Introduction

Ada is a performant and highly configurable system for secured integration, visualization, and collaborative analysis of heterogeneous data sets, primarily targeting clinical and experimental sources.
The name Ada refers to Ada Lovelace, a British mathematician, and a coinventor of the Analytical Engine, the first model of a computer (together with Charles Babbage).

Content

Statistics

Ada allows users to conveniently explore and filter data, and calculate various statistics with embedded interactive visualizations, for instance, categorical and numerical distributions, scatters, correlations, and box plots.

Moreover, one of Ada's main functionalities is to produce dynamic and personalized views containing filters, statistical widgets, and tables, which can be saved and shared among the users. This makes Ada an ideal tool for collaborative reporting.

Data

Ada supports diverse data sources from clinical, research, and experimental origins. It is designed to handle heterogeneous data sets of varying sizes and structures, making it suitable for a wide range of data exploration and analysis tasks.

Ada facilitates robust access control through LDAP/OIDC authentication, and in-house user management with fine-grained permissions. With a convenient user management UI, admins can simply specify which data sets a user is allowed to access and which actions on the data set he/she is allowed to perform.

Note
Although Ada has been primarily designed for Biomedicine, there is nothing preventing it from being used for other domains. For instance, we successfully imported and analyzed data sets for wine types, car acceptability, mushroom traits, and poker card combinations.

Metadata

To define data set’s metadata Ada provides an editable dictionary, and a categorical tree with drag-and-drop manipulation. Ada supports many data field types including number, date, boolean, enumeration, and json. These (collectively called dictionary) could be automatically inferred during an import. Further, each field type can be either scalar or array, which makes Ada's type system flexible enough to cover a wide range of data origins and flavors.

Import/Export

The data set import adapters currently support three file formats: CSV, JSON, and tranSMART data and mapping files, and two secured RESTful APIs: REDCap and Synapse.

Any data sets provided from these sources can be added to (or removed from) Ada on-the-fly as well as scheduled for periodic execution. As such, Ada has potential to serve many translational medicine or any data exploration endeavors. For post-processing, filtered data can be exported into CSV, JSON, or tranSMART format.

Machine Learning

For more advanced analysis, well-grounded machine learning and statistical approaches were integrated using Spark ML library. This covers a wide variety of classification, regression, clusterization, feature selection, normalization, and time-series processing routines.

Ada is available for registered users only. If you wish to use Ada request an account.