Conclusions (Thus Far...)

As the due date for my Honors Project is upcoming, I thought it appropriate to make a post that offers some thoughts or tentative conclusions on this project thus far. These include what I have learned about conducting digital history and using data visualizations, the opportunities and pitfalls present in such an approach, and what can be done in the future (in this project and others like it) to remedy limitations in approach and design.

I am hesitant to term these thoughts “conclusions” because the word suggests that the project has a definite end. Due to the exploratory nature of any investigative project, however, these projects only have an end insofar as the researcher stops asking questions of the data found. Additionally, the nature of the Web as ever-expanding lends an additional bulwark against this project ending. In some ways, it is easier to come back to the models on this website and the data underlying them to add on further findings or thoughts than it is to expand a long-form, traditional research paper that is bogged down by the need to maintain a continuity of style and writing for cohesiveness.

One dimension of this project that has surprised me is frankly the abundance of “low hanging fruit”, so to speak, within digital history. There are so many topics that, while receiving ample treatment within traditional historical analyses, have not been approached from a digital history perspective. This is partially due to the field being relatively new, but also because of the perceived limited audience of such efforts. If a historian wants to take the data they have gathered and create digital models based on such data, it could be perceived as too niche or not worth their time given that it might only serve a public historical function, rather than aiding their research. On the other side of the spectrum, public historians might be hesitant to use data-centric models due to a fear that viewers will not be drawn in.

What historians of all stripes must come to understand in the coming years is 1) the relative ease with which they can digitize and model their data with a variety of 3rd party tools and that 2) there is utility in these models that applies to both the historian as well as the public. Data visualization has the power to reveal what was previously unseen or perhaps overlooked in traditional approaches to historical analysis. It can be used to direct research towards answering a specific question, as a previous post illustrated; and if unable to fully answer such a question it at least giving evidence of one more tool used by the historian in conducting her due diligence. For public historians, data visualization models, when made compelling through design and clarity, can capture the viewer's attention more immediately and convey information more readily than other digital (or non-digital) communication mediums. Data visualization and digital history, as I have found, are an essential tool for the 21st century historian, another feather in her cap.

My categorizing of data visualization and digital history as a “tool” for historians reflects my belief that it is not a historian's panacea. Data-driven history cannot become disengaged from the historical narrative that lends relatable stories and human experiences to people’s understanding of history. In the case of the Coolie Trade, one has to do the dual legwork of presenting digital models within a larger context focusing on the historical phenomenon itself. In other historical periods that are better known, this might be unnecessary or less important to do, but for a period of history that few people know about, any attempt at digital history must bring with it a larger narrative, especially if aimed at a popular audience.

In terms of this project’s limitations or shortcomings, the accuracy of the models is necessarily limited by the accuracy of the data itself. This is the case for any kind of data visualization model, but is especially important to keep in mind when dealing with data sets pulling from 19th century records. For example, the models on the site excluded those ships that did not have information on the number of coolies being transported, and the data set more than likely is missing ships that transported coolies since much of the trade was illicit. If we had a perfect data set, the results of the models could be different. That is not to say that the conclusions being made from the models would be invalidated. More than likely the "big-picture" takeaway from the models would be maintained. Nonetheless, we must always remember that the conclusions drawn from the models must be used as a starting point for further investigation rather than being the last word.

In terms of my previous experiences conducting historical research throughout my undergraduate career, this project and the work required to bring it to fruition has been entirely new and refreshing. Thinking about conveying historical information in a visual and succinct manner, while tapping into skills used in writing good history, is something that students of history are seldom asked to do. The time-honored practice of writing lengthy research papers, while important and necessary in academia, seems to be over emphasized and beaten to death, much to the detriment of new mediums for conveying history like those used in this project. There is little doubt in my mind that other historians, whether they are in undergraduate or graduate programs, should be exposed to writing and conducting history within this new and innovative framework that the Web and data visualization tools have made possible. This framework holds great promise for students of any historical topic and helps to make history more relevant in the digital age.

As I mentioned at the beginning of this post, this is certainly not the end of this project, nor have we reached the limit of what data visualization can offer us in analyzing the Coolie Trade. Further exploration of the Coolie Trade could be done using the Cuba Commission Report, which lends itself well to data-driven analysis because of the statistics underlying the aggregated testimonies of Coolie laborers and the possibilities for finding commonalities or differences among the various testimonies that may not be evident without digital parsing to search for patterns. Since the document details the abuses suffered by the Coolie laborers, making the Cuba Commission Report digitally "parsable" would be a significant step forward in uniting traditional historical narrative and data-centric history and could offer new insights into the experiences of these laborers. It is my hope that this project will continue to bring attention to the history of the Coolie Trade through the use of data visualization.