Contributors: Ahmed Albasri
... Aim: This dataset aims to provide open access of raw EEG signal to the general public. We believe that such fusion of human moods (Relaxation & concentration) shall increase scientific transparency and efficiency, promote the validation of published methods, and foster the development of new algorithms. In addition, publishing research data is becoming more important as public funding agencies are moving towards open research data requirements. Scenario: The proposed scenario adapted to acquire the brain EEG signals in two different mental status. First while subjects in a relaxed mood, and second in concentration mood. Both of these cognitive stimuli considers as self-induced motivation. The recording period continues till three minutes for each session, as follows: -In the first minute, the subject is asked to relax and sit on a handed chair with eye open looking at a black screen computer of about 40cm far. Until hearing beep sound. -In the second minute, a random picture appear on the screen contain a question or some different objects. The subject is asked to solve the problem or to find common relation links all these objects together. -In last minute, the subject is asked to close his/her eyes and relax again until the beep sound. Sessions: Fore sessions were recorded for each subject. Such that, first two sessions are done on the same day with 1-2 hours interval, and remaining sessions are done after 2-3 days in the same way. The reason behind this separation is to avoid medium term influences that may subjects have. Each session continues for three minutes. The total recording time for each subject equal to 720 seconds. A small program designed to control the timing and recording procedure of the sessions. Numbering system: The numbering system is formatted to include both subject enrollment number and trials. First four characters represent the subject number, where last three characters represent the session record number. For example (S001E03) indicate 1st subject and 3rd recording session. Artifacts: In this experiment, we notice that some subjects accidentally generated internal artifacts. Therefore we intentionally continue recording their brain signals to provide more realistic condition to the experiment and also provide a role for the artifact removal techniques in the pre-processing phase. Data recording: EEG raw data recorded using EMOTIV EPOC+ device with 14 channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF42), plus References in the CMS/DRL noise cancellation configuration P3/P4 locations. The signals were sampled with 250 SPS. Sample space: The sample space consists of 30 participants (56.6% male and 43.3% female) with ages of 18-40 years. The subjects do not suffer(ing/ed) from any brain problems (mentally or physiologically). 33% of the subjects were smokers and 3% of them were alcoholics. All the subjects are well educated and have at least B.S degree.
Contributors: Igor Kudinov
... Supplementary material to the article containing the problem’s solution file with the solution algorithm, the final formula for the desired function, graphical results, and an assessment of the basic equation and uniqueness conditions discrepancies
Contributors: Lucio Fernandez-Arjona
... These are simulations of a Hull-White + GBM model (IR + equity), exact simulation on annual steps. Additionally, the dataset contains the cash flows of an insurance product corresponding to these simulations. For more details, look at the file dataset description.
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Contributors: Xiaoyu Wang
... 1.Full record and cited references from all articles and reviews published in Neuroaesthetics journals between 1991 and 2018 were exported as text files. We used the keyword“neuroaesthetics”to search the literatures. The information we choose including author data, keywords, citation data and abstract. 2.We imported the data from Web of Science Core Collection into a plain text, sent the text to the new created project named“Neuroaesthetics”in the CiteSpace. After the data was imported into CiteSpace, the network of neuroaesthetics core literatures was visually analyzed. For some related variable analysis, due to too many links, it is impossible to directly understand the visualization process. Thus, we made the cluster. 3.Co-Cited References Analysis reflect the dynamic changes in neuroaesthetics research fields Dual-Map Overlay Analysis The dual-map overlays can identifies the dynamics of previous study on the basis of the data set with cross-discilines. Burst Detection Analysis The function of burst detection searches for scientific features that have high intensities over limited temporal durations and capture the sharp increases in interest in a specific research field. Burst detection can present some topics which were actively discussed in neuroaesthetics for a time. The analysis of burst detection can find the emerging trends in the area of neuroaesthetics.
Data for: Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images
Contributors: Damian Matuszewski, Ida-Maria Sintorn
... The source code and results of the best U-Net-based model described in the paper.
Contributors: Frederik Tirsgaard, Casper Rosenhøj Enselmann
... The Camera Sled is an underwater ROV towed behind a boat. The Camera Sled is self-adjusting the height it keeps above the seafloor in order to monitor algae through images
Data for: New method for denoising borehole transient electromagnetic data with discrete wavelet transform
Contributors: Xueping Dai, Daniel Lemire, jean-claude mareschal, Chong Liu, li zhen cheng
... Data_10512 is generated using Loki, which is a 3D forward modeling program (Raiche et al., 2007). Sferics is from a field survey. the three *.raw files are borehole TEM data recorded by SmarTem24.
Common genetic variations associated with the persistence of immunity following childhood immunisation
Contributors: Daniel O'Connor
... Genotyping data of common genetic variations associated with the persistence of immunity following childhood immunisation
Data for: Thermodynamics, electronic structure and vibrational properties of Sn(n)[S(1-x)Se(x)](m) solid solutions for energy applications
Contributors: Jonathan Skelton, David Gunn, Lee Burton, Sebastian Metz, Stephen Parker
... This repository provides additional data to accompany the paper: "Thermodynamics, Electronic Structure, and Vibrational Properties of Sn(n)[S(1–x)Se(x)](m) Solid Solutions for Energy Applications" D. S. D. Gunn, J. M. Skelton, L. A. Burton, S. Metz and S. C. Parker Chemistry of Materials 31 (10), 3672-3685 (2019), DOI: 10.1021/acs.chemmater.9b00362 This article examines the properties of four solid-solution models: Pnma and rocksalt Sn[S,Se], Sn[S,Se](2) and Sn(2)[S,Se](3). This repository makes available a full set of data for all of the ~5,000 symmetry-unique structures across the four sets of calculations, including: * Optimised structures; * Calculated total energies and degeneracies; * Calculated bandgaps and partial density of states (PDoS) curves; * Simulated dielectric functions; and * Data from lattice-dynamics calculations on selected structures. In addition, the thermodynamically averaged pair-distribution functions, PDoS curves, dielectric functions, and structural-similarity analyses presented in the paper, calculated based on a 900 K formation temperature, are also provided. Finally, the repository also contains sample input files for the Vienna Ab initio Simulation Package (VASP) code. For details of how this data was generated, viewers are referred to the published article and supporting information. Brief details of file formats and links to further documentation are given in the included README file.
Contributors: Andrew Latimer, Yunlu Zhu, Sarah Kucenas
... RNA-sequencing of neural crest cells collected from 36 and 72 hpf Gt(foxd3:mCherry);Tg(sox10:mEGFP) embryos