The client is a major Japanese organisation better known internationally for their retail camera products. This project will focus more on their smart IoT cell culturing and microscopic devices that connects to a dashboard where the data is transferred  through the network for further processing.

The project team and the client worked completely remotely, given the locations of everyone involved, as well as the pandemic forcing the work from home situation in both Australia and Japan. We used tools like Microsoft Teams, Slack, Figma, and Miro to communicate and collaborate online. Translations were done by the project lead and the translator, translating in real time on post it notes in Miro.

When scientists perform their experiments, they have several verticals that they work within to get to the stage of writing and publishing reports and research papers. That does not mean their work is linear, but these are the major themes within a project. Through discussions and workshops with the client, we collaboratively defined the opportunity areas that will be the focus of our project and will become the focus of our thinking and strategy when ideating on the future solution.

We worked agile on this project, dividing the project up into sprints or "loops" as they are referred to in japan. Each loop had a high level description of the activities. During the project at the end of every week, we would reiterate the plan of the project, where we were up to, what was completed, and workshopping any outputs that required validation or feedback. This allowed us to align with the client, collaborate with them and help them to achieve their business objectives and vision for the product.

As stated earlier, the client were looking to optimise their ecosystem consisting of cell incubators, microscopes, and the central dashboard that is the repository of the data collected by the devices. The dashboard required flexibility to receive the data and allow the users to manage the data so they can note down any observations or discoveries, then later write reports where supervisors can access the reports and notes for feedback and approval.

The current solution did not provide users this functionality in a usable way (feedback from user testing participants described the difficulty and frustration they experienced interacting with the current version of the dashboard). The graphic below expresses the early impressions we had when diving into the first loop (discovery).

During the design loops, we ideated concepts with hand drawn sketches to formalise our concepts and validated our wireframes with the client upon completion of the low, mid and high fidelity designs. The platform allowed the users to connect the devices to the dashboard, which would send data for the researchers to manage. They will then create a project based on the IA, and easily move images and data to the correct stage of the project, where they can favourite and share images, and begin collating their thoughts and observations within the notes panel. There is also a communication function where they can leave comments on photos, tag colleagues and share their notes with their supervisor for feedback and validation.

By digitally tracking experiments, comments, discussions and notes, it allows a laboratories knowledge base to have a digital presence rather than it being held within a senior researcher that could have barriers to accessing that person.

Along with the solution, the client required us to deliver a "rule book" which is a graphic of how the solution works in the ecosystem and with the users, along with a style guide of the solution. I defined the elements within the style guide and we presented them to the client which was easy for their design team to understand.