This project explores the historical Anatomy Registers from The University of Sydney Medical School, which document the sourcing of human bodies for anatomical study from 1883 to 1983. Through digital humanities methodologies, this research aims to uncover shifts in societal attitudes towards body donation, the evolution of medical practices, and the broader implications of these changes on contemporary medical ethics and practices.
The repository contains all the materials used in the analysis, including Jupyter Notebooks, data files, and source files for both the report and presentation. The project highlights the challenges and insights gained from working with historical, hand-transcribed datasets and demonstrates the application of geographic information system (GIS) techniques to visualise and analyse trends over time.
README.md: This file.presentation.pdf: Final presentation slides in PDF format.report.pdf: Comprehensive project report in PDF format.src/: Source files used in the analysis and documentation.analysis/: Jupyter Notebook files and associated data files for coding and analysis.presentation/: Source Beamer LaTeX files used to generate the project presentation.report/: Source LaTeX files used to generate the project report.
In the modern digital era, vast amounts of data are at our fingertips, yet significant historical data remain locked in non-digital formats. This project applies modern data science techniques to extract meaningful insights from the Anatomy Records at The University of Sydney, providing a deeper understanding of historical medical practices.
- Data Cleaning and Validation: Addressing the challenges of transcribing historical handwritten records.
- Feature Extraction: Techniques used to extract and categorise data from unstructured text.
- Geospatial Analysis: Using GIS to create choropleth maps to visualise the distribution and evolution of anatomy donors across New South Wales over a century.
- Historical Insights: Analysis of societal changes reflected in the patterns of body donations.
To replicate the analysis or to explore the data further:
- Clone the repository to your local machine.
- Ensure that you have the required Python environment; refer to
requirements.txtfor the necessary packages. - Explore the Jupyter Notebooks in the
src/analysis/directory to see the step-by-step analysis.
This project was undertaken for the SCDL3991 Science Dalyell Individual Research Project unit at The University of Sydney in Semester 1, 2024.
See below for contact details.
| Role | Name | |
|---|---|---|
| Supervisor | Dr. Rebekah Jenkin | [email protected] |
| Co-supervisor | Professor Kevin Keay | [email protected] |
| Student | Antriksh Dhand | [email protected] |