First of all, register an account on HTRC portal on the production stack, from where you will access the HTRC Data Capsule.
Install a VNC Client on your computer to enable the communication between your computer and the capsule, which is a virtual machine, to be created. You can choose any VNC client you prefer.
We use VNC View for Google Chrome in this tutorial so also recommend people install the same. Install and launch the app.
Getting Familiar with the Data Capsule
Log in to the HTRC portal where you just created an account and sign in. Create a capsule by clicking on the "HTRC Data Capsule" -> "Create Virtual Machine" on the top of the page. You will be asked to provide information about the capsule you would like to create.
Start the capsule you were assigned by clicking on the "Start VM" button on the Virtual Machines list page (make sure you have logged in the portal in order to see the page).
After starting the capsule, you can connect to and operate on the capsule via the VNC Client you just installed. Use the "Host Name" and "VNC port" fields of the capsule as input to the VNC Client: put them the "Address" field of the VNC Viewer, separated by a semicolon.
Each capsule is designed to have 2 modes: maintenance node and secure modes. Under the "Virtual Machines" page, click on "Switch to Secure Mode" or "Switch to Maintenance Mode" buttons to switch between modes.
Under maintenance mode, user is allowed to access network freely except for HTRC corpus repository and install whatever software she wants. In secure mode, network access is restricted. User is only allowed to access a few network addresses e.g., HTRC corpus repository and search service.
Run text analysis experiments in the capsule. Details of conducting experiments are demonstrated in the 4 use cases below. If users want to export results out of the capsule, they can release the result in the capsule's secure mode.
We walk participants through 4 use cases on using HTRC corpus for text analytics within the HTRC Data Capsule. For demo participants' convenience, the capsule you just requested has been pre-loaded with required R packages and the IPython tool, along with a volume ID list of the English Short Title collection. All these use cases are to be operated within the capsule.
Since it's performed within the capsule's virtual machine environment, it will be helpful to open a browser in the capsule, e.g. Firefox, and go to the url http://wiki.htrc.illinois.edu/pages/viewpage.action?pageId=22085965 or http://bit.ly/1whzT6H Then you can easily copy and paste the hyperlinks and the commands from the Wiki.
HTRC provides a search engine API, Solr API, for scholars to search volumes of their interest. Scholars can search by full-text, or MARC catalog fields. An example query is
chinkapin.pti.indiana.edu:9994/solr/meta/select/?q=title:war which returns all volumes of which the titles contain "war".
Given a list of volume IDs supplied by users, the HTRC Feature API returns a Term-Document-Matrix (TDM) for the volumes. The matrix contains term frequency count information of each volume, which can be used for further statistical analysis. In this example, we use the English Short Title Catalog's volume ID list, to request its Term-Document-Matrix from the API.
Using the returned Term-Document-Matrix, we run some R analysis and visually show some insights of the collection (English Short Title Catalog).
Use the IPython interactive interface to fetch volume content, and then run vector space model and topic modeling on volumes' OCR content.