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Project TRIKE — Project Proposal

MADH/MALS 75500 – praxis pitch summary

Nancy Foasberg, Hannah House, Rob Garfield

We’re presenting our summary as an FAQ for maximum clarity. Attached at the bottom are the key elements from our full project proposal.

What is Digital Humanities Data?

With the explosion of Intro to Digital Humanities (DH) courses, increasing numbers of humanities students are working with datasets. Digital humanities data is most commonly text, but can also be numbers, images, film, sound files, or can come in other forms. Datasets, however, are not sufficiently self-explanatory. As Gehru et al. propose in Datasheets for Datasets, data is best delivered alongside an explanation of its creation, as these circumstances shape the way it can be worked with and what conclusions which can be drawn. Our project seeks to present DH datasets in a more transparent, thoughtful way, highlighting both the decisions made during the process and the methods by which practitioners make sense of them.

What questions are we answering? What problems are we solving?

  • How can we better present digital humanities datasets to communicate the choices made during data creation and manipulation and shine a light on how they shape analysis and the conclusions that are drawn?
  • DH curricula vary widely; methods of teaching data are still far from standardized. This project aims to develop a method of data presentation in the hopes of moving toward a more broadly applicable standard.
  • What data resources would be useful in digital humanities courses? This project will establish an open educational resource that may fulfill some of these needs.

What is the contribution to the field?

  • Supports DH pedagogy, particularly the growing number of Intro to DH courses, by providing professors and students with a collection of humanities datasets containing step-by-step examples of the data preparation and analysis process. This approach will demonstrate a humanistic approach to interrogating digital data.
  • Provide an open educational resource that allows DH instructors to focus on specific parts of the quantitative research process.
  • Model the presentation of research data alongside information about the creation and transformation of the data, in order to better contextualize research findings and other conclusions drawn from that data. The intent is to establish a convention that contextualizing information is always communicated alongside the datasets themselves.

What is the output?

  • The target output for this semester, our minimum viable product, is a website featuring at least 3 datasets, each on its own page and accompanied by information on its origin, transformations, and discussion of how decisions made during this process shape the conclusions that can be drawn from the data. A major consideration in the selection of datasets will be variety — that is, ideally, each dataset will represent a different type of data. The website will also include an About This Project page which will explain our project history and goals, thoughts on next steps, and provide contact information and/or a form for site users to give feedback. This website will be available for community review and potential in-class use for the Fall 2019 semester.
  • We have carefully tailored this proposal to ensure that a team of 4-5 people can feasibly complete work on the product by the end of spring break. A week-by-week work plan listing major activities and milestones is in development. We have procured faculty and technical advisement. Hosting will be on the CUNY Commons or other CUNY space. No extra budget is required.

Who is the audience / user?

We have three intended audiences:

  1. Instructors teaching Intro to Digital Humanities courses may use this tool in a course module on humanistic interrogation of data. We have tentative buy-in on some Fall 2019 use at the GC;
  2. Students of digital humanities may use this as a reference for examples of how data result from a series of choices that influence the conclusions they may draw when using processed data for analysis. This will help students draw more robust and meaningful conclusions; and
  3. Digital humanities practitioners, who we hope will find this method of presenting data broadly useful as a convention for the field. We will seek feedback from DH practitioners about whether our model is successful in this respect.

What are the sources? Will we need permissions?

  • As part of the research component of this project, we will identify 3 datasets that are openly licensed, are in the public domain, or for which we can easily obtain permission. One potential source we may use is a TEI-tagged Open Editions text, with project owner Jonathan Reeve’s permission.  We will also consult with Professor Matthew Gold and other faculty to help identify datasets.

Who is already involved?

  • Project staff
    • Nancy Foasberg: design, development, OER perspective
    • Rob Garfield: design, development, pedagogy perspective
    • Hannah House: project management/development, design, data critique perspective
  • Project advisors
    • Matthew K. Gold: Faculty perspective advisement
    • Jonathan Reeve (PhD candidate at Columbia): Technical advisement

Who might like to join our project?

  • We are all highly motivated and have a supportive, collaborative, friendly group dynamic. We are looking for positive and enthusiastic teammates to keep the good times rolling. Our primary need is for someone skilled at outreach. We also welcome participation from anyone excited about data, especially if you have a dataset in mind you think we should include, or if you are knowledgeable about pedagogy and methodologies for working with data. Of course, those who have  expertise in UX also have a lot to contribute! Let’s make something together!

 

Open the full(er) proposal (note that you will need to be logged into the Commons to open it): Project Trike proposal

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