Note: As the dh+lib Review editors work behind the scenes this summer, we have invited a few members of our community to step in as guest editors and share with us what they are reading and why the dh+lib audience might want to read it too. This week, we hear from Pamela Andrews, Repository Librarian for scholarly Works in the University of North Texas Libraries’ Digital Curation Unit.
There’s definitely a theme in my readings this summer as I find myself pulled deeper into questions about research design, methodology, and assessment. Interdisciplinarity is certainly a strength in digital humanities and library science, but it comes with its own challenges regarding the methodologies and underlying values that guide research design. The readings below have been touchstones for me to consider when making these decisions for myself: how can I draw on the values from these different areas for my own work?
Rawson, K. and Muñoz, T. (2016, July 6). “Against Cleaning.” Curatingmenus.org. http://curatingmenus.org/articles/against-cleaning/
While Digital Humanities and Library Science often overlap, it seems that just as often their methodologies come from very different sets of values. It can be hard to understand these differences when you’re standing on the other side, which is why I appreciate this article situating its critique of data cleanup within the humanities field, while also demonstrating what this “critically-attuned data work” might look like. Katie Rawson and Trevor Muñoz reflect on their work on the New York Public Library Curating Menus Project to interrogate what it means to clean data, how it matches against current humanistic critiques of reductionism, and how library frameworks for indexing and authority can help answer these challenges.
Other People’s Data
Allison, S. (2016, December 8). “Other People’s Data: Humanities Edition.” CA: Journal of Cultural Analytics. http://culturalanalytics.org/2016/12/other-peoples-data-humanities-edition/
With the increasing spotlight on open access, open data, and reproducibility, I find myself thinking more about how data exists outside of the immediate research project and how it can be used by others. Here, Sarah Allison addresses the need to make better use of discarded data, what she phrases as “data recycling to combat data waste.” Her article engages with a project website created by Ted Underwood and Andrew Goldstone to accompany their essay “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us” to examine what it looks like to reproduce this kind of humanities research. This approach can be used to both explore the choices these authors made, and as a generative resource for new lines of inquiry.
From Reproducible to Productive
Goldstone, A. (2017, February 27). “Other People’s Data: Humanities Edition.” CA: Journal of Cultural Analytics. http://culturalanalytics.org/2017/02/from-reproducible-to-productive/
Andrew Goldstone’s response to Sarah Allison’s “Other People’s Data” takes a deeper look at what exactly makes data reproducible. For Goldstone, this approach means making visible the transformations used for data analysis, through both the interpretive and mechanical choices. This exposure allows for others to follow along the path set out, produce further analyses from this data, or even prove the initial approach wrong. Regardless, all three paths increase the body of knowledge and potential for collaboration. More importantly, for me, it points to how scholarship can become more generous and collaborative if the process and the product are made visible–warts and all.
Data Humanism: The Revolutionary Future of Data Visualization
Lupi, G. (2017, January 30). “Data Humanism: The Revolutionary Future of Data Visualization.” PRINT Magazine. http://www.printmag.com/information-design/data-humanism-future-of-data-visualization/
Putting aside the urge to define digital humanities, one common challenge that strikes me is how those of us engaged in this work convey its complexity. Moving work from the command line to a user interface, whether print or born-digital, draws on a visual skill set. Giorgia Lupi looks at how we reconnect data to the knowledge, behaviors, and people it represents. Alongside a brief history of data visualizations, Lupi discusses a project using hand drawn data visualizations to understand how we can convey data in a way that carries the personal nuances and interpretations made by the research or analyst–making it more personal and less sterile.