Data Exploration & Analysis Portal (DEAP)

The Data Exploration & Analysis Portal (DEAP) is designed to lower the barriers to scientific research with NBDC datasets by promoting scientific best practices and fostering collaboration, sharing, and reproducible science. 

DEAP allows everyone to explore the data dictionaries of NBDC datasets (ABCD & HBCD), including variable-level descriptive statistics. DEAP is also integrated with the study data documentation to allow users to fast-track their dataset knowledge. 

Users with a valid data use certification (DUC) can:

• Download custom or pre-assembled datasets;
• Generate plots and charts to explore the data in the platform;
• View data in a built-in tabular data viewer;
• Analyze data (imaging and non-imaging) using inferential statistics (including voxel-/vertex-/connectome-wise) and visualize results directly within the application;
• Share, save, organize, and lock all objects (datasets, explorations, analyses).

All features listed are available for both the ABCD & HBCD studies.

Available without a data use certification:

• Data dictionary & study ontology explorer: Variable details, aggregate statistics, study structure.

Available with a data use certification (login to use):

• Custom tabulated dataset creation; Pre-assembled dataset access;
• Download of custom or pre-assembled datasets (CSV/TSV, Stata, SPSS, RDS, and Parquet/Arrow formats);

• Generate and customize plots, charts, and tables using study data (Bar, scatter, correlation tables, summary tables, and more);
• View data in a tabulated format with a customizable and filterable table;

• Use linear mixed-effects models (LME) to analyze vertex-wise, voxel-wise, connectome-wide, region-of-interest (ROI) imaging and non-imaging data;
• Visualize brain imaging surface and volumetric results as well as tabulated coefficient plots;

• Label, group, and archive objects (datasets, visualizations, and analyses) for a clean working environment;
• Save and lock objects for reproducibility;
• Share objects with collaborators and reviewers;

Upcoming features: 

• Upload transformed data or use a built-in interface to utilize custom variables;
• Analyze ABCD/HBCD data in a secure research environment using R or Python.