Data Visualizations
- Initial Thoughts
The topic I signed up for in this project is, “Ignored, Scorned, Vindicated: The maverick scientists whose heretical ideas were ultimately proven correct.” On an initial pass without any research a few things come to mind. Tesla’s work was certainly revolutionary but hotly contested by his peers due to competition. Most famously my mind goes to Edison VS Tesla on whether AC or DC were safer. This led to Edisons flashy murdering of an elephant to falsely prove that DC was safer which in reality it is not. While this is certainly a maverick type of behavior it is far from heretical. For that I would have to point to the many scientist crushed under the boot of Christianity regarding the sun being the center of the universe.
It is interesting to look at these scientists as they are laid out currently. It’s about 45 people and each of them have roughly 23 data points attached to them of various levels of relevance. So if I incorporated all of this then there’s about 1,035 points that would need to be addressed without any research done on my part. I’m quite certain many books have been written about most if not all of these people so I will be interested to see how to approach this work without getting too into the weeds of research.
- A note on bias in data
Thought it would be good to have a place where I can put some of my thoughts about the potential bias present in this data set. It is a relatively small dataset and I find it hard to believe this was without intention. I will make a note though that they do put a call out at the end to let them know if they missed any. Let’s take a moment here to note that not all aspects of science are covered here and it is unlikely this this group intended to gather what in reality is probably hundred of heretics together. It seems like they just wanted to create an intriguing graphic that could potentially aid in viewers seeing more of our planets rich history. For now though it should be noted that everyone working on this project is from Europe, four in UK and one in Italy. Two members are female and all of them are white.
- How would you characterize the steps in the narrative and key points you are meant to see?
This data set is less of a narrative and more of an index. Below the actual numbers is a writeup that shows some of the trends the designers found. One of the first focused on was that medicine and physical science produced the most heretics. Medicine was the field that brought about the most violent reactions whether it was beatings, exile or death. Its should be noted here that the narrative is quite playful, putting in funny little remarks here and there while talking about statistics. The largest cluster of ages for when they were labeled as heretical was 28–34. There is also a further break down of the way different rewards are given by category. This doesn’t give any particularly useful information but it is placed near the end to have a decent capstone and it’s pleasing to look at.
- How are you beginning to frame the data that you will include in your project?
It may be interesting to map out these heretics on a map and adjust visuals tho show which areas had the most heretics and when. It would also be interesting to spatialize the years they spent in exile in order to show which places were more obstinate and when.
- What is informing your decisions?
The most obvious trend I saw was the length of time before people’s ideas were recognized shortened as we continued on through time. The number of violent reactions to these theories also reduced. As an example every single woman in this dataset was after 1900 and all of them lived to see their ideas be accepted.
- What have you gathered from the readings and class activities to date?
From the readings I have seen that there are a number of ways to represent data but that it is very context specific. Contrasting red and blue in a gradient may work well in one context but it may not make sense in a more linear comparison. While there are multiple ways of showing data there are also multiple ways of organizing it that is also influenced by context. A map may work well in some contexts but it it does not make an indexical piece any less valid.
- What facets of your data are you considering using in your project and why?
For my data The most interesting comparison I can find so far is the amount of time scientists have being a heretic generally lessens as time goes on. It would be interesting to show that in comparison to the time they die and if they get to see themselves proven right. There is also a number of people who die right before their work is accepted.
- What design research question is guiding your project?
As time progresses, have scientists spent less years being treated as pariahs and have they also been punished less harshly? Is there a reason that some scientists die right about when their ideas are accepted? Did their death send out ripples that caused people to reexamine their work? Is there any correlation between the age of the heretics and how long they were pariahs? Why are so many of the pariahs from before 1900 medical?
- What organization methods do you imagine leveraging in the data (LATCH)? What coordinate system(s) do you see emerging as logical and appropriate?
Many of these methods have real potential but I don’t know how edifying it would be without a more solid narrative. A timeline that also depicts hierarchy could be nice. Just to line up these scientists across history, show how many years they were a pariah and if they got to see themselves proven right. It may also be fun to show them on a map but given how small and biased this data is it wouldn’t really teach anybody anything.
- What may serve as a logical sequence for people to move through the content (narrative/indexical/combo)?
My current dataset is indexical but there isn’t too much you can glean from it apart from the small data points they found and many of those aren’t valid since the data set is so narrow. The most interesting part of it was the story they wrote at the end to guide the viewer through it.
- What have you gathered from the readings and class activities?
The readings highlighted a number of ways to depict data and the importance of literal vs abstract in the depiction. Laying out my thought process/narrative on Miro was helpful for solidifying what I am trying to do and if it will flow naturally.
- What organization systems do you propose employing for each type of data and why?
A geographic coordinate system with length to show where these scientists traveled from birth to where they were when they were labeled a pariah would definitely be interesting. It doesn’t really say anything however so it may need to just be scrapped. A cartesian showing of length such as a bar chart could be good to show the years before being vindicated. This could also be accomplished by area instead of length, maybe the scientists get bigger the longer it takes? Changing of size to something larger usual implies superiority though so it may not be appropriate here. 1670 years before everyone became a heretic.
- What levels of scale and ranges do you plan to use and why?
For ranges I may only focus on the scientists that are relatively geographically close. If I try to make a global map there is one scientist randomly off in Australia that would make it look quite strange. It may be best to focus on the United States and Europe as they have the largest clusters of data points. Time is the scale that is most appropriate for this subject matter but if at the end it feels a little light I may point to categorical scales as well to either speak about the role of their subject matter or to further point to the lack of diversity in STEM.
- How do your decisions inform your visual exploration of representation approaches?
By focusing in on Europe and the U.S. I can play with hue or saturation to show which countries and states had the most heretics in them. Displaying time is tricky but not if you show it in averages. I may take the just show it approach for the individual years with a bar chart or even a polar bar chart but then do something like a piechart for the averages. Or perhaps I could show each year as an angry scientist?
- What are you learning from building your prototype?
From building my prototype I’ve learned a few things. the mapping of numbers to geometry is actually visually underwhelming. I had to scale everything up by 5.68 in order to get within a proper visual range and it could still potentially go up further. building all of these with components to quickly test out different visuals and color palattes. Area may still not be a good enough tool to properly show time.
- What are your key takeaways from the second project?
My biggest takeaways from this project is a knowledge of the language used for organizing data visually. I also grew to love this process and the idea of creating unique infographics. That being said my prototype is far from perfect and could still go through a number of edits to go further.
- How might you apply what you learned in the future?
I think there will always be a market for this type of work inside of a work setting. Being able to display data visually in order to make arguments is a valuable skill.
