This course is for people who have or are making textured, rich humanities data and want to be able to use, share, and preserve their information. We will take a multi-faceted approach to the challenges of curating data that integrates
- immediate, practical concerns of preparing, transforming, and analyzing data,
- strategic tasks of mapping data models and developing maintenance plans,
- and foundational thinking about the role of data curation in research.
We will move between hands-on work with data sets and tools to discussions about the nature of data curation. Working with the tools like IPython notebooks and OpenRefine and with open data sets in a variety of formats from institutions like the Metropolitan Museum and the Digital Public Library of America, we will explore topics such as defining data quality and identifying data problems; translating data models between different systems; developing best practices for data reuse and interchange. Participants will be able to use data from their own research or work with practice sets we will supply.
We ask that people who take this course have some experience using open source software, including reading technical documentation and help forums, and that they have a basic understanding of programming (e.g. what is a variable, some familiarity with loops, etc.). Please contact the instructors if you need guidance in attaining these prerequisites in time for HILT.