Age dataset: A structured general-purpose dataset on life, work, and death of 1.22 million distinguished people
Several fields of study can benefit from a large, structured, and accurate dataset of historical figures. Due to a lack of such a dataset, in this paper, we aim to use machine learning and text mining models to collect, predict, and cleanse online data with a focus on age and gender. We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project. The dataset is the largest on notable deceased people and includes individuals from a variety of social groups, including but not limited to 107k females, 124 non-binary people, and 90k researchers, who are spread across more than 300 contemporary or historical regions. The final product provides new insights into the demographics of mortality in relation to gender and profession in history. The technical method demonstrates the usability of the latest text mining approaches to accurately clean historical data and reduce the missing values.
- The dataset is presented in CSV format.
- Microsoft Excel can be used for browsing the data in tabular format.
- Python pandas library can be used for automated manipulation.