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This dataset provides a comprehensive collection of vibrational data for the purpose of structural health monitoring, particularly focusing on the detection of bolt loosening in offshore wind turbine jacket foundations. The data set comprises 780 comma-separated values (CSV) files, each corresponding to specific experimental conditions, including various structural states of the wind turbine's support structure. These states are systematically varied considering three main aspects: the amplitude of a white noise (WN) signal, the type of bolt damage, and the level at which damage has occurred. The data were meticulously collected using eight triaxial accelerometers (PCB R Piezotronic model 356A17), strategically placed at different locations on a scaled-down replica of an offshore jacket-type wind turbine. This setup facilitated the acquisition of detailed vibrational data through a National Instruments' data acquisition (DAQ) system, comprising six input modules (NI 9234 model) housed in a chassis (cDAQ model). The white noise signal, simulating wind disturbance at the nacelle, was produced by a modal shaker and varied in three amplitudes (0.5, 1, and 2), directly proportional to the induced vibration in the wind turbine. The dataset uniquely captures the vibrational behaviour under different scenarios of bolt loosening in the turbine's foundation. The conditions include a healthy state (bolts tightened to 12 Nm) and various degrees of loosening (bolts loosened to 9 Nm, 6 Nm, and completely absent), examined at four distinct levels of the turbine's base structure. This granular approach offers a nuanced view of how varying degrees of bolt loosening impact the vibrational characteristics of the structure. The value of this dataset lies in its potential for wide-ranging applications in the field of structural health monitoring. Researchers and engineers can leverage this data for developing and testing new methodologies for early damage detection and progressive damage assessment in offshore wind turbines. The dataset's comprehensive coverage of damage scenarios makes it a valuable resource for the validation and enhancement of existing damage detection algorithms. Furthermore, the dataset can serve as a benchmark for comparing the efficacy of different vibrational analysis techniques in the context of wind turbine maintenance and safety. Its application is not only limited to wind turbines but can extend to other structures where bolt integrity is critical for operational safety. This dataset represents a significant contribution to the field of structural health monitoring, providing a detailed and practical resource for enhancing the reliability and safety of offshore wind turbines and similar structures., (© 2024 The Author(s).)

Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition., (© 2023 The Author(s).)

The data collected in this article is based on a performance test of a polymer electrolyte fuel cell (PEFC). The behavior the different parameters of a PEFC is analyzed considering different aspects relative to the inlet gases temperatures. The fuel cell was evaluated by means of a current sweep at different percentages of relative humidity between the feed gas and the cell. The relative humidity values were established by means of the temperature setting. The data presented show the experimental response of the cell in real time, which can be used to perform a depth analysis or they can be a starting point for material and performance investigation. In addition, charts presenting the voltage and power density behavior as a function of the volumetric flows of the anode (H2) as well as cathode (O2). The data presented in this article are originally from our research performed in [1]., (© 2019 The Author(s).)

Analyzing the fluid behavior in complex porous media like gas diffusion layers (GDLs) in polymer electrolyte fuel cells (PEFCs) can be accurately done using the lattice Boltzmann method (LBM). This article shows the data obtained from a study in which diffusion parameters such as porosity, gas phase tortuosity and diffusibility are computed considering simulated porous media [1]. The data were computed when a water drop obstacle is placed inside the GDL domain and the size of the water-drop is varied. Additionally, figures showing the evolution of the flow velocity field are presented alongside graphics that presents the change in local and bulk porosity for each obstacle size. Finally, there is a detailed method explanation concerning the implementation of the lattice Boltzmann method and a general description of computational codes for the domain and obstacle generation as well as the boundary conditions simulation. Data and processes in this article can be exploited in new attempts to solve real case problems in complex mesoscale media., (© 2019 The Authors.)

Two subtractive cDNA libraries from banana leaves (cultivar 'Williams', genotype AAA) after biostimulant application on the leaf (library 1) or the substrate (library 2), with Pseudocercospora fijiensis infection were generated. The banana plants were applied first with the biostimulant and later the inoculation of P. fijiensis was performed on the leaves after one week. The suppression subtractive hybridization was performed by using as tester the treatments with biostimulant application by sampling banana leaves after two weeks of P. fijiensis inoculation, and every two weeks for two months (four time points); while the driver were collected on the same dates on independent banana plants that were only inoculated with P. fijiensis (no biostimulant application). The plants were maintained in the greenhouse for the entire assay., (© 2019 The Author(s).)

The aim of the study is to identify the factors that influence innovation activities associated with business management, known in the academic world as organizational innovation. Data was gathered by administering a survey on the managers or owners of hotels in the province of Santa Elena, Ecuador. Three components of organizational innovation were analyzed: methods of organizing job positions, work organization practices and management of external relations; all of which were tested with both internal variables (individual and structural characteristics) and variables external to the firms.