Playing with collections at the Cooper-Hewitt (September 19)

1. Some terms to look up and define for your group, in your own words:

  • metadata
  • API (Hint: You’re probably going to find the PDFs at the bottom of the Wikipedia article more accessible than the Wikipedia entry itself.)
  • Creative Commons
  • public domain
  • open source
  • Omeka
  • Dublin Core

2. Visit the Cooper-Hewitt website.  What do you think the museum’s mission is?  Why would it decide make its collections data digitally accessible to so many people? How does that release of data correspond to its mission?

3. What do Ridge and Murray-John suggest are the biggest hurdles to working with collections data from museums?  Why might these hurdles exist?

4. Micah Walter writes that the visualizations he shares in his blog post “are only possible because we released the collection data as a single dump. If we had, like many museums, only provided an API, this would not have been possible (or at least been much more difficult) to do.”

  • What is the difference between releasing a collection’s metadata as a single file and providing an API to the museum’s collections?
  • What are the challenges to museum staff and end users in each scenario (API and data release in a single file)?
  • Why would a museum opt to use either approach rather than (or in addition to) sharing its collections through a web browser interface it built, as the Powerhouse Museum has done?  (See also the Cooper-Hewitt’s searchable collections database.)
  • What are the advantages and liabilities of each approach?

5. Does what you learned today, through the readings and during class discussion, change how you would approach the big data visualization project your group proposed during the last class?  If so, how and why? If not, why not?

Big Data Digital Project: Historic Treasure Valley Occupation Map

Reposting our proposal to the blog — the original is in the comments section.

Team members are Eric Schooley, April Raine, Jim Duran, Ryan Regis, and Elly Couchum.

The project we envision would involve gathering information about income, gender, occupation, ethnicity, and religion from sources such as the Polk City Directory, Census Bureau, and the IRS/State taxes. The time period we will cover would be from 1863 to the present for as much of the Treasure Valley as we can handle.

Some questions we anticipate:
What jobs were located in which areas?
Are the patterns unique or to they follow larger patterns?
What are the correlations between our factors and geography?
What kind of segregation patterns will occur?
What effect would religion have on these patterns?
How did new developments (business/housing) affect these patterns?
Were women more prone to a specific field than another?
Was education a determining factor?

The methods:
We could use Google Fusion tables to analyze spatial information. This platform easily converts place names and addresses to GIS. We can then overlay our information on a standardized map. Then we INTERPRET!!!

A modern map or historical maps with overlays similar to the San Francisco map, wherein the markers can be chosen in layers. We also envision intensity based markers when the data can be applied as such. More detailed information would be available by zooming into a plat. (POLK) (bureau of labor statistics)

Dan, Travis, Jon, Charles

1.  We would use census data for eligibility of voters, party affiliation, party candidates between 1948 and 2012.

2.  Questions….major political issues, demographic of voters, party affiliation, gender of voters between 1948 and 2012,  mapping, education levels and military experience

3.  We would use statistical analysis, graphing, interactive mapping, education levels and military service

4.  We would differ from existing data and maps by using linear regression and individual maps of districts



In-Class Excercise

1.) ; its called the national archives experience. also helps with actual numbers data. We picked a document about amendment #26, dropping the voting age to 18.


2.) Some questions a historian might ask could include things like if this amendment effected anything the first year this was in effect. Another might be what percentage were actually 18.


3.) Some methods would include using a number of different websites to see if all the data was the same. Another method could be seeing who the people are voting wise back in the day.


4.) The most useful visualizations would be graphs and maps about how many people and who all voted…etc


Names of group: Michael Winters, Stephen Gentry, Sky Winter



Data, visuals, and visualization (September 17)

Resources for in-class discussion

Map of Salem Village and map of witchcraft accusers and accused

Cholera in London

Napoleon’s invasion of Russia in 1912

On the Origin of Species: The Preservation of Favoured Traces

Animal City

Digitally reconstructing Washington, DC as it appeared circa 1814

Chronozoom project

Name Voyager and Name Mapper


1. How might a humanist approach or use data sets differently than a scientist would?

2. Why might historians want to create visualizations?

3. What are the advantages and liabilities (for historians and their audiences) of transforming data into visualizations?

4. Which of the visualizations in the reading, or at the links above, do you find particularly interesting or persuasive, and why?  Which ones are less interesting or persuasive?

An in-class exercise

1. Find online either (a) sources that you could convert into data or (b) an existing dataset drawn from primary sources.

2. What questions might an historian ask of this data?

3. What methods might the historian use to make sense of this data?

4. What kind(s) of visualization(s) do you think would be most useful to (a) the historian as she conducts her analysis and (b) the audience for her work?

5. Post your responses to these questions, along with a link to the data or dataset you used in your example, to the course blog.  In the category list, check the box for “data experimentation.”  Be sure to include the first names of everyone in your group.