Tag Archives: visual analysis

Connecting the Public to Public History

Nam-ho Park, “A Half-Day Walk through Hanoi,” CC license

There are many implementations and activities that can connect the public to public history using online digital collections. As Sheila A. Brennan and T. Mills Kelly wrote in 2009, the digital humanities are comfortable with the “read-write web.” The Web 2.0 (or 1.5 as they call it) allows public historians to collect and share the stories and narratives of people through their direct participation. Digital history project also benefit from the volunteer efforts of people to identify and enhance their narratives, help to piece together the narratives of others, and provide valuable information and context.

A great example of this can be found in the project “Invisible Australians” that used a facial detection script, tagging, photos, and people to analyze the “White Australia Policy.” Other successful crowdsourcing and public history collaborations include Flickr Commons,  created as a “forum for institutions to share their rich photographic collections. . . and provide insights into how knowledge, skill, and abilities of librarians, archives, and museums can converge in the Web 2.0 environment to provide collection access to new. . . audiences,” (Smithsonian Team Flickr). The Smithsonian Institute’s collaboration is sharing its rich photo archive with Flickr Commons has created an amazing public-private partnership.

In this spirit, the following list includes the kinds of public history implementations and activities that having a basic digital collection enables. 

  1. Tagging, Identification
  2. Transcribing
  3. Exploration
  4. Social Media
  5. Contests
  6. Visual analysis
  7. Direct Collaboration
  8. Geo-spatial mapping (see image at top of post)
  9. Memory-making
  10. Storytelling

Omeka has emerged as the premier platform for open-source digital public history projects.  With a variety of templates, plug-ins, and customization options, most of the items on the above list can be achieved using Omeka’s open source web platform.  For my own project, I will be able to use information from and about historical markers in Nashville’s downtown core. This includes temporal and geographic locations, marker text, and related primary sources. These resources could ultimately be used to create explorations via walking tours, contests for users, storytelling via historical contextualization, direct user collaboration via tagging or identification, and social media. While many of these goals remain quite distant, the fluid nature of DH and the trajectory of rapidly advancing technology make these goals possible.

It remains important to consider several factors that remain critical to the long term usefulness, credibility, and sustainability of digital archives. First archival projects need to be clearly identified. There are many genres and meanings of the word “archive” as noted by Trevor Owen. Ranging from a records or storage management system to what some critics call “artificial collections,” properly defining the mission, scope, and function of an a digital archive is essential (What Do you Mean by Archive?). Likewise the issue of metadata is important. Metadata is not always exciting on its face, but it provides the foundation on which successful digital history projects depend. As the guide for “Describing Metadata” suggests: “Metadata is the glue which links information and data across the world wide web. It is the tool that helps people to discover, manage, describe, preserve and build relationships with and between digital resources” (Describing Metadata).

Coupled with high standards of historical scholarship, digital projects can produce and make available large collections that can be used to disseminate and distribute information to the greater public while also providing countless primary sources to current and future historians. As Dan Cohen and Roy Rosenzweig emphasized in Digital History, “Collecting history through digital archives can be far cheaper, larger, more diverse, and more inclusive than traditional archives. This democratization however, does not mean compromising the quality of the historical work.” (Why Collecting History Online is 1.5).

Works Cited:

Brennan, Sheila A., and T. Mills Kelly. “Why Collecting History Online is Web 1.5” Roy Rosenzweig Center for History and New Media. 2009.

JISC Digital Media. “Metadata: An Introduction.” (first section – “From Metadata: a definition” to “Metadata often reflects the community it has come from.”

Kalfatovic, Martin et al. “Smithsonian Team Flickr: a library, archives, and museums collaboration in web 2.0 space.” Archival Science (October 2009).

Owens, Trevor. “What Do You mean by Archive? Genres of Usage for Digital Preservers.” The Signal: Digital Preservation (blog), February 27, 2014.

Sherratt, Tim. “It’s All About the Stuff: Collections, Interfaces, Power, and People.” Journal of Digital Humanities 1.1 (Winter 2011).

Visual Tools: Voyant, CartoDB, and Palladio

New web-based, open source technology has dramatically shifted the landscape of digital humanities. It has affected fields related to  digital humanities in two significant ways. For institutions and digital humanists a new quest to create, build, and host project sites has emerged. These digital projects allow users to interact and manipulate data in specific ways that yield almost infinite combinations. For users, these digital projects have laid the groundwork for moving research beyond the archive and to digest and draw conclusions based on datasets and information expressed through new macro-based visuals. The projects/programs reviewed here focus on textual analysis, geospatial mapping, and visual graphing based on large sets of metadata and archival information.

Voyant
Strength/Weakness: The strength of Voyant is the range of text analysis provided: cirrus, networks, graphs, context, verbal patterns. This is also its weakness. At first glance it’s very impressive but when trying to set or manipulate certain features available to the user for the purposes of customization or multiple datasets, the program does not function well.
Similarity/Difference: Voyant is similar to CartoDB and Palladio in that they are all free open-source, web-based programs. Voyant and Palladio do not require usernames or passwords. Voyant is different from CartoDB because CartoDB does require a sign-up. Voyant is different from Palladio because Voyant has one main screen with several visual fields, while Palladio only focuses on one type of visual analysis at a time, i.e. maps or graphs.
Complement: Voyant provides sophisticated text analysis and CartoDB provides sophisticated geographical analysis. Paired together, they provide unbelievably rich yet simple ways to “see” data relationships. Palladio and Voyant complement one another because they allow users to layer and filter the same data to produce different types of word graphs, clouds, and networks.

CartoDB
Strength/Weakness: The strength of CartoDB is the visual clarity and graphic options for its maps. The program’s weakness is that it really only serves to create maps and not graphs or other visual organizers. As a side note, this could just as easily be a strength because it does one thing well.
Similarity/Difference: CartoDB is similar to Palladio in that it focuses on one type of visualization, which it does very well. It is different in that its foci are  maps=CartoDB and graphs=Palladio. CartoDB is similar to Voyant on a basic level; they both produce visual graphic representations of the relationships within a large set of data. They are different because Voyant attempts to do many things (but not geospatial mapping), while CartoDB focuses on geography and not text.
Complement: CartoDB and Voyant complement each other well for the same reasons that they differ (above). Voyant does what CartoDB does not and vice versa, so together they provide an even more comprehensive picture of patterns that can be draw from data. Palladio and CartoDB complement one another because each does a different thing well. I would be tempted to use these two rather than Voyant because they are both user friendly.

Palladio
Strength/Weakness: The strength of Palladio is its relatively easy interface and the ability to drag and organize nodes and lines. The weakness of Palladio is the inability to save projects in formats other than svg or json, and that beyond the visual graphing network there is no additional information.
Similarity/Difference: It is similar to CartoDB in that it does have a map function, but Palladio is different because the most effective feature is visual network graphs. Palladio is similar to Voyant in that they both have word links and network features. They are different because Voyant is difficult to use (because of glitches not design), while Palladio is much easier to use.
Complement: Palladio complements Voyant by providing more options for word clouds and visual networks. Palladio provides a complement for CartoDB as they are both based on layering datasets manually with selecting different modes and filters.

As these open-source programs continued to “hone their skills” and “work out the kinks,”  they will no doubt provide continued and enhanced methods of data analysis that can be customized for and by individual interests.