An efficient laboratory assistant (embodied or digital) that supports good science is doing three crucial tasks exceptionally well: 1) tracking your monotonous functions like how long until something needs to be removed from the stir plate, 2) passive monitoring of stock and supplies to ensure no disruption will occur, and science can proceed on time, and 3) listening to your every word for insights and monitoring procedures for verification. [Read More]

While the costs associated with a LIMS implementation may initially appear daunting, the long-term benefits far outweigh the expenses. By addressing key economic considerations such as project management, vendor fees, infrastructure investments, configuration, testing, and training, labs can unlock significant efficiencies and economic advantages. A well-executed LIMS is a strategic investment that modernizes lab operations and positions the lab for long-term success. [Read More]

Sapio Sciences, the science-aware^TM lab informatics platform, today announced the launch of its new multimodal registration capabilities. This new functionality unifies small-molecule, large-molecule, and multimodal discovery workflows, including entity registration, on a single platform. With these latest features, Sapio is helping R&D teams overcome the collaboration and efficiency challenges traditionally associated with the development of multimodal therapies and products. [Read More]

This blog will dive into the essential reasons why you should use laboratory software, particularly Laboratory Information Management Systems (LIMS). Laboratories of all sizes are turning to digital solutions to streamline their operations, enhance efficiency, and ensure compliance with regulatory standards. [Read More]

Informatics systems like LIMS help streamline medical microbiology lab operations amidst evolving technological, microbiological, and economic landscapes. A LIMS is critical for managing complex workflows, preparing and tracking derivative specimens, interfacing with other systems, advanced result entry and reporting features, support for susceptibility testing and antibiograms, flexible public health reporting capabilities, and decision support systems. [Read More]

Artificial Intelligence (AI) has been continuously integrated into the field of clinical research. A formerly time-consuming workflow has now been shifted into an efficient process with lowered cost, less labor and improved clinical trial outcomes. As society shifts into a technology and digital driven era, it is important to see how this can be leveraged within clinical trials. We will take a look at 3 prominent ways that AI has been streamlining the clinical trial process since the shift into the digital age.  AI in clinical trials will continue to become a dominant theme among clinical technology and strategy professionals for the foreseeable future. [Read More]

We are thrilled to announce the launch of STARLIMS Advanced Analytics 2.0, our new Advanced Analytics platform that allows customers to make critical decisions faster with better data. With a more powerful analytics engine and new capabilities like natural language processing (NLP), Advanced Analytics 2.0 will help customers make better, data-driven business decisions that improve laboratory operations and allow their organizations to drive revenue faster. [Read More]

In this 2024 paper published in the journal Engineering in Life Sciences, Habich and Beutel review the potential technological transformation processes that await the academic bioprocess laboratory, moving from paper-based workflows to electronic ones. Noting multiple challenges facing these academic labs, the authors look at multiple laboratory informatics and automation technologies in the scope of the FAIR (findable, accessible, interoperable, and reusable) data principles, standardized communication protocols, digital twins, and more. After going into detail about these and other related technologies (including electronic laboratory notebooks and laboratory information management systems), the authors present digitalization strategies for academic bioprocess labs. They conclude that "when supported by digital assistants, using connected devices, more intuitive user interfaces, and data gathering according to FAIR data principles, academic research will become less time consuming and more effective and accurate, as well as less prone to (human) error." They add that experiment reproducibility, instrument integration, and other aspect also benefit, though no one-size-fits-all approach exists for these labs.