Raw Synthetic Real-World Data

Real World Data from a simulator for 1,000 people belonging to any of four behavioural groups: athletic, normal, unfit and feeble, simulated over 2 years and 3 months.

The dataset is organised into 4 CSV files:

aggregations.csv contains the daily physiological data, including their short- & long-term averages and their trends. Every row corresponds to a day for a person.

answers.csv contains the answers to questions. Each row corresponds to a question answered.

activity_summaries.csv contains summaries of the detected activities. Each row corresponds to an activity someone performed.

participants_features.csv contains the demographic info of each simulated person. This is static info, every row corresponding to one person. An ML engineer can combine information from any of these files to setup model learning datasets. This is done in the “Processed Synthetic RWD for binary modelling” and the “Processed Synthetic RWD for tristate modelling” datasets that are also shared here.