13 Matching Annotations
  1. Jun 2019

      Just in general, I didn't have a lot of annotations here because a lot of this is very simple, straightforward design advice. It is presented very well, in casual but informative language, but most of these ideas were not new to me as someone who's studied statistics and art.


      Nice! I love the double-purpose of the legend. It's very clever and space-conscious.


      Ironically, what is being praised here would have had me losing marks in my statistics class. I guess it really depends on the subject, as well as the difference in scale. Because while 1 to 100 is very doable here, if you have data that can range from 1 to 10,000.... things get harder. I do appreciate this though. It is easier to compare.


      This marriage of data and art - and by extension, STEM fields and arts/humanities fields - is everything I want to see in academia. How much more engaging, and therefore effective, would academia be if people really presented their work? Would chemistry still put me to sleep? I don't think so.


      I had NO IDEA this was a media of art that could be explored! I don't feel like I'm nearly creative enough to come up with an idea, but I would love to do a project like this in the future!


      The best part of this graphic is the emphasis on men and women - someone untrained in statistics or graphs may have trouble seeing how "women live longer" because the lines do eventually merge - the emphasis shows where in the graph they should be looking to fully understand it.


      This is so incredibly cool and is such a god study into the human experience of feeling like you're the only one experiencing something, while it is statistically likely that someone else is going through the exact same thing. Unrelated, the colours they chose are great too. Very 90s.


      This ties in with last weeks annotations, where I talked about the inherent political nature of data/algorithms. This is the same idea, except I believe the author is going to put a more positive spin on things.


      Labelling axis works to contextualize the data being presented, but sometimes the axis is not clear. It makes it so difficult to read a graph when the axis is a long drawn out sentence or uses jargon to define. If it is made for the public to be reading it, it needs to be easily understandable. Just as the design needs clarity, so do the axis.


      This seems like one of the most important parts of data analysis. If people can not understand the way you have presented your data then all your hard work is worthless. This seems to especially ring true when it comes to more complex graphics. Even if it is pretty if it can not be read than it is not helpful or functional.


      Relationships in data also create a greater understanding of the data in general. By having the causation or the correlation element present with the rest of the data, it can help prove or explain why something has happened or what it effects. Without supporting data information, in some cases it can be very difficult to understand, Data sets with relationship provide a fuller understanding of the data being presented.


      It is so easy to forget that data statistics are all rooted in real life events. Whether its due to the separation in time from the event to us or a difference in a people group, it is often so difficult to grasp how real these stats are. However, this data represents human lives and experiences. For some people, certain data stats can have great meaning due to the direct impact on their lives, or the lives of those they love.

  2. Nov 2018
    1. If there is a recent date in the very title of the video, and that particular video has been uploaded to YouTube multiple times over a short span of time, then there is high probability that the video is a fake

      Remember to see how many times that video has been uploaded. Also check for the date in the title could also instigate that it is a fake.