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  • Time series data
    Data Types:
    • Tabular Data
    • Dataset
  • This article reports on patterns in learning device use (PCs and smartphones) of 107 university students in an online program in South Korea. After participating in a blended College TOEIC course for 15 weeks, participants’ online behaviors were scrutinized through learning analytics. Learning analytics is a research method which collects learner log data in order to analyze online behavior. Thus, researchers gain greater understanding of how these participants completed their online activities throughout the semester. The results of this research can be utilized in teaching interventions, materials development, learning environment design, and pre-service teacher training.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
  • Climate change has a significant impact on seasonal snow cover. However, obtaining robust data on snow cover remains a challenge. There is a significant lack of ground-based data for verification of remote and model data. Observation network in Siberia is quite rare, and the location of the snow stations does not always represent the characteristics of the territory. We aimed to extend the observation coverage of climate stations and to assess variability in different ecosystems. We focused on the representation of different ecosystem types in the southern West Siberian Plain and Altai low mountain area. We carried out our research in two catchments - Kasmala and Maima, located in the forest-steppe and lowland areas. The observations were conducted during the peak snow accumulation (late February - early March). In the Kasmala catchment, the observations were conducted in 2011-2014 and 2017-2019, in the Mayma catchment from 2015 to 2019. These works were funded by state projects of the Institute for Water and Environmental Problems SB RAS. In 2019, a joint 3S (South Siberian Snowpack) project funded by RFBR (N 19-35-60006, 2019-2022) was launched at Lomonosov Moscow State University. As part of this project, we expanded the observation network and conducted observations during the whole winter season 2019-2020 in three catchments: Kuchuk (steppe), Kasmala (forest-steppe), and Mayma (low mountains). Also, the 3S project merged existing data into a single dataset on snow properties (depth, density, SWE). Observations till 2019 were carried out on snow courses and small snow sites. Courses were 500 m to 2 km long. Depth measurements were made every 20 m, density measurements every 100/200 m. The snow sites were two perpendicular transects of 50 or 20 meters long, including 20 depth and 5 density measurements. In the 3S project, we changed the observation scheme (data 2019-2020). All observations were made at the snow sites, which included 61 depth and 13 density measurements. The sampling scheme was proposed by Jost et al., 2007. In total, in the Kasmala catchment, we carried out about 600 depth/70 density measurements, in the Mayma catchment about 800 depth/200 density measurements. Within the 3S project, we carried out 8781 depth and 1873 density measurements during the winter season. We highly recommend aggregating the data by courses, sites or catchments (do not use individual values).
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
  • These data have been used to better understand the Gladkop Suite rocks located in the NW Bushmanland Subprovince, particularly with regard to how it relates to the Palaeoproterozoic evolution of this part of the Namaqua Sector and its relationship with the Mesoproterozoic-dominated Bushmanland Subprovince. For more details and interpretation of the data, please refer to A.Y. Nke, R. Bailie, P.H. Macey, R.J. Thomas, D. Frei, P. Le Roux, C.J. Spencer, The 1.8 Ga Gladkop Suite: the youngest Palaeoproterozoic domain in the Namaqua-Natal Metamorphic Province, South Africa, Precambr. Res. In Press.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
  • The dataset covers the macroeconomic facts of ten member countries, Brunei, Cambodia, Indonesia, Laos, Myanmar, Malaysia, Philippines, Singapore, Thailand, and Vietnam, of Associations of South East Nations (ASEAN) from 1967 to 2019. This dataset was extracted from the World Bank and Asian Development Bank databases. The primary goal of the dataset is to enhance our understanding of the macroeconomics trends in the ASEAN region. The understanding of the dependencies and causality associations among the key fiscal variables helps to formulate long-term sustainable economic policies and strategies to face any external shocks or global/regional financial crises. Thus, the data has critical applications for economic policymakers and financial institutions. The dataset raw files span over the past several decades, i.e., starting from 1967 to 2019, of individual countries, and from 1990 to 2019, for panel analysis of ASEAN10. Thus, it has a significant potential for future studies on the impact of fiscal policy, such as pre-crises, during crises, and post-crises periods of Global Financial crises, Asian Financial Crises, and specifically, the regional economic trends before the COVID-19 epidemic.
