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Preparation

First of all, register an account on HTRC portal on the development stack, from where you will access the HTRC Data Capsule. 

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Install a VNC Client on your computer to enable the communication between your computer and the Virtual Machine (VM) 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.

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Getting Familiar with the VM

Log in to the development portal where you just created an account and sign in. Create a VM (virtual machine) by clicking on the "Experimental Analysis" -> "Create Virtual Machine" on the top of the page. You will be assigned a VM after submitting the VM Creation page.

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Start the VM you were assigned by clicking on the "Start VM" button on the Virtual Machines list page

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After starting the VM, you can connect to and operate on the VM via the VNC Client you just installed. Use the "Host Name" and "VNC port" fields of the VM as input to the VNC Client.

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The VM 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.

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Run text analysis experiments in the VM. Details of conducting experiments are demonstrated in the 4 use cases below. If users want to export results out of the VM, they can release the result in the VM secure mode.

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Use Cases

We walk participants through 4 use cases on using HTRC corpus for text analytics within the HTRC Data Capsule VM. For demo participants' convenience, the VM 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 VM.

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 http://chinkapin.pti.indiana.edu:9994/solr/meta/select/?q=title:war which returns all volumes of which the titles contain "war".

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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 collection's volume ID list, to request its Term-Document-Matrix from the API.

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Using the returned Term-Document-Matrix, we run some R analysis and visually show some insights of the collection (English Short Title Collection).

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Use the IPython interactive interface to fetch volume content, and then run vector space model and topic modeling on volumes' OCR content.

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