucidate these mechanisms aims at applying tACS while electric or magnetic brain oscillations targeted by stimulation are recorded. However, reconstructing brain oscillations targeted by tACS remains a challenging problem due to stimulation artifacts. Besides lack of an established strategy to effectively supress such stimulation artifacts, there are also no resources available that allow for the development and testing of new and effective tACS artefact suppression algorithms, such as adaptive spatial filtering using beamforming or signal-space projection. Here, we provide a full dataset comprising encephalographic (EEG) recordings across six healthy human volunteers who underwent 10-Hz amplitude-modulated tACS (AM-tACS) during a 10-Hz steady-state visually evoked potential (SSVEP) paradigm. Moreover, data and scripts are provided related to the validation of a novel stimulation artefact suppression strategy, Stimulation Artifact Source Separation (SASS), removing EEG signal components that are maximally different in the presence versus absence of stimulation. Besides including EEG single-trial data and comparisons of 10-Hz brain oscillatory phase and amplitude recorded across three conditions (condition 1: no stimulation, condition 2: stimulation with SASS, condition 3: stimulation without SASS), also power spectra and topographies of SSVEP amplitudes across all three conditions are presented. Moreover, data is provided for assessing nonlinear modulations of the stimulation artifact in both time and frequency domains due to heartbeats. Finally, the dataset includes eigenvalue spectra and spatial patterns of signal components that were identified and removed by SASS for stimulation artefact suppression at the target frequency. Besides providing an valuable resource to assess properties of AM-tACS artifacts in the EEG, this dataset allows for testing different artifact rejection methods and offers in-depth insights into the workings of SASS.
The proposed dataset aims to benchmark the performance of SfM software under varying conditions - different environments, different lighting, image positions, camera setups, etc. Images of six objects are provided with varying shapes, sizes, surface textures and materials. The dataset is divided in two main parts, together with ReadMe files:
- Objects and environments data - images from each of the objects both from indoor and outdoor environments are provided.
- Capturing setups data - images from one of the objects are provided captured with different setups. Both with and without using a turntable, using one and multiple light sources and different amount of images
All images are captured using Canon 6D DSLR camera. All images contain EXIF data with used camera parameters. A ground truth high resolution scanned of each of the objects is provided for verifying the accuracy of the SfM reconstructions.
This data provides the detailed test results of the benchmarking for the binary-classification performance metrics. The benchmark comprising three stages was applied on 13 metrics namely True Positive Rate, True Negative Rate, Positive Predictive Value, Negative Predictive Value, Accuracy, Informedness, Markedness, Balanced Accuracy, G, Normalized Mutual Information, F1, Cohen’s Kappa, and Mathew’s Correlation Coefficient (MCC). The new benchmarking method is described in Gürol Canbek, Tugba Taskaya Temizel, and Seref Sagiroglu, "BenchMetrics: A Systematic Benchmarking Method for Binary-Classification Performance Metrics", Information Processing & Management, 2020 (Submitted).
The IR-QUMA study (Iranian Survey on Quality in Messenger Apps) is defined to evaluate the quality of some messenger apps.
A questionnaire was designed to evaluate some quality-related measures, metrics and features from users’ experience point of view. The questionnaire was published in popular channels of Iranian mobile social networks, in 10 different messengers (including Telegram, Whatsapp, Instagram, Eita, Soroush, Bale, Gap, IGap, Shaad and Rubika). More than 40 communities of users in these 10 messengers have contributed to this research questionnaire. Total of data is exceeds the level of 7k filled online forms. We hash the name of these messengers randomly by assigning ID-codes from M1 to M10.