    Data Types:
    • Tabular Data
    • Dataset
  • By optimizing for a reduction in peak line loading in a typical benchmark 0.4 kV network, additional line loading percentages caused by the introduction of incremental increases in the number of EVs charging in an agent based Monte Carlo simulation can be reduced by over 10% in the worst case and about 5% on average across several cable types. Optimization for a reduction in peak voltage drops shows an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for total line losses shows a negligible savings across low voltage networks
    Data Types:
    • Tabular Data
    • Dataset
  • The presented data article aims to provide the whole dataset obtained during an experiment of updating laser scan point clouds with photogrammetry meshes. In this context, the data quality and calculation time of photogrammetry models from different recording devices and different software solutions were compared. It was investigated whether photos from smartphones are also appropriate for updating point clouds by using photogrammetry in a factory environment. The photos of a technical installation were taken in 08:30 min with these three devices: Nikon D810 with Sigma art 24mm, iPhone 6 and iPhone XS. With each of the mentioned devices, three datasets have been created to provide enough data for the comparisons. One dataset (photos in .TIFF) of the iPhone XS is provided. The results of the data sets are used for a photogrammetry mesh quality comparison and a calculation time comparison. For the mesh quality comparison, visual qualitative inspections were performed on the models and the results were compared. Furthermore, all settings in the RealityCapture BETA 1.0.3.9696 ppi and Meshroom 2019 2.0 software are provided. A comparison of the quality of the photogrammetric 3D meshes was performed by comparing the rendering results. The dataset of the iPhone XS can be used to compare further photogrammetry software or single algorithms. Besides the images, the initial point cloud of the laser scanner is provided. Also included is the combined file which consists of the laser scan point cloud and the photogrammetry mesh of the end of the experiment.
    Data Types:
    • Software/Code
    • Image
    • Tabular Data
    • Dataset
  • Supplementary Data: 40Ar/39Ar data for hypogene and supergene jarosite samples
    Data Types:
    • Tabular Data
    • Dataset
  • This data set is included the full analysis of the 57 papers that were reviewed in the paper "Public participation in the planning process of open spaces for tourism and recreation: a scoping review"
    Data Types:
    • Tabular Data
    • Dataset
  • In response to the COVID-19 pandemics, drastic measures for social distancing have been introduced also in Italy. The purpose of this study was to describe some aspects of lifestyle, access to health services, and mental wellness of Italian pregnant women and new-mothers during the lockdown. We carried out a web-based survey to assess how pregnant women and new-mothers were coping with the lockdown. Expected outcomes were categorized in different analysis domains: psychological well-being and support, physical exercise, dietary habits, access to care, delivery and obstetric care, neonatal care and breastfeeding. We included 742 respondents (response rate 86.2%), 603 were pregnant (81.3%) and 139 (18.7%) had delivered during lockdown. We found a high score for anxiety and depression in 62.7% of pregnant women and 61.9% of new-mothers. During the lockdown, 61.9% of pregnant women reduced their physical exercise, and 79.8% reported to eat in a healthier way. 94.2% of new-mothers reported to have breastfed their babies during hospital staying. Regarding the impact of restrictive measures on breastfeeding, no impact was reported by 56.1% of new-mothers, a negative impact by 36.7%. The higher prevalence of anxiety and depressive symptoms in pregnant women and new-mothers should be a public health issue. Clinicians might also consider to recommend and encourage “home” physical exercise. On the other hand, most women improved their approach towards healthy eating during the lockdown and a very high breastfeeding rate was reported soon after birth: these data are an interesting starting point to develop new strategies for public health.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
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