RADCLIFFE.HARVARD.EDU
Turning Terabytes of Academic Data Into Actionable Research Insights
The Challenge
Harvard Radcliffe Institute's fellowship programs generate vast amounts of research data across multiple disciplines — from gender studies to public policy to scientific research. Researchers were spending 60% of their time on data wrangling rather than analysis. The institute needed automated pipelines to extract data from 15+ heterogeneous sources ( surveys, databases, APIs, document collections ), transform it into analysis-ready formats, and generate standardized reports — all while maintaining strict IRB compliance for sensitive research data.
The Solution
We built a Python-based data engineering platform using Apache Airflow for workflow orchestration. Custom extraction adapters handle each data source type: REST APIs, SQL databases, Excel/CSV files, and even OCR for historical documents. The transformation layer uses pandas and dask for processing datasets that don't fit in memory, with automated data quality checks at each stage. A Jupyter-based analysis environment gives researchers interactive access to clean data, while automated report generation delivers weekly summaries in publication-ready formats. All data flows through encrypted channels with comprehensive audit logging for IRB compliance.
Data Pipeline
Automated ETL workflows
Analysis Tools
Statistical modeling & insights
Report Generation
Automated research reports
Data Security
IRB-compliant handling
Our Approach
Discovery & Data Audit
Catalogued 15 data sources, mapped researcher workflows, identified data quality issues, and designed the pipeline architecture with IRB compliance requirements.
Pipeline Development
Built Apache Airflow orchestration layer, developed custom extractors for each data source, implemented transformation pipelines with dask for large datasets.
Analysis & Reporting
Created Jupyter notebook templates for common analyses, built automated report generation system, implemented visualization library for research outputs.
Security & Deployment
End-to-end encryption implementation, audit logging for IRB compliance, researcher training sessions, and production deployment with monitoring.
Outcomes
Automated extraction and transformation of 2.3TB of research data from 15 sources
Reduced researcher data preparation time by 94% — from 60% of work hours to under 5%
Full IRB compliance with encrypted data flows and comprehensive audit logging
Weekly automated reports reduced manual reporting effort by 20 hours per week
Jupyter analysis environment adopted by 100% of active research fellows
Pipeline architecture supports addition of new data sources without code changes
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