Research Records Stewardship Guidance Procedure

The University of Alberta has just passed a Research Records Stewardship Guidance Procedure which says that we “are responsible for the stewardship of the research records created, acquired, managed or preserved.” The procedure specifically says,

The Principal Investigator (PI) is responsible for the collection, maintenance, confidentiality, and secure retention of research records until such time as the University may assume responsibility for their management and preservation.

The good news is that we have excellent support in the Library for dealing with research records. We have the Education and Research Archive where we can deposit data. We also have staff in the Digital Initiatives unit of the Library who can help us develop research management plans.

I joined forces with Geoff Harder and Chuck Humphrey to give a presentation on Data Management Plans (my slides).

Pentametron: With algorithms subtle and discrete

Scott send me a link to the Pentametron: With algorithms subtle and discrete / I seek iambic writings to retweet. This site creates iambic pentameter poems from tweets by looking at the rythm of words. It then tries to find ryhming last words to create a AABB rhyming scheme. You can see an article about it on Gawker titled, Weird Internets: The Amazing Found-on-Twitter Sonnets of Pentametron.

Vicar – Access to Abbot TEI-A Conversion!

The brilliant folk at Nebraska and at Northwestern have teamed up to use Abbott and EEBO-MorphAdorner on a collection of TCP-ECCO texts. The Abbot tools is available here, Vicar – Access to Abbot TEI-A Conversion! Abbot tries to convert texts with different forms of markup into a common form. MorphAdorner does part of speech tagging. Together they have made available 2,000 ECCO texts that can be studied together.

I’m still not sure I understand the collaboration completely, but I know from experience that analyzing XML documents can be difficult if each document uses XML differently. Abbot tries to convert XML texts into a common form that preserves as much of the local tagging as possible.

Social Digital Scholarly Editing

On July 11th and 12th I was at a conference in Saskatoon on Social Digital Scholarly Editing. This conference was organized by Peter Robinson and colleagues at the University of Saskatchewan. I kept conference notes here.

I gave a paper on “Social Texts and Social Tools.” My paper argued for text analysis tools as a “reader” of editions. I took the extreme case of big data text mining and what scraping/mining tools want in a text and don’t want in a text. I took this extreme view to challenge the scholarly editing view that the more interpretation you put into an edition the better. Big data wants to automate the process of gathering and mining texts – big data wants “clean” texts that don’t have markup, annotations, metadata and other interventions that can’t be easily removed. The variety of markup in digital humanities projects makes it very hard to clean them.

The response was appreciative of the provocation, but (thankfully) not convinced that big data was the audience of scholarly editors.

Virtual Research Worlds: New Technology in the Humanities – YouTube

The folk at TextGrid have created a neat video about new technology in the humanities, Virtual Research Worlds: New Technology in the Humanities. The video shows the connection between archives and supercomputers (grid computing). At around 2:20 you will see a number of visualizations from Voyant that they have connected into TextGrid. I love the links tools spawning words before a bronze statue. Who is represented by the statue?

Continue reading Virtual Research Worlds: New Technology in the Humanities – YouTube

Tasman: Literary Data Processing

I came across a 1957 article by an IBM scientist, P. Tasman on the methods used in Roberto Busa’s Index Thomisticus project, with the title Literary Data Processing (IBM Journal of Research and Development, 1(3): 249-256.) The article, which is in the third issue of the IBM Journal of Research and Development, has an illustration of how they used punch cards for this project.

Image of Punch Card

While the reproduction is poor, you can read the things encoded on the card for each word:

  • Location in text
  • Special reference mark
  • Word
  • Number of word in text
  • First letter of preceding word
  • First letter of following word
  • Form card number
  • Entry card number

At the end Tasman speculates on how these methods developed on the project could be used in other areas:

Apart from literary analysis, it appears that other areas of documentation such as legal, chemical, medical, scientific, and engineering information are now susceptible to the methods evolved. It is evident, of course, that the transcription of the documents in these other fields necessitates special sets of ground rules and codes in order to provide for information retrieval, and the results will depend entirely upon the degree and refinement of coding and the variety of cross referencing desired.

The indexing and coding techniques developed by this method offer a comparatively fast method of literature searching, and it appears that the machine-searching application may initiate a new era of language engineering. It should certainly lead to improved and more sophisticated techniques for use in libraries, chemical documentation, and abstract preparation, as well as in literary analysis.

Busa’s project may have been more than just the first humanities computing project. It seems to be one of the first projects to use computers in handling textual information and a project that showed the possibilities for searching any sort of literature. I should note that in the issue after the one in which Tasman’s article appears you have an article by H. P. Luhn (developer of the KWIC) on A Statistical Approach to Mechnized Encoding and Searching of Literary Information. (IBM Journal of Research and Development 1(4): 309-317.) Luhn specifically mentions the Tasman article and the concording methods developed as being useful to the larger statistical text mining that he proposes. For IBM researchers Busa’s project was an important first experiment handling unstructured text.

I learned about the Tasman article in a journal paper deposited by Thomas Nelson Winter on Roberto Busa, S.J., and the Invention of the Machine-Generated Concordance. The paper gives an excellent account of Busa’s project and its significance to concording. Well worth the read!

Juxta Commons


From Humanist I just learned about Juxta Commons. This is a web version of the earlier downloadable Java tool. The new version still has the lovely interface that shows the differences between variants. The commons however, builds on the personal computer tool by being a place where collations can be kept. Others can find and explore your collations. You can search the commons and find collation projects.

Another interesting feature is that they have Google ads if you search the commons. The search is “powered by Google” so perhaps that comes with the service.

Pundit: A novel semantic web annotation tool

Susan pointed me to Pundit: A novel semantic web annotation tool. Pundit (which has a great domain name “”) is an annotation tool that lets people create and share annotations on web materials. The annotations are triples that can be saved and linked into DBpedia and so on. I’m not sure I understand how it works entirely, but the demo is impressive. It could be the killer-app of semantic web technologies for the digital humanities.

War and Peace gets Nookd

From Slashdot I found this blog entry Ocracoke Island Journal: Nookd about how a Nook version of War and Peace had the word “kindle” replaced by “nook” as in “It was as if a light has been Nooked (kindled) in a carved and painted lantern…” It seems that the company that ported the Kindle version over to the Nook ran a search and replace on the word Kindle and replaced it with Nook.

I think this should be turned into a game. We should create an e-reader that plays with the text in various ways. We could adapt some of Steve Ramsay’s algorithmic ideas (reversing lines of poetry). Readers could score points by clicking on the words they think were replaced and guessing the correct one.