This data was obtained from an assessment using an academic procrastination scale towards 586 students in the XIII semester at Makassar, Indonesia. Students respond to statements with a Likert-scale of 5,4,3,2,1, which represents strongly agree, agree, doubt, disagree, and strongly disagree, respectively.
OP = Oil Price
GDP = Gross Domestic Product
EXR = Exchange Rate
FBI = Foreign Direct Investment
NX = Net Export
INF = inflation
Βo = Intercept
B1 = co-efficient
E = Error Term
r = Pearson Correlation
t = time series
The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 13, 2020.
Please cite as:
• Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881.
• Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.
• Country data (country_data.txt): Country data.
• Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators.
• Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators.
• Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.
Raw data of figures and tables:
• Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2.
• Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3.
• Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1.
• Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2.
• Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
The clinical characteristics, chest computed tomographic (CT) scans, medicine treatment and laboratory information were collected and analyzed. All cases had finished CT scans and real-time reverse-transcriptase polymerase-chain reaction (RT-PCR) before admission and also had finished examinations after discharge from hospital per 2 weeks. The results of RT-PCR assay in all cases were negative after discharge. Clinical information included demographic data, medical history, symptoms, signs, course of the disease, length of stay in hospital, therapeutic schedule and basic disease. The characteristic of CT scans including the zone of lung lesion and CT imaging which are ground glass opacity (GGO), patch shadow, consolidation and reticular pattern. Blood tests upon admittance and in the early-stage evolution (24-48 hours) were carried out, including white blood cell (WBC) count, neutrophil count, lymphocyte count, monocyte count, platelet count and partial pressure of arterial oxygen (PaO2). According the tests, neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR) and PaO2 /FiO2 (oxygen concentration) ratio were measured.
Contributors:Tapia Nieto Gerson, Collantes Junior, Murguía Danny
This research data aims to measure the level of Building Information Modeling (BIM) adoption in urban building projects in Lima city and Callao by the end of 2017. This level helped us to determine in what category of adopter Lima city, Peru most important city in terms of urban buildings, is located according to the Diffusion of Innovations theory (Rogers, 2003). Our hypothesis was that 15% of urban building projects adopted BIM by the end of 2017.
The level of adoption is estimated through sampling principles and the population data (N=1218) can be found in the publication “Urban buildings market in Lima city and Callao 2017: edition 22” (CAPECO, 2017).
The survey (docx file) is divided in five sections: general data of the interviewee, BIM perception, BIM acceptance, BIM adoption and general data of the project. The final data (xlsx file) provides the results of the survey that was answer by 323 professionals related to the building industry (Civil engineers, architects and others) and each answer corresponded to a unique project.
As it was mentioned the population was based on all new urban building projects in the geographical area of study and under construction process during the period of data collection. A project was considered under construction process when it was found at the beginning of earthworks or preliminary works until the delivery of the unoccupied project. In addition, remodeling projects that involved expanding their built-up area were also considered, but this expansion had to be at least 500 sm. On the other hand, all single family houses and multi-family buildings that do not had a public construction license were excluded.
The data collection was carried out by a research manager and two research assistants. Each research assistant was assigned a certain number of clusters within the designed sample. The sampling frame used in this research is one of an area type which are geographical surfaces well-defined. These surfaces are clusters and in this case were districts of Lima city and Callao. The data collection was taken from October to December of 2017.
The method to reach the sample size (n=323) was through an emailed virtual survey (52 answers) and by visiting building projects site (271 answers). Projects visited were found aleatory, with the only requirement to be inside a designed sample cluster.
It has been considered that a project has adopted BIM if it has used any of its applications: 3D models visualization; 3D modeling; material quantification and budgets made from 3D models; structure, MEP or HVAC model coordination; 4D construction simulation; control of construction progress with BIM; procurement of precast components; or the generation of 2D drawings from 3D models.
The main notable finding is that the BIM adoption level in urban building projects in Lima city and Callao in 2017 was 21.6% (70/323), which places this analyzed region in the category of “early majority” adopters (Rogers, 2003).
Contributors:Elmendorf Christopher S., Biber Eric, Monkkonen Paavo, O'Neill Moira
presume_constrained.xlsx: excel spreadsheet with principal results
presume_constrained.rda: Rda file with additional variables
An R Markdown document with code to replicate all figures and quantitative results in the paper is available from Elmendorf upon request.
The autocalibration tool coupling SWMM with the Genetic Optimization Algorithm is used to constantly change the parameter combination of the model, and choose the best parameter group by comparing the performance of the model. The validated SWMM model is used to simulated hydrological processes with different rainfalls. the climate projection data for three periods in four return periods are used to analyze the flooding process under different scenarios. The simulation result data including the total waterlogging volume and waterlogging duration is showed as the attribute data of the manholes
Reports of major limb defects after prenatal cannabis exposure (PCE) in animals and of human populations in Hawaii, Europe and Australia raise the question of whether the increasing use of cannabis in USA might be spatiotemporally associated with limb reduction rates (LRR) across USA. Geotemporospatial analysis conducted in R. LRR was significantly associated with cannabis use and THC potency and demonstrated prominent cannabis-use quintile effects. In final lagged geospatial models interactive terms including cannabinoids were highly significant and robust to adjustment. States in which cannabis was not legalized had a lower LRR (4.28 v 5.01 /10,000 live births, relative risk reduction = -0.15, (95%C.I. -0.25, -0.02), P=0.021). 37-63% of cases are estimated to not be born alive; their inclusion strengthened these associations. Causal inference studies using inverse probabilty-weighted robust regression and e-values supported causal epidemiological pathways. Findings apply to several cannabinoids, are consistent with pathophysiological and causal mechanisms, are exacerbated by cannabis legalization and demonstrate dose-related intergenerational sequaelae.