As a complement our discussions about new forms of scholarship and scholarly publication, I’d like to propose a session about how digital tools are already transforming the academic production of knowledge. Despite recent transformations, I think most scholars in the humanities still follow a research and writing model that goes back at least a hundred years:

1.) Collect information
2.) Organize information
3.) Write scholarship

As a history graduate student knee-deep in dissertation research, I’ve been surprised how little my advisors and my peers’ reflect upon their method. I suspect that their approaches to steps two and three have changed very little in the past twenty years. For most, Microsoft Word and notecards still seem to be the “tools of the trade,” with perhaps a nod to citation software (Zotero/EndNote) and data backups (DropBox).

In this session, I’d like to discuss how 21st century scholars should approach both the organizing process and (on good days) the writing process as well. Everyone has their own system I’m sure; this would be an opportunity to share our tips and tricks. How do people fit their messy data –archival photos, interview transcripts, and odd notes– into searchable databases? Or do they? Have people ditched Word for plain-text files or Scrivener? Do people hide their data away on their personal drives, or publish it online? When are digital tools helpful, and when are they just, well, fiddling? Hopefully our conversation will generate some useful guides and blog posts.

In this workshop we will provide both a general overview of the JSTOR Data for Research (DfR) service and a “how to” for using Hadoop and cloud computing for text mining large datasets. For the big data mining portion of the workshop we will be using a large dataset consisting of the JSTOR Early Journal Content (EJC) collection. A bundle of metadata and full text for the approximately 460,000 articles in the EJC collection can be downloaded from the DfR site. For this tutorial we have pre-loaded the EJC content into Amazon Web Service (AWS) data storage and will provide instructions on how to use the AWS Elastic Map Reduce (EMR) service for efficiently mining this dataset. In this tutorial we’ll show how to create an AWS account, develop and submit Map-Reduce jobs (written in Python) and retrieve results. The examples provided will include the generation of ngrams from full text and the identification of the top words in articles via the calculation of TF*IDF scores.

David Phillips and Tyler Pruitt, Wake Forest University

What do you need to consider in planning and designing a website for a DH project meant as both a resource for the public and a vehicle for outreach and public input? What strategies can you employ in creating such a site?

We would like to explore and have a discussion about experimental ideas and best practices in creating multi-modal sites that have these goals.

What platforms work the best for particular objectives, and why? What web design tools and platforms are most effective for a public humanities DH project?

If you’re in the early planning stages of site design for a DH public humanities project, come discuss your ideas, your questions and your insights.  We’ll work collaboratively on developing ideas for a ‘matrix’ of solutions that looks at the relative advantages of a variety of API, plug-ins and platforms.

There have been sessions at past THATCamps that have explored the use of 3D design to envision historical sites or perhaps to demonstrate relationships of words in a concordance or index. In those cases the examples in most cases were a transference of Atoms (papers, manuscripts, notes from conversations) to bits (program design, programming, data entry, user interfaces). For this session I would like to explore your ideas and mine about the impact of Making, Tinkering, Physical Innovation to create 3 Dimensional Objects in the Digital Humanities.

One quick example: 1) Analog — a historian discovers description, perhaps with an illustration of a piece of table ware or furniture. She notes that it has certain qualities that she’d like to explore more. This is where the project moves into 2) Digital space. The object is sketched, then rendered into a 3D representation. Then that file is may need to be translated into a format that can be used by a 3D printer. The object can be scaled down or up to fit the 3D printer that will be used to “print” the object. Once the object is “printed” it has returned to an 1) Analog object. Now the object can be closely observed to better understand those curious qualities. There are many variations on this theme. Let’s share some of them.

A true story. At the San Diego Super Computer Center at UC-San Diego there was a Laminated Object Modeling (LOM) lab. Their printer used thin paper layers pasted then cut with lasers. They created a small model of the Rocky Mountains and the Appalachian Mountains in the same scale. They were small, hand held objects. A ninth grade class on tour was shown the models and asked what did they learn from seeing these two objects. A young woman was the first to raise her hand. Her answer, “The Appalachians are older than the Rockies because they are worn down and smoother than the Rockies.” She was correct.

So, how could 3D tools and resources for Making or Tinkering be applied to ideas, questions, or the work in the Humanities you are doing?

Why should STEM (Science, Technology, Engineering, and Mathematics) have all the fun? It is time for the humanities to embrace the studio model as a pedagogical means to foster intellectual curiosity. MIT has NuVu; let’s create THATClass! Bring your ideas on partnerships, collaboration, technology integration, hands-on projects, uncovering content, and ways to apply knowledge and skills rooted in the humanities to develop the future of secondary (and post-secondary) education. ==> Saturday