publish to the CUNY Academic Commons DUE 12midnight Wednesday, June 26
The white paper details what you did in your final project, why you made the choices you made, and how those choices affected the end result. The format is a hybrid of a methods section and discussion section of an academic article. When someone reads your white paper, they should not only have a very clear picture of what you did and why you did it; they should be able to replicate your steps and come up with a similar result.
You will, of course, include a summary of what you covered in the blog post: the research question, context, data, and design decisions. Anything that you did that was a compromise, and why you made that choice should be detailed here as well.
However, as opposed to the blog post, you will justify your choices with references to readings, other visualizations, and other practitioners who have inspired your project. (i.e., "inspired by Tufte’s use of small multiples (1990), I applied that strategy here" or "similar to how Posner describes census data, I tried to…(2016)". In this way, the white paper is an opportunity for you to engage in the discussion more broadly and identify how your work fits within the larger ecosystem of data visualization.
The white paper is also a chance to reflect on the iteration process, and how the pinups and critiques helped shape your project. If there were any turning points or revelations along the way, this is where they should be discussed. Particularly in terms of how you could have taken one path, but took another instead, and what you think was gained and lost in those decisions.
Finally, this is an opportunity to educate your audience. Assume your reader is an educated novice. They probably know what a scatter plot or a bar chart is, but may not know what a tree plot is, or why you would use one. Explain any terms that could be unfamiliar.
Ultimately, the white paper serves as a reflection on the visualizations, the data, and the process of creating. These three pillars come together in almost every project, but are readily apparent in data visualization, where design and iteration are essential components of the work.
The final paper should be 1,500 to 4,000 words (more than 4,000 is too long)