In the manner of the digital culture it is intimately engaged with, Digital Humanities itself undergoes change rapidly, so that now, only 8 years after that “moment of definition,” we are once again asking questions of who we are and what it is we do. Specifically, how does DH work “in the moment” and why have our embraced definitions been noticeably devoid of an integrated activist component.

Live Love Hope demonstrates how a digital platform can be designed to ethically respond to the needs of vulnerable and marginalized Caribbean communities while representing them from elsewhere.

Additionally, Dawes was inspired to create a collection of poetry from the experience and to perform the poems to music. Still unsatisfied with these results, Dawes further collaborated with the Pulitzer Center to create Live Hope Love, an interactive web project that combined much of the above materials, with additional video interviews, music and photography.

Fifth, we have a desperate need for help with data-modeling — and here is another place where I think libraries could really play a big role. This July, I’ll be directing a summer institute on digital humanities and art history, and as I’ve been reading through the participants’ project ideas, I’ve been struck by how often it seems that what the scholar really needs is data-modeling advice. For example: the art historian who wants to show how and when art objects traveled across the Indian Ocean and relate that movement to corresponding changes in artistic practice.

Third, it’s just awful trying to find a humanities dataset. There are various humanities data repositories or registries, but they’re terribly limited. And right now we’re starting to see museums and cultural institutions releasing their data, and there’s just no way to know who’s released what, unless you’re the kind of person who stays on top of these things. So we urgently need some help locating these datasets, aggregating them, and perhaps even linking them.

This is not a perfect analogy, but imagine that someone called your family photograph album a dataset. It’s not inaccurate per se, but it suggests that this person just fundamentally doesn’t understand why you value this artifact. And it’s the same with humanists. With a source, like a film or a work of literature, you’re not extracting features in order to analyze them; you’re trying to dive into it, like a pool, and understand it from within.

One way to think about how the process of topic modeling works is to imagine working through an article with a set of highlighters. As you read through the article, you use a different color for the key words of themes within the paper as you come across them. When you were done, you could copy out the words as grouped by the color you assigned them. That list of words is a topic, and each color represents a different topic. Note: this description is inspired by the following illustration from David Blei’s article [pdf], which is one of the best visual representations of a topic I’ve found.

This may seem counterintuitive if you’re planning to use topic modeling to help you find out more about a large corpus, and yet it is very important that you at least have an idea of what should be there. Topic modeling is not an exact science by any means. The only way to know if your results are useful or wildly off the mark is to have a general idea of what you should be seeing.

With some tools, you will have to prepare the corpus before you can topic model. Essentially what you have to do is tokenize the text, changing it from human-readable sentences to a string of words by stripping out the punctuation and removing capitalization. You can also tell it to ignore “stopwords” which you define, which usually include things like a, the, and, etc. What you (hopefully) end up with is a document with no capitalization, punctuation, or numbers to throw off the algorithms.

A prototypical example of smart data are scholarly digital editions produced using the Guidelines[8] of the Text Encoding Initiative. Technically, TEI documents are usually considered semi-structured; usually, they follow a data model expressed in a schema, but such schemas allow for considerable flexibility. In addition to a very clean transcription of the text, digital editions using TEI can make a lot of information explicit: first of all, TEI files contain not just the full text, but also metadata associated with the text (in the teiHeader section); also, the data is structured and explicit: there is markup making the structure of the text explicit, identifying parts, chapters, headings, paragraphs, as well as page and line breaks, for example. Finally, many more types of information can be specified: for example person names in a novel or play, place names in a letters or documents, and many more things; and links to other parts of the documents and to external documents. Making all of these things explicit allows to visualize them in specific ways and to index, count and analyze them computationally.

What difference does it make to analyze the digital representation or version of a novel or a painting instead of the printed book, the manuscript, or the original painting?

Projects can involve partner institutions suchas museums, libraries, and archives as well asmembers of the community, alumni, and membersof interested virtual networks such as collectors,amateur historians, and the like.Partnerships with corporations, in particularmedia and technology companies, are also possible,with a caveat that corporate and academic culturesmay be different in their goals and values

The challenges include addressing fundamentalquestions such as: How can skills traditionally usedin the humanities be reshaped in multimedia terms?How and by whom will the contours of cultural andhistorical memory be defined in the digital era?How might practices such as digital storytellingcoincide with or diverge from oral or print-basedstorytelling? What is the place of humanitas in anetworked world

Digital Humanities is defined by the opportunitiesand challenges that arise from the conjunction of theterm digital with the term humanities to form a newcollective singular

An oral historian makes tape-recordings of interviews with members ofa particular ethnic group. Intervieweessign a paper release form givingintellectual property rights to the historian.Most interviewees grant permission todisseminate the interviews in print andelectronically, but several restrictpublication and dissemination until 25years after death.Information about each interview iskept in a database: Interviewer,Interviewee, Date, Place, etc. Eachinterview follows a questionnaire format.The questionnaire exists as a text file. Thetapes, release forms, database, and textfile are donated to a library that has aspecial collection focusing on the particularethnic group.The tapes are digitized. Since eachinterview runs over several tapes,technicians record structural metadata tokeep component parts of each interviewtogether. Technicians recordadministrative metadata such as filenames, location of each interview in thefiles, equipment used, the methods ofdigitizing and assuring quality andcompleteness, file formats, etc. Differentsegments of this metadata allow the audiofiles to be automatically tracked, accessed,stored, refreshed, and migrated.An archivist expands the database toinclude the persistent identifier of eachinterview, thereby linking the audio file tothe descriptive metadata. The names ofthe data elements a

Metadata is key to ensuring thatresources will survive and continueto be accessible into the future.Archiving and preservation requirespecial elements to track thelineage of a digital object (where itcame from and how it has changedover time), to detail its physicalcharacteristics, and to document itsbehavior in order to e

Most current metadata effortscenter around the discovery ofrecently created resources.However, there is a growingconcern that digital resources willnot survive in usable form into thefuture. Digital information is fragile;it can be corrupted or altered,intentionally or unintentionally. Itmay become unusable as storagemedia and hardware and softwaretechnologies change. Formatmigration and perhaps emulation ofcurrent hardware and softwarebehavior in future hardware andsoftware platforms are strategie

Noble’s central insight — that nothing about internet search and retrieval is politically neutral — is made again and again through the accumulation of alarming and disturbing examples. Image searches for “gorilla” turn up photos of African-American people. Looking for “black teenagers” returns police mug shots. Searching “professional hairstyles” returns images of white women wearing ponytails and French braids while “unprofessional hairstyles” features black women. The story told about black people online is almost entirely refracted through a white racist lens. What she surfaces online parallels extended histories of racist white representations of blackness, and black femininity in particular. The pornification of black women on the web bears echoes of Sarah Baartman’s exploitation in the 19th century, argues Noble. For all its innovation and disruption, Silicon Valley simply repeats very old racist stories.