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Eppendorf SE and DataHow AG announce strategic collaboration to advance bioprocess data management

An integrated AI and cloud-based platform supports bioprocess development with enhanced analytical capabilities.
| 3 min read

Original story from Eppendorf.

Hamburg/Juelich, December 2024 

Eppendorf SE, a leading life science company, and DataHow AG, a pioneer in advanced data analytics and AI-driven prediction software, are excited to announce a strategic collaboration aimed at transforming bioprocess development. This partnership will integrate DataHow's innovative AI-enabled analytics solution, DataHowLab, with Eppendorf's cloud-based monitoring and analytics platform, BioNsight® cloud, providing scientists with unparalleled insight and analytics capabilities. 

Enhancing bioprocess development efficiency and collaboration 

In the highly specialized field of bioprocess development, the ability to design and optimize complex processes that meet stringent quality standards with maximum efficiency is crucial. Process data is a critical asset in this endeavor, yet leveraging it effectively remains a significant analytical challenge for process scientists. 

The integration of DataHowLab software within BioNsight cloud addresses this need by offering a streamlined journey from data generation to advanced analytics and insight, enabling researchers to make informed decisions quickly and efficiently. 

Data harmony around bioreactors is essential for enabling a robust bioprocess ecosystem. Bioprocess engineering demands seamless convergence between research and development as well as scalable production. This collaboration addresses the intricate requirements for digital services in this context. 

Seamless data integration and accessibility 

"Integration and collaboration are sometimes overlooked but are crucial for the success of bioprocess projects. Our solutions enable seamless data sharing and contextualization across multiple devices and locations, ensuring that scientists can make meaningful comparisons and generate valuable insights.", says Lina Tao, Senior Vice President Eppendorf Bioprocess. 

“The combined platform will enable scientists to access and model data from multiple devices—whether it's a PC, laptop, or mobile—across labs, industries, and even countries”, says Dr. Alessandro Butté, Chief Executive Officer at DataHow. “This level of accessibility allows for easy and meaningful comparisons, generating insights across different scales and various runs.” 

Automating data preparation

Manual data cleaning and formatting are often tedious and error-prone tasks that can significantly impede the research process. By automating these processes, the integrated platform saves valuable time and reduces the risk of errors, allowing researchers to focus on value-add analysis and in generating critical insights for decision-making. This is where cloud technology truly excels, providing easy access to clean, formatted data that is ready for analysis. 

Unlocking the power of predictive modeling 

With clean and well-organized data, DataHowLab democratizes access to advanced AI-enabled analytics, enabling non-data science experts to perform complex analyses with guided workflows. 

Scientists can use the software to perform advanced process data analytics, run in silico simulations to generate insight outside of the wet-lab, or for AIenabled optimal experimental design. With faster process understanding, R&D can operate more efficiently by reducing experimental effort and accelerating project timelines. 

Fostering collaboration and integration

The collaboration between Eppendorf and DataHow sets a new standard for data integration and collaboration in the bioprocessing field. By enabling seamless data sharing and contextualization between BioNsight cloud and DataHowLab. This is crucial for advancing our understanding of complex biological processes and ultimately, for the success of research projects. 

Find more information about Eppendorf bioprocess: www.eppendorf.com/bioprocess


This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release republishing policy can be accessed here.

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