The discovery process can be adjusted to the particular needs of your research project.
AutoDiscovery provides pre-configured discovery programs to facilitate the task.
If a specific setup is required, a customized program and advanced configuration tools are also available to fine tune the discovery performance.
The discovery process consists in evaluating the relationships between every pair of variables and special combinations of them such as subintervals and ratios.
Depending on the nature of the data of each variable, a particular flexible statistical test is computed (Spearman's Rank correlations, one-way ANOVAs or Cramer's V contingencies) to assess until which points these variables may be associated.
This process is also performed in specific subsets of your data called "segments" (e.g. groups of patients or animals) and "subintervals" (e.g. patients older than ...).
The exclusiveness post-analysis determines the scientific relevance and also the statistical significance of the relationships evaluated, that is, its likelihood to become a confirmed novel finding or an exploratory result to be tested in a further confirmatory phase of the experiment.
The Discovery Map and Hypo Booster tools facilitate browsing the list of relationships detected between your data files and variables.
A exhaustive table of relationships arranged by their scientific relevance is provided.
The plot shows the particular samples of your data used to evaluate a relationship, which enables the traceability of the results.
Graphs, plots and tables can be easily exported to share!
The most of the experiments are designed to study a problem from a variety of approaches and identify novel relationships among them.
This requires capturing, storing and integrating information coming from different project teams, equipment and software packages.
AutoDiscovery saves you a lot of time every day by performing an automatic consolidation process which aggregates the rows and columns of the imported data files into a single data table.