Metadata provides structure and organization, both enabling and constraining searches, Big Data analysis and the use of other tools to look at what it tags. It’s sometimes invisible to users, often ignored by them in any case, and yet can heavily influence the materials they consider as well as the results they receive when applying analytic tools. There’s also a deep tension between having clear and consistent tags which convey information in a brief and precise manner, and tags which capture provisional or uncertain information or permit for the range of fuzziness which often arises in non-computing spaces. For example, the metadata on EEBO-TCP texts whose dates are conjectural defaults to the beginning of the century they were likely written in, meaning that various sorts of analysis will find spikes in 1501 or 1601 or 1701 because those dates match the metadata entries for these texts.

I’d like to invite a conversation between participants which considers both the practical, on-the-ground realities of making metadata for search and designing search tools to draw out the meaning of metadata, as well as the broader theoretical issues involved in placing a definitive tag on material which may be quite indefinite. (Does Shakespeare’s [i]King Lear[/i] receive a tragedy or history tag, for example?)

Databases are my friends. Every application that I use and care about has a database built into it somewhere: digital audio workstation, video editor, address book, calendar, to-do list (don’t forget online shopping…). Important research is predicated on the analysis of databases. Increasingly, many of my favorite works of art are driven by databases. I’m inspired by civic hacking projects made possible by the open data movement.

So why is it such a pain to create and work with databases? How come we still reach for a spreadsheet when we know darn well a database would be better? Would the world be a better place if everyone databased? Has the NSA made database a dirty word? What tools do we need to make databasing more like sledding?

Come share your love/hate experiences with databases. Teach us about your favorite tools for making life with databases more fulfilling. Let’s imagine together what will replace databases as we know them.

This session will have elements of teach and play.  Bring your favorite web-based text analysis tool (i.e., virtually, of course) and share what you like, what it does well, and what you wish it did.  I’ll start with Textal.

The more information is collected, the more difficult it becomes to sift it, organize it, and find what you need.

Statistical data analysis (data analytics) is the process of this type of analysis, encompassing natural language processing and more.  This is what comes after gathering “big data”.  Imagine taking the contents of 2 or more online databases, combining them, and then sifting through them for relationships, correspondences, linkages, similarities and differences and more.

Data analysts are increasingly in demand by large research institutes, big business, and government agencies.  How will we fill the same need for humanities scholars?  Where could we find the funding?  How do we make this happen?

I for one am very interested in pursuing this type of career, but would like to do so in support of scholars, so I’m very interested in your thoughts about this.