QuasiScience helped a leading advanced materials company transform how it manages and uses its testing data. Drawing on our experience building data systems for Formula One teams, we developed a unified materials data platform that centralised experimental results, automated data capture, and enabled more effective analysis and collaboration. This accelerated R&D, improved material selection, and preserved valuable institutional knowledge.

Effective data management can be the key to improving efficiency in materials research and opening up its added value.

— Dr Ben Thomas, Department of Materials Science & Engineering, University of Sheffield

Materials Testing

Industries from aerospace to automotive run hundreds of materials testing experiments every year. For any business involved in producing physical products, it’s crucial to understand how different materials, or combinations of materials, surface treatments and adhesives perform under varying conditions.

However, many organisations are not making the most of this data. Results may be stored in disjointed spreadsheets, lab notebooks, or historic databases. This fragmented approach is far from the best foundation for research and decision-making.

Our client, a leading advanced materials company, wanted to unlock the power of its data by improving the way it collected, organised and analysed the results of experiments, past and present.

From F1 to the Factory

Drawing on our team’s experience building high-performance data systems for Formula One teams, QuasiScience designed and implemented a unified data platform tailored to the client’s needs.

When we worked in F1, we needed to track variables such as composite layering, surface coatings, adhesive types, and mechanical test outcomes, to select the optimal combination for each component, balancing strength, weight, and durability. We built a similar system, featuring:

• Centralised Data Collection: Automated data capture from multiple lab instruments and testing set-ups.
• Smart Organisation and visualisation: to make experiments easily searchable and comparable.
• Custom analytical tools: Built-in visualisation tools to analyse trends, correlations, and trade-offs using custom metrics.
• Collaboration Features: Live, secure, role-based access for engineers, researchers, and management.

Information management in R&D

QuasiScience’s new data platform transformed the client’s R&D process. For the first time, engineers and scientists could view all test results in one place, compare outcomes across experiments, and quickly identify promising combinations. This meant:

• Faster innovation: Engineers reduced the time spent searching for past data and setting up redundant experiments.
• Improved decision-making: data-driven selection of materials based on all the evidence available.
• Knowledge retention: Institutional knowledge from years of testing became easily accessible, reducing reliance on individual experts
• Enhanced collaboration: Multiple teams across different sites could access consistent, validated data, accelerating joint research.

The Good Data Advantage

Data infrastructure may sound like a dry topic, but without good databases and working data pipelines there is no methodology that could yield a good outcome. What is the use of advanced simulations, machine learning models or digital twins if the underlying data is poor quality, incomplete or inaccessible?

Good infrastructure makes sure data is clean (free of structural issues like typos, duplicates and missing entries); accurate; legally usable; secure and accessible, and opens up the opportunity for companies to use all the latest data science and research techniques.