This dataset was created using an environmentally extended multi-region input-output (EE-MRIO) model, which uses an input and output approach that tracks flows of monetary values of trade to allot environmental impacts between regions and sectors. The MRIO model is a predominant method for the estimation of consumption-based greenhouse gas (GHG) accounting. Our particular model was constructed using Python.
The tabulated results show the flows of emissions embodied in imports, exports, and domestic consumption of a region. In our case, we calculated the consumption emissions of all Canadian provinces and territories from 2010-2015. The tables show intersectoral, interprovincial, and international flows of embodied emissions, presented in the North American Industry Classification System (NAICS)"
Regarding our model’s inputs for estimating embodied emissions in international trade, we utilized the Environmental Accounts and financial input-output tables from the World Input-Output Database (WIOD). For estimating embodied emissions in Canada and Canada’s trade, we used Canada’s Physical Flow Accounts, Interprovincial Input-Output and Supply-Use Tables, and Canada’s Trade Online Data. These datasets were accessed in August 2019 from the following websites (see links):
• WIOD Input-Output Tables: http://www.wiod.org/home
• WIOD Environmental Accounts 2013: http://www.wiod.org/home
• WIOD Environmental Accounts 2019: https://ec.europa.eu/jrc/en/research-topic/economic-environmental-and-social-effects-of-globalisation
• Interprovincial Input-Output Tables: https://www150.statcan.gc.ca/n1/en/catalogue/15-211-X
• Interprovincial Supply-Use Tables: https://www150.statcan.gc.ca/n1/en/catalogue/15-602-X
• Physical Flow Accounts: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3810009701
• Canada’s Trade Online Data: https://www.ic.gc.ca/eic/site/tdo-dcd.nsf/eng/Home
Contributors:Carolina Todesco, Rodrigo Cardoso da Silva
O banco de dados em excel apresenta de forma organizada por ano, programa e projeto os dados orçamentários do Ministério do Turismo de 2003 a 2018. Os dados foram obtidos do Relatório de Execução Orçamentária da União disponibilizados pela Câmara dos Deputados em banco de dados em access.
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 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on May 25, 2020.
Please cite as:
• (Data in Brief article)
• 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.
The COVID-19 pandemic is a worldwide public health crisis. A vaccine with efficacy against SARS-CoV-2, the pathogen that causes COVID-19, is needed. While most vaccines under investigation are optimized to generate an antibody response, we hypothesize that peptide vaccines containing optimized epitope regions with concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE), all the while providing a platform with fast manufacturing potential and with high shelf-life stability. Here we combine computational prediction of T cell epitopes with recently published B cell epitope mapping studies to propose optimized peptide vaccines for SARS-CoV-2. We begin with an exploration of the predicted T cell epitope space in SARS-CoV-2, with interrogation of HLA-I and -II epitope overlap, protein source, concurrent human/murine coverage, and allelic space. The T cell vaccine candidates were selected by further considering their predicted affinities for MHC-I and MHC-II alleles across the human population (as well as H2-b/H2-d murine coverage to support preclinical studies), predicted immunogenicity, viral protein abundance, sequence conservation, and co-localization of MHC-I and -II epitopes. The predicted B cell epitope regions were selected by starting from responses identified in linear epitope mapping studies of patient serum and filtering to select those with high molecular dynamics-derived surface accessibility, high sequence conservation, spatial localization within functional domains of the spike glycoprotein (RBD, FP, and HR regions), and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified using these criteria. By combining these B cell and T cell analyses, we propose a set of human and murine-compatible SARS-CoV-2 vaccine peptide candidates.
Counting_for_memorizing_english_words xlsx is the file for recording the number of memorizing English words.
Mindfulness_mindpainting, mindfulness_mindpainting_music and mindfulness_only are the data files for recording meditation and attention value on memorizing and meditation process.
Contributors:Max van Riel, Zidan Yu, Shota Hodono, Ding Xia, Hersh Chandarana, koji fujimoto, Martijn Cloos
Free-breathing MR Fingerprinting acquisitions of the abdomen. One dataset is acquired with the normal ordering, the other with the motion-robust ordering, as described in the article "Optimization of MR Fingerprinting for Free-Breathing Quantitative Abdominal Imaging". The data acquired with the normal ordering shows artefacts caused by respiratory motion, while these artefacts are reduced by using the proposed motion-robust ordering of the acquired k-space.