Lineage Graph

Code Ocean

The Product

Code Ocean empowers computational scientists to run, reproduce, and share analyses. As projects scaled, so did complexity: dozens of Capsules and datasets interacted invisibly, so teams struggled to trace dependencies, debug failures, or reproduce results confidently.

The Lineage Graph goal is to bring clarity and trust by mapping every relationship between code, data, results, and pipelines in real time.

The Process

The Challenge

No Role Divercity

No Clear Path

Code only view

High Effort

“I just want to see what feeds what, without reading five scripts.”

— Data Scientist, Bioinformatics Team

The Research

Competitive Benchmarking

User Interviews

Heuristic Analysis

Findings

Cognitive overload: Users abandoned DAGs (Directed Acyclic Graphs) once graphs exceeded ~50 nodes.
Traceability gaps: Users couldn’t answer “what created this file quickly?” without investigating many scripts.
Role diversity: Scientists needed readability, not raw DAG syntax.
Tool gaps:
- Airflow → powerful but intimidating for non-engineers.
- Neo4j Bloom → friendly but too generic for code/data context.
- Alation → excellent progressive expansion, limited workflow awareness.

Takeaways & Goals

Address complexity through progressive discolsure

Visuals- first

Quick Positioning detection

Canvas controls as a must

The Design

Principles

Architecture

Graph canvas (core view).
Layer controls.
Filters
“Details Panel” (on demand).
Breadcrumbs highlights on hover for orientation.

Solution Overview

Visual Vocabulary
Calm Overview + Expandable Depth

Role -Friendly
Real-Time Synchronization

Validation & Outcomes

Results from Usability Sessions

Tasks: trace upstream sources, verify reproducibility, assess impact of change.
Outcome:
- 90% task completion success.
- Average trace time reduced from ~6min → 2min.
- “Feels calm and obvious” repeated across testers.

“This finally matches how I think, start small, follow the thread, open detail when I need it.”

— Computational Scientist, internal pilot

Reflection

Designing for technical users reaffirmed that:

People want clarity, not complexity. Even engineers appreciate a “calm default.”
Progressive disclosure builds trust. A predictable reveal rhythm reduces stress.
Reproducibility is emotional. Seeing lineage instantly builds confidence in results.

Collaboration with engineering was crucial: graph performance required virtualized rendering and dynamic clustering, ensuring UX fluidity even with 1,000+ nodes.

The Lineage Graph transformed hidden complexity into navigable clarity.

It unified scientists and engineers under a single visual truth, reduced cognitive friction, and redefined reproducibility as an interactive experience.