Learning about and trying my hand at the Text Encoding Initiative (TEI) was not too strange or overwhelming for me. Our Humanities 502 class last semester did some work on html, and the TEI system (or really the XML system that structures it) did not seem appreciably different. It can be really frustrating when you get the coding wrong, and you have a hard time figuring out how to fix it! Patient and experienced project managers are truly golden for this!  It makes think back to the time we simply used a Control-F word search, and how effective that was in developing some sense of what a document contains. This might be all you need to analyze a text in some cases. Using the Voyant program takes this a step further. It can assist you in finding terms in the text that you did not think of or were unaware that were important, especially if you are examining texts with which you are unfamiliar. The bottom line for me is that while I am not drawn to undertake the actual encoding, I clearly see the utility of doing it. Preparing documents and texts so that they are manageable and pliable for analysis is a worthwhile endeavor. I can also appreciate how time-consuming and laborious (or more accurately, monotonous) the procedure can be, even in the short period of time we spent manipulating Frankenstein in TEI. This is work that requires a team with a budget.  I can see here why student labor might be a really tempting option for a scholar seeking to launch a database or a large-scale document/ text analysis project. But, again, you have to provide students something in return beyond skill building and remuneration.  Students need to be given due credit and the opportunity and guidance to engage the project beyond simply data entry and encoding. Additionally, the chapters we read about data modeling were extremely challenging for me to digest. I am not well versed about the various systems, software and critical analyses about data modeling.  Most of the time, I barely can keep up with what they are stating and arguing. However, I do understand the importance of critically analyzing the ways in which data modeling is accomplished. We have been taught to critically analyze how arguments are made and supported in books and articles. We are trained to scrutinize how and why the data is used. It makes sense that we should critically evaluate how data modeling systems are designed to manipulate and present data. Frustratingly, this is not a skill set that I yet possess.