Scientific data is information that’s produced through scientific experiments and research. In biopharma, this can be data related to compounds, devices, lab processes, instrument and equipment performance, and more.

Raw scientific data poses a significant challenge for life sciences organizations, because it is typically siloed, illiquid, proprietary, and subscale. TetraScience specializes in converting raw scientific data into Tetra Data that’s analytics and AI ready. This engineered scientific data helps optimize the most common workflows used by scientists.

The DMTA Cycle

The process of creating, running, and analyzing scientific experiments usually follows a standard Design-Make-Test-Analyze (DMTA) cycle:

• Design: Experiment designs are typically recorded in informatics applications like an Electronic Lab Notebook (ELN) or Laboratory Information Management System (LIMS). These designs are often created by analyzing the results from previous experiments or data provided by other teams.
• Make: Samples are created to test. This process can involve compound synthesis, cell growth, as well as protein and analyte purification, and more.
• Test: Scientists run the designed test. To do the testing, they either use their organization’s own equipment, in-silico methods to simulate the test computationally, or a contractor organization like a contract development and manufacturing organization (CDMO) or contract research organization (CRO).
• Analyze: Scientific data from each phase of the experiment is then analyzed alongside historic data, if there is any, to inform scientific and business decisions. This process can include the use of many tools, including analytics applications, data visualization tools, and artificial intelligence and machine learning (AI/ML) models.

For more information about how TetraScience helps life sciences organizations manage their scientific data, see Use Cases.