Project Showcase

Experimental Design Platform for Behavioral Science

Automated experimentation tooling supporting rapid hypothesis testing and peer review.

Key Technologies

TypeScript Front-end analytics dashboards and experimentation workflows.
R Statistical engines for sequential testing and Bayesian analysis.
PostgreSQL Versioned experiment metadata and audit trails.

GitHub Repository

DiogoRibeiro7/experimental-design-platform

Infrastructure-as-code, APIs, and statistical services powering the lab platform.

Stars: —
Forks: —
Open Issues: —

Live Demo & Visualizations

Problem Statement

Research teams needed a compliant, auditable system to run concurrent behavioral experiments while sharing progress with peer reviewers and external collaborators.

Solution Approach

Implemented experiment blueprints, randomization engines, and sequential monitoring.
Integrated JupyterHub and RStudio Server for reproducible analysis with shared datasets.
Shipped automated report generation with APA-style summaries and data availability statements.

Results & Findings

Platform adoption cut experiment setup time from four weeks to five days and improved audit outcomes thanks to comprehensive provenance tracking.

Setup time reduction

75%

Active research groups

Peer review approval rate

96%

Challenges & Lessons Learned

Balancing audit requirements with researcher autonomy necessitated granular access controls and automated consent verification pipelines.

Datasets & Sources

Secure object storage for raw behavioral logs
Public derivatives published via Zenodo with DOIs per study

Model Performance

Sequential Bayes factors stabilized within 1.5% of ground-truth simulations across 10,000 bootstrapped trials.

Future Work & Improvements

Add adaptive experimentation modules leveraging contextual bandits and integrate ORCID-based single sign-on for external collaborators.

Collaboration & Contributions

External labs can request sandbox access through the collaboration form.

Statistical Analysis Blueprint for Experimental Design
Companion article describing the statistical methodology that powers the platform.

BibTeX

@misc{ experimental-design-lab-platform,
  title = { Experimental Design Platform for Behavioral Science },
  author = { Diogo Ribeiro },
  year = { 2026 },
  publisher = { DataLog | Data Science \& Research Theme },
  url = { https://diogoribeiro7.github.io/analytics-blog-jekyll/portfolio/experimental-design-lab-platform/ }
}

RIS

TY  - COMP
TI  - Experimental Design Platform for Behavioral Science
AU  - Diogo Ribeiro
PY  - 2026
PB  - DataLog | Data Science & Research Theme
UR  - https://diogoribeiro7.github.io/analytics-blog-jekyll/portfolio/experimental-design-lab-platform/
ER  -

EndNote

%0 Computer Program
%T Experimental Design Platform for Behavioral Science
%A Diogo Ribeiro
%D 2026
%I DataLog | Data Science & Research Theme
%U https://diogoribeiro7.github.io/analytics-blog-jekyll/portfolio/experimental-design-lab-platform/

Open science & reproducibility badges

These badges highlight the transparency practices applied to this work. Hover or focus on each badge to learn more about the criteria.

Open Data Dataset and code repository published with permissive license. Public repository, DOI issued, README with reproduction steps.
Reproducible Workflow Containerized environment and automated tests provided. Continuous integration pipeline with reproducibility checks.
Transparent Peer Review Peer review reports archived with DOI and linked to article. Open peer review statement and archived reports on Zenodo.