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As a product of the seminar/workshop attendees will select file formats, choose a file naming convention, and design a file system architecture for their research. Specific learning goals include to: Understand file format choices and their implications for data sharing, data publication, and data re-use, identify best practices for file system organization, and identify best practices for file naming conventions.

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This is the second in a series of short seminars that explore topics in research data management (see intro above and other DM seminars in this list).

File Formats and System Organization for Research.

As a product of the seminar/workshop attendees will outline a data management plan (DMP) for their research. Specific learning goals include to: Understand federal policy context for data management and sharing, explain the data lifecycle, critically evaluate existing Data Management Plans (DMPs), and be able to identify key elements in a DMP. This is the first in a series of short seminars that explore topics in research data management (see intro above and other DM seminars in this list). These topics introduce practical behaviors to ease the digital research process. Specific learning goals include the identification of best practices for: file naming conventions, file system organization, data security, data privacy, backup strategies, data sharing, data documentation, and data publication. This is an introduction to topics in research data management designed to foster skills and encourage data management best practices for efficiency, compliance and security in the research environment. Timothy Norris, Data Scientist, The Data Management Challenge: Wrangling Data in the Research Environment Prior experience with R required-this workshop assumes knowledge of the R language and environment.Дr.

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Introduction to using the software program R for visualization. It covers making common graphs and tables, importing data, making sheets and dashboards, and exporting images. Introduction to using the software program Tableau for data visualization.

It covers common statistical methods in R such as means comparisons, ANOVA, linear regression, and basic visualization. It provides an introduction to the R software program, including its programming language, software -environment, importing data, descriptive statistics, transforming variables, selecting and splitting data, exploratory tests, and visualization.Дesigned for intermediate R and R Studio users. It provides an overview of the SAS software program, including its programming language, software environment, importing data, descriptive statistics, transforming variables, selecting and splitting data, exploratory tests, regression models, and visualization.Дesigned for new R and R Studio users. It covers common statistical methods in SPSS such as means comparisons, ANOVA, linear regression, and logistic regression models.Дesigned for both new and intermediate SAS users. It provides an introduction to the SPSS software program, including its software environment, importing data, descriptive -statistics, transforming variables, selecting and splitting data, and visualization.Дesigned for intermediate SPSS users. Cameron Riopelle, Head of Data Services, Introduction to SPSSДesigned for new SPSS users.

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