Data TRIKE is an experimental framework for how to better present humanities data work in context. It consists of breaking a project into steps that isolate data creation and each stage of transformation it undergoes; adding context and critique to explicate the choices made.

Data work is storytelling, in the sense that at many points different decisions could be made, changing the trajectory and the outcome. Every dataset has a history and all information exists because of human interference. The purpose of Data TRIKE is to explore how data can be used in humanities study by taking a critical look at data origins and transformations. The website serves as an open educational resource for anyone interested in working with or understanding data as a non-neutral object of critique. Because there is no such thing as “raw” data, we believe that it is important to look closely at the choices that affect the outcomes of data transformations.  

What you will find on this website are a series of case studies that demonstrate different approaches to data, and the digital tools we used to work with this data. We explain how each data set was selected, and detail every transformation the data goes through as well as the choices we made along the way and why. You will also find illustrations of what we found interesting in the data, and examples of how we might use it in a humanities project, but what we are most interested in is demystifying data work: encouraging you to pick up the tools and play along with us. Let’s see what stories you find.

Data TRIKE core principles

  1. The idea of “raw data” is misleading. Data become data when they are identified as such. And thus the process of identifying data is, itself, a fertile object of humanistic interrogation.
  1. Datasets are not sufficiently self-explanatory. Data are best delivered alongside an explanation of their creation and any transformational process they have gone through to get to their audience. How data are identified, so-called “cleaned”, visualized and analyzed becomes, itself, an object of study when using Data TRIKE.
  1. Transparency facilitates robust research practices. The choices we make working with data can have unrecognized ramifications. Sharing the base dataset, when possible, and the data at each stage of transformation gives the most complete picture and allows for peer review of practices and results.
  1. There is room for more evolution and formalization in the teaching and practice of Humanities-centered data research. In particular, there is room to create new discussion spaces for DH practitioners to discuss their data choices, and also room to expand student access to datasets and research developed expressly for the purposes of teaching and learning. We hope Data TRIKE will inform developments in those areas in the future.

You can view the method we used to create our case studies on the Data Trike Method page.

We hope you enjoy the following transformations:

Please see our inspirations, explore further readings and projects on our Resources and Inspirations page.

We want to know what you think of this project. Please help us improve our work by answering a few brief questions on this Data TRIKE feedback page.

P.S. In case you were wondering, TRIKE stands for Transformational Repository for Instruction, Knowledge, and Explication.