![\"\"](\"http:\/\/blogs.ubc.ca\/jgock87\/files\/2025\/03\/Screenshot-2025-03-06-105754.png\")
<\/a><\/p>\nIn addition, based on the size of the nodes and frequency of edges, it appeared that among the most common song choices were:<\/p>\n
- \n
- Brandenburg Concerto<\/li>\n
- Johnny B Goode<\/li>\n
- Fifth Symphony<\/li>\n
- Crane\u2019s Nest<\/li>\n<\/ol>\n
![\"\"](\"http:\/\/blogs.ubc.ca\/jgock87\/files\/2025\/03\/Screenshot-2025-03-06-114112-300x121.png\")
<\/a><\/p>\nOn the other hand, some of the least common song choices were:<\/p>\n
- \n
- String Quartet No. 13<\/li>\n
- Gavotte en rondeaux<\/li>\n
- \u00a0Pygmy Girl Initiation Song<\/li>\n<\/ol>\n
![\"\"](\"http:\/\/blogs.ubc.ca\/jgock87\/files\/2025\/03\/Screenshot-2025-03-06-114230-300x112.png\")
<\/a><\/p>\nInterestingly, several songs were unrepresented* in the visualization, such as:<\/p>\n
- \n
- Bach\u2019s Glenn Gould<\/li>\n
- Georgia<\/li>\n
- Malanesian Panpipes<\/li>\n
- \u00a0Iziel je Delyo Hagdutin<\/li>\n
- Beethoven\u2019s 5th, part 1, Otto Klemperer<\/li>\n<\/ol>\n
*To find which ones were not included I actually had to go into the notepad version of the .json file, as I found trying to sift through the visualization to be too difficult (although there is probably an easier way that I didn\u2019t know about).<\/i><\/p>\n
![\"\"](\"http:\/\/blogs.ubc.ca\/jgock87\/files\/2025\/03\/Screenshot-2025-03-06-114317-300x157.png\")
<\/a><\/p>\nHowever, while these images visually represented the frequency and pattern of selections that were made, it made me wonder about what was left out. That is, what about the reasons behind the songs I didn\u2019t<\/i> choose, as well as the ones that my classmates did not select? And how might the weighted connections used in the graphs oversimplify our preferences? For example, the data being shown does not reveal my own rationale for choosing Cranes in Their Nest<\/i>, which centered on the emotions that it evoked and its cultural connections. It only reveals that by choosing it, I was more likely to also select Flowing Streams<\/em>.<\/p>\n
To further examine what information was left out, I went to visit the blog posts from others in the course, in an attempt to discover the reasons they gave for their song selections. Three examples of reasoning that are left out of the visualization can be seen in the quotations below:<\/p>\n
- \n
- \u201cI curated the list of musical content down to these ten picks in order to create a narrative for both the record and our human society. I tried to mix as many different cultures while keeping the narrative, but I worry that my own inclination with western media may have come through with the majority of selections being from the northern hemisphere.<\/i>\u201d (Navid Panah)<\/li>\n
- \u201cI wanted to choose a variety of songs to preserve the original intent of the golden record. Some major considerations I had when determining my opinion of \u201cvariety\u201d were: country of origin, length, genre, and if the song was instrumental or vocal. This list contains both short and long songs (in fact, it keeps both the shortest and longest track on the record!), represents each continent of the world, and has a mix of vocal and instrumental tracks. Overall, I also tried to include songs that differed from each other tonally, as I felt that would have the strongest impact<\/i>.\u201d (Tristan Wong)<\/li>\n
- \u201cFor instance, I chose Johnny B. Goode because it\u2019s meant purely for listening and entertainment while Azerbaijan bagpipes seems more ritualistic and cultural (though it\u2019s not a guarantee aliens would interpret that way). I also ignored lyrics, except to include tracks with vocals, because aliens would be unlikely to be able to interpret spoken words.<\/i>\u201d\u00a0 (Evan Daigle)<\/li>\n<\/ol>\n
After reading through these blog posts, I could see that it’s important to understand what these data networks represent: not just what\u2019s visible, but also what\u2019s missing. The gaps in data can shape narratives just as much as the information that is included. While I could find information from the visualizations about frequency and patterns of selections, I could not fully discern the meaning behind the choices (and thus the data) itself. Consequently, data visualizations can only tell part of the story; if we want to understand their real meanings we have to dig deeper and look beyond just the graphs and numbers.<\/p>\n
<\/p>\n
References<\/b><\/p>\n
Systems Innovation. (2015, April 19). Network connection<\/i>s<\/a>.[Video]. YouTube.<\/p>\n","protected":false},"excerpt":{"rendered":"
When I first examined the visualization of our song choices, I noticed how difficult it was to interpret and understand what I was looking at.…<\/p>\n
- \u201cI wanted to choose a variety of songs to preserve the original intent of the golden record. Some major considerations I had when determining my opinion of \u201cvariety\u201d were: country of origin, length, genre, and if the song was instrumental or vocal. This list contains both short and long songs (in fact, it keeps both the shortest and longest track on the record!), represents each continent of the world, and has a mix of vocal and instrumental tracks. Overall, I also tried to include songs that differed from each other tonally, as I felt that would have the strongest impact<\/i>.\u201d (Tristan Wong)<\/li>\n
- \u201cI curated the list of musical content down to these ten picks in order to create a narrative for both the record and our human society. I tried to mix as many different cultures while keeping the narrative, but I worry that my own inclination with western media may have come through with the majority of selections being from the northern hemisphere.<\/i>\u201d (Navid Panah)<\/li>\n