Automating a wet lab requires the ability to create and translate complex lab procedures into machine protocols, and to intuively capture the realtime state of the lab. Working together with the engineering team, I experimented with numerous workflows that would provide scientists with clear and immediate access to data, outstanding lab actions, protocol building tools, and scheduling conflicts.
I interviewed stakeholders to create user personas and user journey maps to establish an information hierarchy. How would students, junior and senior scientists, lab technicians, and supervisors interact with the scheduler differently? What are their priorities when accessing the portal? I wanted to ensure each user type was able to quickly find what they needed and be alerted of any manual input required by the lab.
User tasks included troubleshooting incidents, processing lab actions, onboarding lab equipment, filtering large (>10,000 item) datasets, accessing protocol histories, expanding well plates, and simulating protocol schedules before committing. These processes took advantage of visual representations when possible to reduce clutter while retaining the flexibility to make scalar queries. Repetitive tasks used a string naming system similar to ID3 tagging for bulk processing that allowed onboarding to be flexible but accurate.
The discovery and research phase produced various low-fi versions that guided the subsequent wireframes. These were then translated into hi-fi designs that were built into a Figma prototype for testing. Live feedback sessions were conducted to better understand heatmaps on individual modules and improve clarity of communication and accessibility.