Data in Brief最新文献

The population of older adults (elders) is increasing at a breakneck pace worldwide. This surge presents a significant challenge in providing adequate care for elders due to the scarcity of human caregivers. Unintentional falls of humans are critical health issues, especially for elders. Detecting falls and providing assistance as early as possible is of utmost importance. Researchers worldwide have shown interest in designing a system to detect falls promptly especially by remote monitoring, enabling the timely provision of medical help. The dataset ‘GMDCSA-24′ has been created to support the researchers on this topic to develop models to detect falls and other activities. This dataset was generated in three different natural home setups, where Falls and Activities of Daily Living were performed by four subjects (actors). To bring the versatility, the recordings were done at different times and lighting conditions: during the day when there is ample light and at night when there is low light in addition, the subjects wear different sets of clothes in the dataset. The actions were captured using the low-cost 0.92 Megapixel webcam. The low-resolution video clips make it suitable for use in real-time systems with fewer resources without any compression or processing of the clips. Users can also use this dataset to check the robustness and generalizability of a system for false positives since many ADL clips involve complex activities that may be falsely detected as falls. These complex activities include sleeping, picking up an object from the ground, doing push-ups, etc. The dataset contains 81 falls and 79 ADL video clips performed by four subjects.

Improving energy efficiency in the building sector is a subject of significant interest, considering the environmental impact of buildings. Energy efficiency involves many aspects, such as occupant comfort, system monitoring and maintenance, data treatment, instrumentation… Physical modeling and calibration, or artificial intelligence, are often employed to explore these different subjects and, thus, to limit energy consumption in buildings. Even though these techniques are well-suited, they have one thing in common, i.e., the need for user cases. This is why we propose to share a part of the large volume of data collected on our modular education building. The building is located on Nanterreʼs CESI Engineering school campus and welcomes approximately 80 students daily. A network of more than 150 sensors and actuators allows monitoring of the physical behavior of the entire building, preserving optimal comfort and energy consumption. The dataset includes the indoor physical parameters and the operating conditions of each system to describe the physical behavior of the building during a year.

Silvopastoral systems are an important strategy for sustainable livestock production. However, to expand their implementation, it is crucial to identify and develop forage materials that maintain good production and quality while being tolerant to shade conditions as well as other biotic and abiotic stresses. A field trial was conducted to evaluate the morpho-anatomical and agronomic responses of two Urochloa hybrids (Camello and Talisman) under two light conditions: shade (28 % light intensity) and full exposure. The trial followed a randomised complete block design with split-plot arrangement, where each treatment corresponded to a plot with three replications. Morphological and anatomical parameters were recorded in three technical replicates of each replication. Histological leaf sections were analysed for the percentage of adaxial epidermis, abaxial epidermis, vascular tissue, colourless parenchyma, Kranz sheath, bulliform cells, sclerenchyma, and chlorenchyma. Measurements in leaf included relative chlorophyll concentration, leaf area, leaf length, and leaf width. Evaluations in plant included height and number of tillers. Agronomic parameters such as plant cover and dry biomass were recorded for each plot. Additionally, six leaf imprints were made on the leaf undersides to observe stomatal morphology, and their length was recorded. Furthermore, plants from each treatment were grown in soil-filled tubes within the same plots. Root system photographs were taken, and in three replications per treatment, root length, root diameter, root volume, root surface area, and the depth at which 95 % of roots were concentrated (D95) were determined. These data can be utilised by the scientific community and breeders to conduct analyses and meta-analyses to identify shade tolerance mechanisms and develop genetic materials tolerant to changing climatic conditions while being optimal for use in silvopastoral systems.

This data article refers to the paper “A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study” [1]. The Electric Vehicle (EV) charging dataset includes detailed information on plug-in times, plug-out times, and energy charged for over 35,000 residential charging sessions, covering 267 user IDs across 12 locations within a mature EV market in Norway. Utilising methodologies outlined in [1], realistic predictions have been integrated into the datasets, encompassing EV battery capacities, charging power, and plug-in State-of-Charge (SoC) for each EV-user and charging session. In addition, hourly data is provided, such as energy charged and connected energy capacity for each charging session. The comprehensive dataset provides the basis for assessing current and future EV charging behaviour, analysing and modelling EV charging loads and energy flexibility, and studying the integration of EVs into power grids.

Vehicle trajectory data are invaluable for driving behaviour and traffic flow modelling studies, especially at the microscopic level. However, existing public vehicle trajectory datasets only provide data with inherent errors and lack the corresponding ground truth. This study presents a comprehensive vehicle trajectory dataset obtained using both drone and high-precision Global Navigation Satellite System (GNSS) receiver technologies with an error of less than 5 cm. The dataset contains 70 complete trajectories with a total of 10,840 data points and an average length of 48.4 m. This includes 27 left-turn trajectories, 27 through trajectories and 16 right-turn trajectories. The trajectories collected by the centimetre-level precision GNSS receiver can be regarded as the ground truth of the trajectories extracted by the drone video. Researchers can use these two trajectory datasets to analyse driving behaviour at interactive scenarios, validate and calibrate microscopic traffic flow models, and validate trajectory reconstruction methods.

