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Dataset for modeling risky driver behaviors based on accelerometer (X,Y,Z axis in meters per second squared (m/s2)) and gyroscope (X,Y, Z axis in degrees per second (°/s) ) data. Sampling Rate: Average 2 samples (rows) per second Cars: Ford Fiesta 1.4, Ford Fiesta 1.25, Hyundai i20 Drivers: 3 different drivers with the ages of 27, 28 and 37 Driver Behaviors: Sudden Acceleration (Class Label: 1), Sudden Right Turn (Class Label: 2), Sudden Left Turn (Class Label: 3), Sudden Break (Class Label: 4) Best Window Size: 14 seconds Sensor: MPU6050 Device: Raspberry Pi 3 Model B Please See Summary Table for summary of the collected data.
Data Types:
  • Tabular Data
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  • Document
Datasets presented here gives raw and processed data for several scenarios related to the ammonia process as a function of depleting raw material flows, synthesis optimization, power generation, and utility power consumption. The datasets also illustrate economic indicators for which the sustainability of each scenario is assessed and judged using Life Cycle Assessments.
Data Types:
  • Tabular Data
  • Dataset
Evaluation study among students of interactive, browser-based graphics for three courses (animal nutrition, food sciences, zoology) in veterinary education. Three tables in one xlsx-file. Evaluation study was performed by questionnaires. Students were asked to rate on an ordinal scale from 1 (strongly disagree) to 5 (strongly agree) following statements • B1: I can imagine that the majority of students will handle easily interactive graphics. • B2: I´d wish to have more interactive graphics in veterinary school. • B3: I can image using this tool because it ́s intuitive. • C4: I was previously familiar with the taught content. • C5: I understood the teaching contents taught by the interactive graphic. • C6: The interactivity of the graphic had a positive impact on my interest about the taught content. • C7: The interactive graphic had no benefit to the course. • D9: Digitalization is a chance to improve academic education. Additionally, students were asked to provide information if and which other digital media they have been using for learning so far (D8).
Data Types:
  • Tabular Data
  • Dataset
Prior prospective memory (PM) research shows paradoxical findings—young adults outperform older adults in laboratory-settings, but the reverse is found in naturalistic settings. Moreover, young-old outperform old-old adults in laboratory-settings, but show no age differences in naturalistic settings. Here we highlight how time-based task characteristics have differed systematically between studies conducted in laboratory (time-interval cues) and naturalistic settings (time-of-day cues) and argue that this apparent paradox is a function of comparing disparate task types. In three experiments, we tested this hypothesis using analogous paradigms across settings, with event-based, time-of-day, and time-interval cued PM tasks. Experiment 1 compared young (n = 40) and older (n = 53) adults on a laboratory paradigm that measured PM tasks embedded in a virtual, daily life narrative; and on a conceptually parallel paradigm using a customized smartphone application (MEMO) in actual daily life. Results revealed that on the MEMO, older adults outperformed young adults on the time-of-day tasks but did not differ on the time-interval or event-based task. In contrast, older adults performed worse than young adults in the laboratory. Experiment 2 compared PM performance in young-old (n = 64) and old-old (n = 40) adults using the same paradigms. Young-old outperformed old-old adults in the laboratory; however, group differences were not evident in daily life. Experiment 3 compared young (n = 42) and older (n = 41) adults, and largely replicated the findings of Experiment 1 using a more demanding version of MEMO. These findings provide novel and important insights into the limiting conditions of the age-PM paradox and the need for a finer theoretical delineation of time-based tasks.
Data Types:
  • Software/Code
  • Dataset
Natural-killer/T-cell lymphoma (NKTCL) is an aggressive and heterogeneous entity of non-Hodgkin’s lymphoma, strongly associated with Epstein-Barr virus (EBV) infection. To identify molecular subtypes of NKTCL based on genomic structural alterations and EBV sequences, we performed multi-omics study on 128 biopsy samples of newly diagnosed NKTCL and defined three prominent subtypes.
Data Types:
  • Tabular Data
  • Dataset
We showed that S1P and its receptor S1PR2 are involved in maintaining the epidermal barrier homeostasis by controlling tight junction related proteins, corneodesmosin, and filaggrin2 expression.
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
Clinical data of patients with arthroscopically confirmed TFCC lesion including preoperative weight bearing capacity tests. We tested the difference between weight bearing capacity of the injured hand compared to the healthy hand and to the injured hand with WristWidget. Further analysis compared the groups: traumatic vs degenerative lesion; stable vs unstable DRUJ determined by the need for a stabilising operation. Data includes Patient ID, age at time of injury/symptom onset, injured side, etiology, DASH-score, time until examination, pain on forced supination/pronation, clinical stability of the DRUJ, pain on pressure on the ulnar fovea, pain on forced ulnarduction, handgrip of both hands, weight bearing capacity in kg of both hands, weight bearing capacity of both hands with wristwidget, derived variables concerning the weight bearing test, range of motion in degrees for dorsal/palmarflexion, ulnar/radialduction, pro/supination, differentiation between traumatic and degenerative lesions in the MRI report, MRI field strength, static ulnar variance, dynamic ulnar variance in mm, weight bearing test capacity during x-ray, derived variables regarding the weight bearing test, information about stabilising operation, information about intraoperative assessment on type of lesion (traumatic/degenerative). All patients were right hand dominant which is not included in the dataset.
Data Types:
  • Software/Code
  • Dataset
Supplementary Table 1 Infection types on Zhongliang 31 and Mingxian 169 produced by seven Puccinia striiformis f. sp. tritici (Pst) at seedling stage Supplementary Table 2 Genotyping results of resistant bulk, susceptible bulk, and two parents using wheat 660K SNP array Supplementary Table 3 Names and sequences of 30 KASP markers and one SSR marker linked to the stripe rust resistance gene YrZl31 and Yr5 diagnostic KASP marker used to detect Yr5 Supplementary Table 4 Polymorphism of four closely linked KASP markers in 65 wheat cultivars and advanced breeding lines
Data Types:
  • Tabular Data
  • Dataset
This is a dataset on Ghanaian patients’ perception of how nurses, midwives, and doctors communicate with patients, using Four Habits Patients’ Questionnaire
Data Types:
  • Software/Code
  • Dataset
This study seeks to uncover the impact that expatriate labor remittances have on the economic growth of Saudi Arabia. The ARDL and NARDL methods have used to discover the influence and nature of the remittances on Saudi GDP growth. The study is based on data collected over the 1970-2016 timeframe. It concludes that the relationship between labor remittances and Saudi Arabian economic growth is asymmetrical; a one percent decrease in remittances increases GDP by 0.837 percent, while a one percent increase in remittances increases GDP by 0.291 percent. Our dataset collected data from the World Bank, SAMA and the Saudi General Authority of Statistics. This study investigates the impact of expatriate labor remittances (ELR) from Saudi Arabia on its economic growth rate. It uses a logarithmic function (the Cobb-Douglas model), where the logarithm of GDP (LGDP) at constant prices (2010 = 100) is the dependent variable, and the logarithm of the total remittances of expatriate labor (LTR) is the independent variable. Other variables include the natural logarithm of the consumer price index (LCPI), the natural logarithm of the population of 15-65 year-olds (LPOPL), the natural logarithm of fixed capital (LCPTL), the index of trade openness (OPN), and the logarithm of government expenditure at constant prices (LGOV).
Data Types:
  • Tabular Data
  • Dataset
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