CODES123
Dr. Smith
March 26th 2024
I think that both articles made very valid points for each argument. While cleaning data may improve our research by the processes of identifying and correcting errors, inaccuracies, and inconsistencies in a data set to ensure the accuracy of set. In the article “data cleaning” Authors Rawson and Munoz argue that that data is inherently messy and complex, and attempting to “clean” them to fit into neat categories or formats can oversimplify or distort their meaning. And with that cleaning data does have some ethical concerns. There may be ethical concerns associated with data cleaning, such as the potential for bias to be introduced during the process or the erasure of marginalized voices and perspectives. We can see this firsthand in the Herbarium at the gardens, with the indigenous and African extractive practices that had taken place in those cultures by late botanist. Cleaning data may also involve removing or altering certain elements, which could result in the loss of important contextual information. With that being said cleaning data can also have its perks by making it less “messy”, it creates a cleaner space for the data environment. So, for that I think that we should embrace the messiness of data so then maybe we won’t keep digging ourselves into a hole that we can’t get out of in the future.