Clear, efficient reporting is essential for lab success. By automating data capture, standardizing templates, integrating centralized systems, and applying role-based access, labs can streamline reporting, reduce errors, and stay compliant. These strategies boost accuracy, improve decision-making, and support better scientific and operational outcomes.
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Choosing between a LIMS and an ELN was once straightforward—LIMS managed samples, while ELNs supported experiments. But as platforms evolved and labs grew more complex, these distinctions blurred. Today, it’s less about labels and more about finding a system that fits your workflows and future needs. This article breaks down the strengths of each and how to make the right choice for your lab.
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Whether to solve LIMS design puzzles via configuration or customization is an age-old question, right behind “Which came first: The chicken or the egg?” Ok, maybe it’s not that universal, but it is one of the most asked questions within lab informatics. What is the difference between customization and configuration, where is the line between the two, and which approach is the correct one to use in solving your design problems?
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Labs implementing software systems to help them reach their business goals know they need requirements to keep them on track. However, beyond the six common types of requirements — business, user, functional, non-functional, configuration, and user stories — labs should also define clear user acceptance criteria.
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Digital transformation in labs has created a new challenge for quality control teams: how to organize the growing volume of daily data. Efficiently structuring lab data is no longer a competitive edge—it’s a necessity for faster responses, reduced rework, and better decision-making. This article explores best practices for structuring lab data to boost productivity, traceability, and quality management.
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Integrating your LIMS with other lab systems improves efficiency, accuracy, and collaboration. By connecting instruments, databases, and third-party tools, labs can streamline workflows, reduce manual entry, and maintain strong data integrity. This leads to faster turnaround times and better decision-making.
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For decades, LIMS have been the backbone of quality control, compliance, and traceability in industries like Water & Environment, Food & Beverage, Chemical, and Manufacturing. While on-premises systems offered control and customization, shifting demands and IT landscapes are prompting many to consider Software-as-a-Service (SaaS) alternatives. This blog helps lab and IT decision-makers evaluate the operational, compliance, and scientific benefits of SaaS LIMS.
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Lost or mislabeled samples are costing labs time, money, and credibility. This article exposes the hidden risks of poor labeling practices and reveals a simple yet powerful LIMS-driven strategy to eliminate errors, ensure full traceability, and protect your lab’s reputation from preventable disasters.
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AI and machine learning are rapidly reshaping R&D, especially in drug discovery, where they’re transforming how researchers identify and develop new treatments. By analyzing complex data, automating tasks, and uncovering new insights, these tools are driving efficiency and innovation across the industry. As the field evolves, new advancements continue to emerge. This article is part 4 of our ongoing series on AI’s impact in R&D.
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Scientific data platforms have emerged on the research scene in the last few years with a promise to transform laboratory science from a cluttered attic full of uncategorized boxes into a state-of-the-art, AI-powered research library. But what are they, exactly? Are they the Wizard of Oz, or the man behind the curtain? The truth is, they’re a little of both.
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Xybion Medical Device LIMS helps manufacturers ensure regulatory compliance and product integrity by automating quality control, maintaining traceability, and streamlining documentation to meet FDA, ISO, and cGxP standards throughout the medical device lifecycle.
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Before implementing new software, labs should document what the software must do. These “requirements” guide the system to meet the lab’s needs, but not all requirements are the same. Different types serve different purposes throughout the software lifecycle. Whether you're deploying a LIMS, LIS, ELN, CDS, or other system, it's important to understand the various types of requirements and how they’re used.
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In this episode, we explore why tech projects often fail: it's rarely technical, but because of people. Engaging your team early and understanding lab science can make projects six times more likely to succeed. We address common challenges like training gaps and building trust in new tools, including AI. The key takeaway: digital transformation is a people project, not just a software one, with practical tips for getting real value from your lab’s tech investments.
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Implementing a LIMS does not have to be overwhelming. This guide shares essential tips to plan, execute, and succeed, from setting clear goals to ensuring user adoption. Learn how to avoid costly mistakes and unlock the full potential of your lab’s data with a smooth and strategic LIMS rollout.
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Tech giants like Netflix, Amazon, and Tesla have completely reimagined how systems operate. Now, laboratories have the opportunity to follow their lead. Learn how personalization, real-time visibility, and smart automation can reshape how your lab works, grows, and keeps up in a fast-changing environment.
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In regulated lab environments, maintaining data integrity is key to compliance, reliability, and operational success. While most off-the-shelf lab informatics systems offer strong data integrity controls, challenges arise when labs connect instruments and equipment using file transfers. This common practice can compromise data accuracy and consistency, potentially leading to compliance issues.
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This episode demystifies AI's role in the lab, breaking down complex concepts for beginners. We explore real-world applications like intelligent alerts and smarter reporting, while addressing myths such as AI being “plug and play” or replacing humans with AI. Learn why data quality and human oversight are key to successful AI implementation, ensuring efficiency, accuracy, and compliance. Tune in to see how AI is transforming lab operations—and how your organization can embrace it to work smarter and stay competitive.
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Integrations between LIMS, ELN, CDS, and instruments are often ad hoc, leading to isolated data, delays, and limited system value. Manual entry and poor connectivity hinder FAIR data practices and analysis. This webinar explores a better approach to integration, data management, and visualization.
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Human error is one of the biggest threats to lab accuracy, but it’s also one of the easiest to reduce with the right systems in place. From automating data entry to standardizing workflows, labs can take simple, strategic steps to boost reliability, improve results, and keep operations running smoothly.
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In today's competitive lab environment, a LIMS is indispensable. A LIMS implementation is often complex and that can very easily go awry if all pieces of the project are not properly budgeted and accounted for. That budget is often a very large number, which should cover everything involved in deploying a powerful software system. That includes the critical choice of hosting model. Choosing a hosting model upfront is key, as delaying this decision can stall the entire project.
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