Integrating multiple data source technologies is essential for organizations to respond to highly dynamic market needs. Although physical data integration systems have been considered to have better query processing systems, they pose higher implementation and maintenance costs. Meanwhile, virtual data integration has become an alternative topic that is increasingly attracting the attention of researchers in the current era of big data. Various data integration methodologies have been developed and used in various domains, processing heterogeneous data using various approaches. This review article aims to provide an overview of heterogeneous data integration research focusing on methodology and approaches. It surveys existing publications, highlighting key trends, challenges, and open research topics. The main findings are: (i) Research has been conducted in various domains. However, most focus on big data rather than specific study domains; (ii) researchers primarily focus on semantics challenges, and (iii) gaps still need to be addressed and related to integration issues involving semantics and unstructured data formats that must be thoroughly addressed. Furthermore, considering elements of cutting-edge technology, such as machine learning and data integration, about privacy concerns provides a chance for additional investigation. Finally, we provide insight into the potential for a broader review of integration challenges based on case studies.

The proliferation of urban areas and the concurrent increase in vehicular mobility have escalated the urgency for advanced traffic management solutions. This data article introduces two traffic datasets from Madrid, collected between June 2022 and February 2024, to address the challenges of traffic management in urban areas. The first dataset provides detailed traffic flow measurements (vehicles per hour) from urban sensors and road networks, enriched with weather data, calendar data and road infrastructure details from OpenStreetMap. This combination allows for an in-depth analysis of urban mobility. Through preprocessing, data quality is ensured by eliminating inconsistent sensor readings. The second dataset is enhanced for advanced predictive modelling. It includes time-based transformations and a tailored preprocessing pipeline that standardizes numeric data, applies one-hot encoding to categorical features, and uses ordinal encoding for specific features. In constructing the datasets, we initially employed the k-means algorithm to cluster data from multiple sensors, thereby highlighting the most representative ones. This clustering can be adapted or modified according to the user's needs, ensuring flexibility for various analyses and applications.

This work underscores the importance of advanced datasets in urban planning and highlights the versatility of these resources for multiple practical applications. We highlight the relevance of the collected data for a variety of essential purposes, including traffic prediction, infrastructure planning, studies on the environmental impact of traffic, event planning, and conducting simulations. These datasets not only provide a solid foundation for academic research but also for designing and implementing more effective and sustainable traffic policies. Furthermore, all related datasets, source code, and documentation have been made publicly available, encouraging further research and practical applications in traffic management and urban planning.

As Southeast Asia grapples with extreme heat occurrences in recent years, mapping which areas are clustered with elevated temperatures is crucial for monitoring the at-risk population. Identifying the contributing factors to the warming trends in these areas is also vital in formulating adaptation and mitigation strategies. This dataset comprises land surface temperature (LST) in three metropolises in the region – Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta – downloaded and processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. We used MODIS’ inherent grid system to map LST values at the satellite image's most granular level. We combined them with selected environmental and socioeconomic variables, including building and built-up areas, areas of greeneries, industrial zones, and water bodies, nighttime light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on which urban infrastructures, i.e. roads and airports, are present in each grid. Available in shapefile and comma-separate variable file format, this dataset is useful for urban studies in these three cities. The dataset can be easily updated as additional data on LST and other variables becomes available.

This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and raising overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.

The effects of growth and reproduction on the marine Cyclops sp. were investigated using three microalgae as diets. The development period of Cyclops sp. was evaluated at 10⁶ cells/ml in 15ppt salinity to identify the stationary phase. The survival rate of marine Cyclops from nauplius to adult differed according to the microalgal diet. The results showed that the shortest time (14 days) and highest survival (17.6 ± 0.131 %) for Cyclops sp. was achieved with those fed with Nannochloropsis sp. Whereas, it took longest time (37 days) and lowest survival rate (6.40 ± 0.035 %) when fed Chlamydomonas sp. The developmental period from naupli (I - VI) (6.91 ± 0.453 days), copepodite (I - VI) (11.4 ± 0.311days) and naupli to adult (20 ± 1.08 days) appeared significantly longer when fed with Nannochloropsis sp. compared to other treatments. The daily mean naupli production of adult females over 7 days was significantly higher (p ˂ 0.05) in Nannochloropsis sp. compared with Chlamydomonas sp. and Gonyostomum sp. On the 25th day of Nannochloropsis sp. treatment, 99 % of the mature females died. Production (naupli, copepodite adult male and adult female) was significantly higher (p ˂ 0.05) in Nannochloropsis sp. than in other microalgal diets. On the fifteenth day, Nannochloropsis sp. showed a significantly higher (p ˂ 0.05) specific growth rate than other microalgal diets. Nannochloropsis sp. had the highest nauplius survival rate on the sixth day compared to other microalgal diets. With Nannochloropsis sp., the species has a higher hatching rate, and in Chlamydomonas sp. hatching occurs earlier. The average lifespan for Nannochloropsis sp. was 46 days, for Chlamydomonas sp. it was 37 days, and for Gonyostomum sp. it was 32 days.