TITLE: SurvSet: An open-source time-to-event dataset repository 

ABSTRACT: Time-to-event (T2E) analysis is a branch of statistics that models the
duration of time it takes for an event to occur. Such events can include
outcomes like death, unemployment, or product failure. Most modern machine
learning (ML) algorithms, like decision trees and kernel methods, are supported
for T2E modelling with data science software (python and R). To complement
these developments, SurvSet is the first open-source T2E dataset repository
designed for a rapid benchmarking of ML algorithms and statistical methods. The
data in SurvSet have been consistently formatted so that a single preprocessing
method will work for all datasets. SurvSet currently has 76 datasets which vary
in dimensionality, time dependency, and background (the majority of which come
from biomedicine). SurvSet is available on PyPI and can be installed with pip
install SurvSet. R users can download the data directly from the corresponding
git repository.
Time-to-event (T2E) analysis is a branch of statistics that models the
duration of time it takes for an event to occur. Such events can include
outcomes like death, unemployment, or product failure. Most modern machine
learning (ML) algorithms, like decision trees and kernel methods, are supported
for T2E modelling with data science software (python and R). To complement
these developments, SurvSet is the first open-source T2E dataset repository
designed for a rapid benchmarking of ML algorithms and statistical methods. The
data in SurvSet have been consistently formatted so that a single preprocessing
method will work for all datasets. SurvSet currently has 76 datasets which vary
in dimensionality, time dependency, and background (the majority of which come
from biomedicine). SurvSet is available on PyPI and can be installed with pip
install SurvSet. R users can download the data directly from the corresponding
git repository.
SurvSet: An open-source time-to-event dataset repository
http://arxiv.org/abs/2203.03094v1