Data in Brief最新文献

This work presents the MuscleTracker Hand Movement dataset, containing Surface Electromyography (sEMG) data from the right arm of 49 healthy subjects without neuromuscular or cardiovascular issues. Subjects performed five hand movements-pronation with extended fingers, flexion, extension, pronation with flexed fingers, and relaxation-while standing, with one hand palm-down. Data was recorded from two sEMG channels using Biopac MP36 (1000 Hz) and MuscleTracker (512 Hz), with three and four repetitions per device, respectively, for each movement. The dataset includes 825 samples, along with subject details such as gender, age, physical condition, and, for MuscleTracker subjects, anthropometric measurements. This data supports machine-learning development for classifying hand gestures in sEMG signals, with applications in prosthetics control and human-computer interaction. In addition, validation experiments were performed to validate the database and stablish a comparison baseline.

Masonry is a construction material composed of units (blocks or bricks) joined with mortar. It is one of the most widely used materials in construction resisting both vertical and horizontal forces in single and multi-family housing buildings. A correct union between the units and the mortar (interface) is essential, as is determining the resistance from the applied loads. There is a divergence in how the mortar is bedded in the construction of masonry walls. In some countries, such as Canada and Australia, regulations require that the mortar be placed in face shell bedding when hollow blocks are used. However, in countries like Mexico, regulations establish that it be placed in the net area, and construction practices often differ. Much research has been conducted to study the compressive behavior of mortar bedding in masonry of hollow concrete blocks. However, fewer studies have focused on shear behavior. This paper presents the dataset of experimental laboratory tests on wallettes built with hollow concrete blocks. Two methods of mortar bedding were employed: over the net area and the lateral face. The values obtained can be used to compare the shear strength in hollow concrete block masonry and the shear failure. Additionally, they can be useful for calibrating numerical models.

Many forms of bioimage analysis involve the detection of objects and their outlines. In the context of microscopy-based high-throughput drug and genomic screening and even in smaller scale microscopy experiments, the objects that most often need to be detected are cells. In order to develop and benchmark algorithms and neural networks that can perform this task, high-quality datasets with annotated cell outlines are needed. We have created a dataset, named Aitslab_bioimaging2, consisting of 60 fluorescence microscopy images with EGFP-Galectin-3 labelled cells and their hand-labelled outlines. Images were acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification created as part of an RNA interference screen with a modified U2OS osteosarcoma cell line. Outlines were labelled by three annotators, who had high inter-annotator agreement between them and with a biomedical expert, who labelled some of the objects for comparison and reviewed a subset of the labels, making minor corrections as needed. The dataset comprises over 2200 annotated cell objects in total, making it sufficient in size to train high-performing neural networks for instance or semantic segmentation. Labels can also easily be converted to boxes for object detection tasks. The dataset is already pre-divided into training, development, and test sets. Matching nuclear staining and outlines are available for part of the dataset from a previous publication (dataset Aitslab_bioimaging1) [1].

It is challenging for teachers to monitor each student's emotional state in real-time, making personalized learning difficult to achieve. Previous emotion recognition methods, such as support vector machines, are limited by technology and fail to meet practical application requirements. However, the development of deep learning technology offers new solutions for facial expression recognition, which makes emotional interaction and personalized support in education possible. Until now, there has been a lack of facial expression datasets in real classroom settings. To fill this gap, this study collected facial expression data in a real classroom, preprocessed it using OpenCV, and established the first real-world facial expression dataset. The emotion categories include surprise, happiness, neutrality, confusion, and boredom. The dataset was rigorously screened and contains a total of 5,527 images, divided into training, validation, and test sets. This dataset provides a reliable foundation for future research and applications in educational technology, particularly in the development of real-time emotion recognition models to enhance personalized learning and teaching effectiveness. The rigorous data collection and preprocessing approach ensures the dataset's quality and authenticity, addressing the limitations of existing datasets collected in laboratory settings.

This article summarizes a database for analyzing the impact of structural and environmental characteristics on residential property values in Hamilton County, TN. The original dataset consists of house characteristics data for 873 residential sales between January 1, 2023, and September 25, 2023. Using Google's API tools and Point to Edge computations, several geographical variables-including the distance to green recreational areas, surrounding facilities, restaurants, air quality index, walk score-were gathered. The aggregated information can help forecast Hamilton County's housing market with accuracy and correctly assess the environmental impact on housing prices. More specifically, the value of any environmental amenity in the study area can be implicitly estimated using this dataset.

