Good data organization is the foundation of any research project. This is especially true for projects involving data processing, analysis, and visualization. This two-part workshop will introduce best practices for using spreadsheets in the important “data wrangling” stage that sets you up to work with data effectively.

Before data analysis comes the messy work of evaluating, cleaning, and transforming data. This hands-on workshop will introduce OpenRefine, a powerful, free, and open-source tool for data wrangling. We will cover how to navigate the interface, import data, create projects, and use some key features for exploring and processing data, such as faceting and filtering, clustering, and editing data.

An introduction to text mining/analysis and resources for finding text data, preparing text data for analysis, methods and tools for analyzing text data, and further readings regarding text mining and its various methods.

Text analysis is the process of examining texts to discover hidden information, gain new insights, or answer specific research questions using algorithms that quickly identify facts, patterns, and relationships in a body of texts. This information can be converted into structured forms for visualization using charts, graphs, mind maps, word clouds, and more.

This workshop will introduce text analysis and visualization with Voyant, an open-source, web-based application. Participants will learn how to use multiple tools within Voyant and their benefits/limitations, some best practices for text visualization, and some ways text analysis could be incorporated into their research.

Topic modeling is a text mining method that uses algorithms to discover hidden patterns and structures in large collections of texts. A topic modeling program extracts abstract “topics” by identifying and grouping words that frequently co-occur in a text. This workshop will introduce topic modeling with MALLET, a Java-based package for statistical natural language processing, topic modeling, and other machine learning applications to text. Participants will learn how to create and interpret topic models using MALLET.

This LibGuide introduces users to optical character recognition (OCR), outlines OCR best practices, provides information and resources for OCR tools, and links to example OCR projects.