Search Strategy for "Drugs Associated with Development of Pityriasis Rubra Pilaris: A Systematic Review" published in JAAD by Asfandyar Mufti, Yuliya Lytvyn , Khalad Maliyar , Muskaan Sachdeva , Jensen Yeung
This a data about the corona virus COVID-19. It contains the actual reported data. Also, it includes the predicted COVID-19 data in the future based on a model developed to predict in the future. The model used will be published in one of the journals later and will be found on my profile with title "Optimistic Prediction Model For the COVID-19 Coronavirus Pandemic based on the Reported Data Analysis".
The daily folder contains the daily data. The predicted folder contains the predicted data for each country. The total cases folder contains the total cases for each country. he section folder contains a latex code for plotting the figures for each country. Also the source file from European Centre for Disease Prevention and Control is included. More updated files available in the website of European Centre for Disease Prevention and Control.
The ground truth data used in this paper are obtained from three different areas to verify the effectiveness and robustness of the proposed methods. The detailed data information is described as follows:
1) The ground truth data from the Pecan Street dataset are collected from real households in the Muller project in Austin, TX, USA. Muller project funded by the U.S. Department of Energy and the U.S. National Science Foundation are located on the site of the Austin’s former municipal airport, close to central Austin.The selected homes in the project received monitoring equipment that captures electricity use on less than or equal to 1 min intervals for the whole home and 6 to 22 major appliances. Data over one year from August 2015 to July 2016 are analyzed, which contain the application-level and the whole-house energy consumption data. The corresponding 1-hour level temperature data are collected from the nearest Mueller weather station. We down-sample the energy consumption data to the 1-hour level to maintain consistency with the resolution of the temperature data. After data cleaning, customers without air conditioners or with missing readings are omitted, and the data of 119 residential customers are selected for accuracy analysis.
2) The ground truth data from smart home dataset are collected from real households in the Smart Home project in the Western Massachusetts, USA. The goal of this project is to optimize home energy consumption. The project involves several different types of dataset, including apartment dataset of 114 single-family, home dataset of 7 household and solar panel dataset, etc. However, the apartment dataset only contains the aggregated electrical data which can not be used to verify the accuracy of the load decomposition. Therefore, data over one year from January 2016 to December 2016 of home B and home G with individual ACLs monitor are selected for robustness analysis.
3) The ground truth data from low voltage distribution area are collected from low voltage distribution boxes in a developed city, Jiangsu province, China. Power and corresponding temperature data over one year from 2017 to 2018 are used for local DR programs. The dataset involves different distribution areas (i.e., different aggregated DR customers), including garment factory, hotel, rural neighborhoods, etc. However, the sub-meter data of all the ACLs are unavailable, thus it will only be used for aggregated DR potential analysis.
Contributors:Stein Gold Linda, Bhatia Neal , Tallman Anna, Rubenstein David
Mendeley Supplemental Tables and Figures for Stein Gold et al. A Phase 2b, Randomized Clinical Trial of Tapinarof Cream for the Treatment of Plaque Psoriasis: Secondary Efficacy and Patient-Reported Outcomes. J Am Acad Dermatol. 2020.
This data was collected from Garum Seminary High School, Blitar (East Java), Indonesia. Data collection was performed using a student academic resilience questionnaire. Participants involved 113 students of Garum Seminary High School, who were given the task of answering statements with 5 alternative answers (strongly disagree to strongly agree). Scoring for favorable items starting from 1 (strongly disagree) to 5 (strongly agree). Scoring for unfavorable Items starting from 5 (strongly disagree) to 1 (strongly agree).