Enable More Powerful Data Analysis

If your laboratory data is distributed across a large number of Excel and other files, it's very difficult and cumbersome to analyze data across experiments. Getting your data into a database dramatically improves your ability to analyze and visualize data, and is an absolute requirement to start using more advanced data analysis techniques, including artificial intelligence (AI), machine learning and various big data tools.

Improve Collaboration and Data Sharing


A database is a much better way to share data with your colleagues that individual lists that you exchange by email or a shared network drive.

And a workflow system can help you coordinate activities between scientists, lab technicians, department managers, and project managers – especially when the activities span different departments or maybe different organisations, incuding customers.

Boost Productivity in the Lab and when Processing Data

Using the experiment setup functionality and templates in Scifeon, you can speed up the planning process and support laboratory activities with better overview of the processes.

The processing of experimental results often involve time-consuming routine steps, including the import and rearrangement of data, basic statistical processing etc. that can be automated or greatly assisted by a custom IT tool, enabling you to spend your time on more valuable activities.

Enabling Laboratory Automation Efforts

Laboratory robots can greatly increase experimental throughput in the lab. But often, the outcome is that planning and data processing by scientists become a bottle-neck for efficient use of the automated equipment. Custom data management support enables you to fully leverage your automated lab equipment.

Better Data Quality and Traceability

When you are using Scifeon to plan experiments, support the laboratory execution, and process results, the system will help you track data files and link processed results to raw data, ensuring complete traceability from conclusions and hit selections back to experiments, raw data and specific instruments, sample batches, etc.

Consistent data quality is achieved by automating standardized operations, and full audit trail helps you troubleshoot irregularities; you can always get back to the raw data and track the operations and calculations that you have made.