Laborinformatik

Adopting DevOps practices in your laboratory offers numerous benefits. At its core, DevOps is about breaking down the barriers between traditionally siloed teams, development and operations. Among the benefits of this approach include increased trust, faster software releases, ability to solve critical issues quickly, and better management of unplanned work. Having a cross-functional team working simultaneously enhances collaboration and leads to more innovative outcomes. Moreover, implementing continuous integration/continuous delivery (CI/CD) pipelines can streamline processes, automate routine tasks, and significantly reduce manual errors. The result is a more reliable, efficient, and agile laboratory environment that can adapt swiftly to changes or new demands.

Absolutely. The flexibility of DevOps practices allows for the integration of a wide variety of software systems, proprietary or otherwise. The use of application programming interfaces (APIs), microservices, and containerization – such as with Docker or Kubernetes – can help to encapsulate and interface with your existing software systems. We would work closely with your team to understand your software and infrastructure, and devise a strategy for its seamless integration into a DevOps workflow.

Developing software for your laboratory is a multidimensional process that starts with understanding your unique needs, challenges, and objectives. This could involve enhancing data management, automating processes, or improving decision-making, among other goals. Once we grasp these needs, we apply our expertise in software engineering to design a solution that best fits your laboratory’s requirements. This involves creating a blueprint for the software, writing the code, testing the functionality and user experience, and then deploying the software. We also implement Agile practices like Scrum to encourage regular feedback, allowing us to make necessary adjustments and ensure the final product aligns with your needs.

AI and Machine Learning (ML) can greatly enhance the capabilities of laboratory informatics. These technologies can analyze vast datasets quickly and accurately, identifying patterns and trends that would be difficult to discern otherwise. For instance, predictive analytics can forecast experiment failures, helping to prevent costly lab work. Additionally, ML algorithms can optimize resource allocation by learning from historical data and identifying the most efficient use of resources. AI and ML can also play a role in decision support, providing data-driven insights that can inform strategic planning and other high-level decisions. By integrating AI and ML into laboratory informatics, labs can unlock a new level of efficiency and intelligence in their operations.

Adhering to Good Manufacturing Practice (GMP) regulations is crucial in any laboratory beyond R&D setting, and this extends also to software development. Even though GMP compliancy is an optional service we offer, our generic development practice ensures the software performs as intended in a consistent and reproducible manner. We utilize techniques like automated testing and rigorous documentation to verify and validate each aspect of the software. Moreover, our DevOps practices promote traceability and accountability, which are essential for GMP compliance. We adhere to a „quality by design“ principle, meaning quality and compliance are built into the software from the very beginning.

Yes, we recognize the importance of empowering your team with the knowledge and skills to effectively use the tools at their disposal. To this end, we offer comprehensive training for a variety of openly available software solutions and frameworks. Our training is designed to be interactive and hands-on, ensuring that participants can immediately put into practice what they’ve learned. We cover a range of topics, from the basics of data handling and analysis, to more advanced topics such as utilizing specific features of the software for data integration and visualization.