01
JupyterHub or Posit Workbench
Select and configure a shared analysis workspace for R, notebooks, shell access, package management, and study project directories.
Small-team architecture
Enough structure to be controlled. Not so much platform that it becomes unaffordable.
Many small CROs and startups do not need a heavyweight enterprise platform on day one. They need a reliable shared environment for R, notebooks, validated project structure, version control, review, and AI-assisted development that can grow as studies become more complex.
ComputeJupyterHub or Posit Workbench
CodeGit repositories and branch review
RPackage lockfiles and reproducible runs
AIAssistant access with governance controls
Package components
What the setup includes.
02
Version-controlled study work
Git repository conventions, branch strategy, pull-request style review, issue tracking, and release tags for analysis milestones.
03
R-first clinical stack
R package baselines for data derivation, reporting, Quarto documentation, validation summaries, and reproducible output generation.
04
AI-assisted programming access
Codex, Copilot, Claude Code, or other assistant integration patterns with clear boundaries for source data, secrets, prompts, and generated artifacts.
05
Optional SAS alternative path
SAS access can be retained for legacy sponsor deliverables, independent validation, migration periods, or environments where SAS remains required.
06
SOP-ready operating model
Lightweight runbooks for project setup, package updates, code review, output QC, audit evidence, backup, and user onboarding.
Reference architecture
A lean SCE can still have real controls.
01
Identity
User groups, access roles, MFA expectations, and least-privilege project access.
02
Workspace
JupyterHub or Posit Workbench with R, notebooks, terminal, and controlled package libraries.
03
Versioning
Git repositories, protected branches, tags, review records, and change history.
04
AI layer
Assistant configuration, prompt guidance, audit notes, and no-secret/no-PHI guardrails.
05
Delivery
ADaM/TLG outputs, QC summaries, Quarto reports, and controlled release packages.
Recommended implementation phases
Start small, then add controls where study risk justifies them.
1
Assessment
Current R/SAS use, study pipeline, data locations, users, security requirements, and cost constraints.
2
Foundation
Workbench/JupyterHub, Git, project templates, package baseline, folder model, and onboarding notes.
3
AI connection
Assistant access, prompt/instruction files, review gates, safe-data boundaries, and generated-artifact conventions.
4
Pilot study
Apply the environment to one real ADaM, TLG, QC, or medical-review workflow and refine the runbook.
Need the benefits of an SCE without enterprise-platform cost?
Snowbird can help choose the right lightweight stack, configure version control, connect AI-assisted programming, and run the first clinical workflow pilot.
Plan a Lightweight SCE