The first set of data refers to Insulin-like Growth Factor-1 (IGF-1) protein incorporation via emulsion electrospinning into a DegraPol^Ⓡ random fiber mesh and its characterization. Specifically, the fiber thickness was assessed and compared to pure DegraPol^Ⓡ fibers without IGF-1 (control). Furthermore, the mechanical properties of these meshes were assessed and data on ultimate tensile stress, Young's modulus and ultimate fracture strain are presented for ring specimen and rectangular pieces taken from electrospun tubes in the transverse direction as well as rectangular pieces taken in the axial direction of the electrospun tube. Moreover, the static and the dynamic water contact angles were determined. The second set of data represents morphological aspects, such as the cytoskeletal aspect ratio (i.e. length of the cell divided by its width) for rabbit Achilles tenocytes stimulated in vitro with 1, 10, and 100 ng/mL IGF-1 supplementation compared to the corresponding cell culture without IGF-1 (control). Furthermore, qPCR was performed and collagen I, ki67 and tenomodulin gene expression data are presented for rabbit Achilles tenocytes in vitro with 0.1, 1 and 10 ng/mL IGF-1 supplementation, respectively, as well as with a supplementation of released IGF-1 from the DegraPol mesh (concentration was 1 ng/mL).

Some real-world phenomena and human-made problems have been modeled as networks where the objects form pairwise interactions. However, this is a limited approach when the existence of high-order interactions is inherent in a system, such as the brain, social networks and ecosystems. The way in which these high-order interactions affect the collective behavior of a complex system is still an open question. For this reason, it is necessary to analyze theoretically, numerically and experimentally the consequences of higher-order interactions in complex systems. Here, we provide experimental datasets of the dynamics of three nonlinear electronic oscillators, namely, Rössler oscillators, interacting into a simplicial complex whose connections rely on both linear (diffusive) and nonlinear (high-order) coupling. It is well-known that Rössler systems only achieve the synchronization when they are coupled by means of $x$ or $y$ variable. Considering this fact, we designed our experiment considering four scenarios. The first one, when both linear and nonlinear coupling functions are introduced through the $x$ variable. The second one, occurring when linear coupling is introduced through the $x$ variable and the nonlinear coupling through the $y$ variable. The third case happens when the linear coupling is introduced through the $y$ variable whereas nonlinear coupling goes through the $x$ variable. The last case, when both linear and nonlinear coupling are introduced through the $y$ variable. For each scenario, we acquired 10000 times series when both the linear and nonlinear coupling strengths were modified. Each time series contained 30000 temporal points. These datasets are useful to corroborate the conditions to reach the synchronized state varying the linear/non-linear coupling strengths and to test new metrics for better understanding the effects of higher-order interactions in complex networks.

The demand for mobile coverage with adequate signal quality has triggered criticism due to the maturity of the Internet's diffusion in today's society. However, with the deployment of 5G networks, even 5G NSA by 4G LTE, the complexity of the operating environment of mobile networks has increased. To evaluate the behavior of mobile networks in terms of signal quality and other important metrics for mobile telephony, we developed a dataset consisting of 33 radio parameters that can collect up to 736,974 records generated daily by smartphones and tablets. The dataset comprises samples collected in cities situated on the banks of the Amazon and Negro rivers. To create the dataset, an application was designed for the Android operating system using the Kotlin programming language, which can collect data in real time and generate a CSV file. After post-processing the collected data with data science techniques, the filtered dataset was stored in the Mendeley public repository. We divided the data into three regions: the metropolitan area of Manaus, the middle Solimões River, and the middle Amazonas River. To improve the performance of the experiments, the database was separated according to the cities and locations collected.

Voice is a one of media for human communication and interaction. Emotions conveyed through voice, such as laughter or tears, can communicate messages more quickly than spoken or written language. In sentiment analysis, the emotional component is crucial for reflecting human perceptions and opinions. This paper introduces IndoWaveSentiment, a dataset of emotional voice recordings categorized into five classes: neutral, happy, surprised, disgusted, and disappointed. The data collection took place in a recording studio with ten actors, evenly split between men and women. Each actor repeated the same sentence in Bahasa Indonesia three times for each emotion class, and the recordings were saved in .wav format. The annotation process was manually conducted using Audacity and validated through a questionnaire-based sampling technique that supports audio data. This dataset is valuable for researchers in Signal Processing and Artificial Intelligence, aiding the development of classification models within Machine Learning.

This dataset provides a numerical simulation of pavement mechanical behavior under Traffic Speed Deflectometer (TSD) measurements. It consists of simulated deflection slope data for various pavement structures and subgrade properties, generated using the Alizé-LCPC software, a standard tool in French pavement engineering. The dataset addresses limitations in traditional Falling Weight Deflectometer (FWD) methods, offering a more accurate and computationally efficient approach for estimating the Subgrade Resilient Modulus (M_R) using machine learning models. This resource is valuable for researchers aiming to enhance pavement evaluation methods and develop predictive models for road infrastructure maintenance and assessment. The data are openly accessible, facilitating widespread research collaboration and the application of advanced data analytics in pavement engineering.