Week 3: Dawn of the Data

facilitated by Eden Wetzel

Introduction

What is data? How do we know when we have successfully collected it, and when we have something “missing”? How can we organize and manage this data to benefit our scholarly endeavors while preserving the full value of this data?

This week we read about how humanists define data, whether the data collected needs to be “cleaned” or “tidied” and what constitutes good and bad data. We also read about the dilemma of “missing” data and what these missing puzzle pieces can tell us. This week we will discuss our thoughts and takeaways from our readings, and gain a better understanding of some of the concepts and dilemmas the authors raised in their articles.  

Discussion Questions

1. Pulling from Miriam Posner’s article, what is the contradiction she is referring to and how might this affect the way we manage and analyze humanities data?
2. How might data modeling and data management in the humanities differ from the same practice in the sciences? What can these two fields learn from one another? 
3. What is your take on the idea that data may be “dirty”? Have you ever attempted to clean a data set? How was that experience? Was it beneficial to your work or your research?
4. Do you agree or disagree with the authors’ sentiments that we should be against cleaning?
5. Why do you think many humanities researchers do not document their data collection and management practices?
6. What are some of the benefits and drawbacks of categorizing humanities data?
7. Mimi Onuoha highlights the dilemma of missing data with the Library of Missing Datasets installation. What can the absence of data tell us? Is missing data always a bad thing?