Hello, fine classmates.
Word on the street is we didn’t do a great job explaining Project T.R.I.K.E. during our presentation. ¯\_(ツ)_/¯ So here is Take 2 – below please find some vignettes highlighting different ways that Project T.R.I.K.E. can help students and professors.
Graduate student A:
Grad student A is named Hannah. She learned a little about data critique and bias during her summer data visualization course and she wants to learn more. After reading Raw Data is an Oxymoron and Data Feminism she starts googling for examples of data critique on datasets and comes across Project T.R.I.K.E. – the first attempt at putting critique into practice alongside real datasets. Looking at the various datasets in various stages and being able to read statements about the biases and choices at each step gives great real world examples of the things she’d only read about data transformation and the meanings behind it. She goes on to co-found Project T.R.I.K.E. Wait a minute… oh no, she’s stuck in a time loop!*
Undergraduate student B:
Undergrad student B is taking an Intro to DH course at a large public university on the left coast. As an optional extra credit assignment, the professor suggests students go on the T.R.I.K.E. website and write a report about decisions made in one of the lesson plan datasets, including suggestions on how different decisions could have been made with the data and how that would have impacted analysis. Student B does a great job on his extra credit, which pushes his grade just into passing, saving him the thousands of dollars he expected to have to pay to retake the course. He invests those savings wisely in renewable energy and gets really rich.
Professor C provides their own datasets to undergraduate students to clean up and work with in order to build a network analysis in Gephi, but wants to give them example of process and how the data needs to be structured in order to be fed into Gephi. They points their students to T.R.I.K.E., where they have posted a sample dataset and a tutorial taking the demo dataset through steps of cleanup and preparation for Gephi. The students still need to go through the whole prep process with their own datasets.
Part of Professor D’s course for graduate students is an assignment to find a dataset and perform an analysis. Professor D prefers to leave the assignment unstructured so that students have maximum freedom of interpretation, but he does provide Voyant and Mallet as examples of textual analysis tools that can be used, and does include a link to T.R.I.K.E. as an optional project resource. About ⅓ of Professor D’s students check out T.R.I.K.E., which is totally fine. Nobody has to use it. It’s just an optional resource.
Humanities Librarian E:
Humanities Librarian E maintains a DH community website with an extensive list of resources and tools for performing various types of DH work. He adds T.R.I.K.E. to his site. He gets stuck in a time loop too.*
Professor G is teaching a graduate course on working with data and wants her students to learn how to think critically about the decisions they make when working with data. As a term project, she breaks her students into groups and has each group produce a dataset and “clean” and prepare it for analysis.
The groups post all their work to T.R.I.K.E., where they use T.R.I.K.E.’s built in discussion feature to discuss the decisions behind why they collected data they way they did, potential biases introduced at each stage of cleaning and reduction, and a critical meta-analysis of what their data analysis can and can’t be relied up to explain.
All of the students give Professor G reviews as good as they would have given an equivalent male professor.
Professor H is teaching an undergraduate intro to DH course. He need to find humanities datasets for his students to work with, from which he knows they will be able to draw meaningful conclusions when analyzed. Professor H finds many options on T.R.I.K.E., and downloads his favorites to distribute to students for projects. The file downloads impressively fast, and the zip he receives is well organized with all parts clearly labeled. He smiles.
Graduate Student I:
Grad student I is pursuing a PhD in history but is increasingly interested in DH tools. They want to just try some things out for themself before committing to taking any classes. They find a link to T.R.I.K.E. on Humanities Librarian E’s DH site, download the original dataset from a T.R.I.K.E. network analysis lesson plan, and follow along with the transformations shown on T.R.I.K.E. to prepare the data for use in Cytoscape. This scaffolding well prepares Graduate Student I for his next attempt at network analysis using data he collects and preps himself.
*There is a statistically insignificant chance that using T.R.I.K.E. will imprison you in looped time forever.