  Motivation: Visualization of data is a crucial step to understanding and
deriving hypotheses from clinical data. However, for clinicians, visualization
often comes with great effort due to the lack of technical knowledge about data
handling and visualization. The application offers an easy-to-use solution with
an intuitive design that enables various kinds of plotting functions. The aim
was to provide an intuitive solution with a low entrance barrier for clinical
users. Little to no onboarding is required before creating plots, while the
complexity of questions can grow up to specific corner cases. To allow for an
easy start and testing with SCAview, we incorporated a synthetic cohort dataset
based on real data of rare neurological movement disorders: the most common
autosomal-dominantly inherited spinocerebellar ataxias (SCAs) type 1, 2, 3, and
6 (SCA1, 2, 3 and 6). Methods: We created a Django-based backend application
that serves the data to a React-based frontend that uses Plotly for plotting. A
synthetic cohort was created to deploy a version of SCAview without violating
any data protection guidelines. Here, we added normal distributed noise to the
data and therefore prevent re-identification while keeping distributions and
general correlations. Results: This work presents SCAview, an user-friendly,
interactive web-based service that enables data visualization in a clickable
interface allowing intuitive graphical handling that aims to enable data
visualization in a clickable interface. The service is deployed and can be
tested with a synthetic cohort created based on a large, longitudinal dataset
from observational studies in the most common SCAs.
