This module offers an introduction to tabular data and learning how structuring it enable different styles of analysis.
Outcomes
- Increased understanding of what data is and why it is important to organize it in particular ways.
- Ability to “clean” or organize small sets of data using an online tool (Open Refine).
- Increased ability to see how digital projects use structured datasets to answer research questions.
Readings
- Lev Manovich, “Database as a Genre of New Media,” AI & Society 14, no. 2 (June 1, 2000). http://time.arts.ucla.edu/AI_Society/manovich.html ♦ Estimated Read Time = 15 minutes
- Robert Kosara, “Spreadsheet Thinking vs. Database Thinking,” Eager Eyes, https://eagereyes.org/basics/spreadsheet-thinking-vs-database-thinking ♦ Estimated Read Time = 4 minutes
- Hadley Wickham, “Tidy Data,” Journal of Statistical Software 59, no.10 (Sept 2014). http://vita.had.co.nz/papers/tidy-data.pdf ♦ Estimated Read Time = 35 minutes
- Lincoln Mullen, “Introduction,” Computational Historical Thinking: With Applications in R (2017): http://dh-r.lincolnmullen.com/introduction.html ♦ Estimated Read Time = 20 minutes
Questions to Consider:
- Do you agree with Manovich that “database and narrative are natural enemies”? Why or why not?
- Manovich asserts to dominance of databases in new media, but due to the age of the piece his examples (CD-ROMs, HTML only web pages) are outdated. What examples from the present day – in popular culture or your own field – would you use to support or critique his argument?
- Wickham and Kosara both discuss how to structure data in order to facilitate analysis. How is your data currently structured? What changes would you need to make to match their definitions of tidy data?
- Mullen argues for the uses of computational methods for historians. If you are a historian: do you find his argument convincing? If you are not a historian: what aspects of his argument for computational methods apply to your field?
Resource: Data sets for social scientists
Cornell Institute for Social and Economic Research’s list of freely-available datasets: https://www.ciser.cornell.edu/ASPs/datasource.asp
Resource: Manipulating Data with Excel
A basic introduction to best practices for data entry in a spreadsheet as well as some essential functions for manipulating data in spreadsheet applications (Excel, OpenOffice, Google Sheets) developed by Spencer Roberts, a graduate student at George Mason: http://swroberts.ca/academic/spreadsheets-for-historians/
If you are doing a data-driven project, we have some tips for preparing your data.
Activity: Cleaning Data with Open Refine
Follow the instructions on this page to try OpenRefine, a software tool for cleaning and transforming data to normalize and tidy two datasets: http://ssrc.doingdh.org/tidying-data-with-openrefine
Project Lens: Trans-Atlantic Slave Trade Database
Take about 20 minutes to review the Voyages: Trans-Atlantic Slave Trade Database, http://slavevoyages.org/,
What are the goals of this digital project? Who is the primary audience? What are the assets of this site?
What kind of data is available in this site, and how might it be useful for scholarly research?
What else might be possible with this type of data set as a starting point?
Take a look at this summary we created to help you walk through the site: http://ssrc.doingdh.org/project-lens-trans-atlantic-slave-trade-database