Scholars increasingly work on collaborative research projects. Collaborative projects often bring together partners across disciplines, institutions, and sectors. These projects present opportunities for innovation but also raise challenges for the development of efficient and effective workflows and the management of data. This workshop will examine considerations for collaborative research and present some strategies for developing and documenting workflows as well as methods for storing and sharing data. We will also look at some tools (i.e., Box, OSF, PRDN, etc.) available at Duke that can be used to support these types of projects.
Python can be a great option for exploration, analysis and visualization of tabular data, like spreadsheets and CSV files, if you know which tools to use and how to get started. This workshop will take you through some practical examples of using Python and the Pandas module inside a Jupyter notebook, to load data, transform it into a standard "tidy" format, and visualize it with Seaborn (or another similar module). There are no prerequisites for this workshop - familiarity with the Python programming language is not required, but you will probably find it easier to follow if you have a little coding experience since we will not be giving an overview of the language itself. Instead, the focus will be on learning how to use the language through conceptual understanding and recipes for specific, commonly-useful tasks.
This workshop offers an overview of the SAS programming language, focusing on data management activities. Session 1 includes a general overview of major SAS components (Program Editor, Log and Output) and the core concepts of SAS programming (DATA and PROC steps). It focuses on the process of importing and modifying data, including issues of importing/exporting data from other file formats, merging and concatenating data sets, and adding to or subsettting from datasets. Key SAS statements described include: PROC IMPORT, SET, MERGE, IF-THEN, and WHERE. Session 2 focuses on data analysis, including variable creation/recoding and descriptive analyses typically used in data management. Key SAS statements described include: PROC CONTENTS, PROC MEANS, and PROC FREQ. Together, these two sessions allow researchers to learn basic data management processes using the SAS statistical system. Registration required; please click the "more event information" button below to access the registration form.