Initially, we were going to use Google’s spreadsheet because we could all edit it in one place, but we encountered a few problems. Some of the data in the Microsoft Excel spreadsheet when opened in the Google spreadsheet would overlap into other columns, making it hard to read. Additionally, there would be the occasion where data that was present in the Excel sheet was missing in Google’s spreadsheet. As another point, we all had the same version (2013) of Microsoft Excel pre-downloaded on our laptops which made Microsoft Excel compatibility easy. It was unanimously decided that we use Microsoft Excel to input data. However, we also decided to use Google Drive to save and share our data on a cloud. Google Drive also updated us via email anytime one of us contributed to our shared folder.
We created three folders in google docs to organize our saved spreadsheets and other files. These three were ‘1st DH Raw Data’, ‘2nd DH Raw Data’, and ‘DH Meeting Docs’. The third folder held our meeting minutes, or what our discussions were when we met and what goals we discussed to have done before we next met. Both the first and second raw data folders had sub folders of ‘checked’ and ‘unchecked’, where the previously naming convention came in handy. Additionally, both raw data set folders had their respective index card scanned copies were saved there. In doing this, we kept all files organized well and were able to share files efficiently. Although we all saved the most recent files to our desktops and to a shared USB drive for backup, Google Drive assured that our updated and previous files were in one place that we could all access from any computer.
All data we entered had to not only be divided evenly among the team, but it also needed to be checked to make sure that the information is correct and who had last saved the data. We agreed to use an author naming convention by using our initials. In the Microsoft Excel spreadsheets, we designated four additional columns for this purpose, and two more columns were added to communicate on the spreadsheet itself. Columns D,E, R, S, T, and U were used for the following: D was ‘Entered by CQ/MP/AB/EH’, E was ‘Date Entered’, R was ‘Comments’, S was ‘Checked by CQ/MP/AB/EH’, T was ‘Date Checked’, and U was ‘Additional Notes’. The ‘Comments’ column was used to communicate changes to data. Say I had entered a name wrong as ‘McThomas’, but Michelle caught the mistake, and would write in that row under column R ‘MP-AB fixed last name to MacThomas’. This tells us that Michelle is writing to Anastasia (me) that she fixed the error in the last name I made. If we had a question or were not sure of a data entry or part of one, we would write in column R as well. For example, if Ellie had a question about a missing name, she could write in the ‘Comments’ column ‘Which participant is this?’ or simply state ‘No name given’.
The initial convention was used not only to show who input and checked the data, but also who had last saved the data. To give an example, if I was the first to put in data, I would label the newly saved excel sheet ‘AB Raw Data Set 1’. If Ellie was the next to input her data and check mine, the new excel sheet would be titled and saved as ‘AB-EH Raw Data Set 1’. Then, if Michelle were to do the same, the file would be saved as ‘AB-EH-MP Raw Data Set 1’. This naming method would continue until all data is input and checked.
Overall, this system of using our initials to know who last saved, checked, and input data worked very well. It was a simple, clear way to know among the team who had last saved the most recent data and who was communicating with who within the spreadsheet, especially between meetings.
In the Fall 2015 Linguistic Anthropology class taught by Dr. Quizon, students were asked to share information about any and all languages that they knew. She gave out note cards and instructed the class to write down one language per card. Underneath the name of the language, they were asked to write down anything they wished to say about this language. They used descriptors of their own design making these cards rich with open-ended qualitative data. On the reverse of each card, they were asked to write their names.
With support from Seton Hall’s Digital Humanities Fellowship initiative, Dr. Quizon and three student interns who completed the course in the previous semester took a closer look at this data and explored ways to visualize the information. Were there intriguing or interactive ways to plot linguistic information? Could the data be mapped? Were there patterns to be discovered when expressed in visual form?
The class of 35 students was surveyed twice: once in the beginning of the semester, and again towards the end of the semester. The Language Maps, Language Clouds research team took these two sets of note cards, devised ways to capture, organize and analyze the information using linguistic concepts, explored ways to visualize the results of our queries, and aimed to share our findings online. Our goal is to share both processes and results as we seek to deepen our understanding of the data an interesting, interactive setting.
Even though we all participated in every aspect of the project, we each had an area of expertise. Ellie learned how to use and troubleshoot Viewshare and later, with Dr. Quizon, explored Tableau. She worked with Anastasia who was in charge of Excel and added knowledge of its features as needed for the project. I was in charge of learning how to build a blog on WordPress.