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     Classes such as Digital Asset Management and Social Network Analysis equipped me with technological knowledge and an understanding of various softwares, technology theory, and internet protocols. Though some of these selected projects may involve competencies that are not frequently used in public libraries, I feel it is important to showcase these abilities as public libraries are constantly evolving to keep up with their communities. It is entirely possible that in the near future, public librarians will need to be equipped with this knowledge in order to meet patron needs or to keep up with trends in information. 

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Web Server Technologies

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     Digital Asset Management Technologies (ISI 6343) provided me with knowledge of systems, networking protocols and digital architectures. I left this course with an understanding of web server technologies, the functions of several different DAM systems such as Islandora, Samvera, and DSpace, and digital storage tools. I was also able to show my understanding of how to read and create a database entity-relationship (ER) model to present information.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Social Network Graphs

 

     In a special topics course, Applied Social Networks Analysis (ISI 6300), I was introduced to software like VosViewer and Gephi that allowed me to create network maps that visually represent information in an appealing way. With the many assignments to help the class practice creating network maps, I have extensive knowledge of how to translate data into node and edge lists, how to properly manage the data in these lists so it can be read by social networking technology, and how to create and optimize social networking maps through these technologies.

 

     In order to create one of my first social networking maps for this course, I searched for works on poetry published specifically in New York on the Web of Science website and aimed to display what the most common terms in these works were. In order to collect this data, I download over 500 documents acquired from my search, uploaded these documents to the VosViewer software, and extracted all of the terms used throughout these works (a feat that is possible by using VosViewer's term list extraction capabilities). I then created a thesaurus file in Excel SpreadSheets consisting of a total of 1622 extracted terms from the documents and used this to program the software to merge some terms in the final network map to ensure the graph’s clarity.  For example, I chose to merge terms like ‘Poe’ and ‘Edgar Allen Poe’, and ‘Alfred Lord Tennyson’ and ‘Tennyson’, as I did not want two nodes referring to the same term. 

 

 

 

 

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This process then provided me with a co-occurance map that showed the similarities between authors and their works. 

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     Another assignment for this course required me to take the first chapter of The Fellowship of the Ring in order to represent how frequent its characters are. Just as I extracted terms from my Web of Science results for the previous assignment, I input every term included in this first chapter into an Excel SpreadSheet, then created a thesaurus file to isolate character names (such as merging ‘Sam’ and ‘Samwise Gamgee’ to ensure these two different terms didn’t appear as two separate nodes when representing the same character) and merged words referring to certain characters (such as merging ‘old hobbit’ with Bilbo Baggins when it is used to refer to him). I also used this thesaurus file to ensure words that did not refer to characters did not appear in the final network graph (such as ‘house’, ‘hill’, or ‘Shire’). I also created a node and edge list for this data, as these components that note the frequency of character names, once uploaded to Gephi, help the social networking software to understand what to map. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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     This knowledge was used once again for my group’s final project – evaluating every single interaction between every single Harry Potter character from the first three books and movies. This was a massive undertaking that required me and my two other group members to collect huge amounts of data and input it into VosViewer.

     I chose to analyze the second book and the second movie, Harry Potter and the Chamber of Secrets, as this is one of the books I very often assist patrons to find or place a hold on at my library branch. I started by reading the book and simultaneously noting down every single interaction (i.e. conversation, look, physical touch, letter correspondence, etc.) between characters manually on hand-drawn matrixes. Once this process was finished, I began the same process while watching the Chamber of Secrets movie. The collection of this data not only took a matter of months to complete, but also resulted in filling an entire notebook to record every single interaction. I then input this data bit by bit into an Excel SpreadSheet, and used SpreadSheets to create some important files that needed to be uploaded to VosViewer in order to program the software to create the network map. While a thesaurus file was not needed for this assignment, as I recorded the data myself and therefore had full control over it (meaning there was no instance of a character being referred to twice by different names), I did have to produce large node and edge lists.  After the data was ready to be uploaded to the VosViewer software, our team was supplied with large network maps that show the frequency of interactions between characters and who had the most interactions (often, unsurprisingly, this was Harry Potter). 

     I later named this project 'A Network of Witchgrapht and Vizardry', a play on both Harry Potter's magical school of Witchcraft and Wizardry and the terms 'graph' and 'viz' (short for 'visualization') often used in class, and created a fun introduction to this presentation to help capture the attention of our audience. 

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