The Nobel Prize Winners dataset illuminates global patterns of different achievements and cultural changes. This dataset allows us to explore how representation in economics, literature, chemistry, peace, medicine, and physics has evolved over time. It reveals demographic trends, like gender gap in Nobel awards, geographic shifts in where the winners come from, and how major world events (such as wars/political movements can correlate with certain types of awards). Beyond individual analysis, the dataset also shows how collaboration and global mobility influence success.
The exact process of data “generation” and what techniques were used to gather it is not documented in the data summary. The data for this dataset was collected in October 2016 and includes information from Nobel Prize winners dating back to the first Prizes awarded in 1901. The data is courtesy of the Nobel Foundation and their hosting of open source data on laureates via a public API. The Nobel Foundation was founded in 1900 and has kept records of laureates, their awards, and achievements. The specific bodies that decide on winners are: the Royal Swedish Academy of Sciences (for Chemistry; Physics; Economics prizes), the Nobel Assembly at the Karolinska Institute (for the Physiology / Medicine award), the Swedish Academy (for the Literature Prize) and the Norwegian Nobel Committee (for the Peace Prize).
Our dataset about Nobel Prize winners was compiled nine years ago and the dataset is hosted on data.world, and is publicly available. Sources of data include Nobel Prize API and their respective SPARQL Endpoint website. The original sources come from the Nobel Prize organization and were gathered using the Nobel Prize API and their SPARQL Endpoint. Furthermore, the data was created by Selene Arrazolo, a Data Analyst from Texas, who was interested in getting a better understanding of where these exceptional scientists and leaders came from as well as what their motivations were which led to them receiving a Nobel Prize. Ultimately, the dataset was established utilizing the Nobel Prize organization’s database and has specific information about the Nobel Prize Laureates from the award’s inception in 1901 through around October of 2016.
The by_winner dataset contains names, birth/death, gender, born country and city, deceased county and city, gender, year, university, and motivation. Additionally, the by_data dataset contains the year, name, category, and motivation. The dataset leaves out context and details as it doesn’t show how or why each winner was chosen. It lacks information about the factors that influence the selections. The data shows who received the price, their background, and when they received the price but not the reasons, process, or bigger significance. For our team to gain a more complete understanding, we would have to pair these datasets with additional sources (e.g. historical documents).
The categorization of this dataset into categories like country, gender, and university details a specific worldview regarding what constitutes significant information. It reflects the prioritization of measurable facts over cultural context, suggesting that success can be comprehended through data alone. By just focusing on Nobel Prize winners, the dataset overlooks narratives about inequality and opportunity that influence who receives recognition. It establishes a Western notion of merit, where achievement is perceived as individual rather than connected to social or historical factors. If this dataset were our only source, we would miss major human and cultural factors that drive each winner’s motivation and success. Furthermore, there are several missing data (“no data”) in the dataset, which not only limits the accuracy of analysis, but also underscores the selective nature of what is being represented. If this dataset were our only source, we would miss major human and cultural factors that drive each winner’s motivation and success.