Strong lab management support is the backbone of reliable operations. When systems fail or compliance challenges arise, the right support keeps your lab running smoothly, minimizes downtime, and helps teams stay focused on delivering accurate, timely results that build trust and confidence.
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In this whitepaper, explore how the Fourth Industrial Revolution is reshaping quality control labs. Learn the key challenges limiting lab performance today, and discover best practices and strategies to modernize QC operations—boosting efficiency, reducing waste, and maintaining product quality in the smart manufacturing era.
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In this case study, CSols replaced an outdated LIMS with Thermo Scientific SampleManager v11.1, integrating it with the client’s ERP system. The implementation included data migration, process automation, and LIMS-ERP alignment—resulting in standardized workflows, higher efficiency, and improved release speed.
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Baruel, a leading Brazilian foot-care and cosmetics manufacturer, modernized its quality control lab by replacing manual processes with myLIMS. The transformation improved efficiency, enhanced traceability, and enabled faster, more reliable reporting through digitalized, real-time operations.
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Accurate allergen testing is essential to protect consumers and maintain trust in food production. Modern food labs use advanced tools and workflows to detect hidden allergens, ensure regulatory compliance, and prevent costly recalls while helping every product reach the market with confidence and safety.
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FAIR Data Principles are guidelines to make scientific data
Findable,
Accessible,
Interoperable, and
Reusable. Rather than a single standard, FAIR is a framework for making data easier to locate, share securely, and reuse. As data volumes and collaborations grow, adopting FAIR is key to accelerating discovery and improving compliance. This series explores each FAIR component—starting with
Findable—and how modern LIMS solutions can help labs apply these principles.
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In this episode, we break down what different LIMS hosting models—on-prem, cloud, and SaaS—really mean for the end user. Hear how each option impacts user empowerment, collaboration, and your lab’s ability to adapt in a fast-changing environment.
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Clinical diagnostics is transforming healthcare by enabling earlier disease detection, personalized treatments, and faster decision-making. With consistent, accurate, and efficient testing supported by advanced lab management systems, laboratories are driving better outcomes, reducing errors, and shaping the future of medicine.
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Becoming certified to ISO/IEC 17025 is a complete process that requires commitment, planning, and meticulous execution by the laboratory. This blog explores the 10 fundamental steps a laboratory needs to follow to obtain accreditation in ISO/IEC 17025.
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Justifying a LIMS purchase requires documenting both measurable cost savings from reduced labor and greater efficiency, as well as qualitative benefits like improved data quality, better decision making, and enhanced regulatory compliance. The quantitative case uses a nine-step ROI process that assesses current inefficiencies, estimates time savings from automation, and compares those savings to total LIMS costs.
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This presentation explores how business process modeling notation (BPMN) can be transformed into a semantic representation using knowledge graphs. This approach enables richer data integration, queryability, and reasoning across enterprise processes.
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Scientific notebooks have always been at the center of discovery. But today, an ELN that simply records and stores data is no longer enough. Scientists require more. They require a solution that truly thinks the way they do—working as an active collaborator to help them design, plan, execute, and analyze experiments seamlessly.
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Many labs don’t realize they’re falling behind until competitors surge ahead with advanced tools and smarter management. Warning signs include paper logbooks, missed deadlines, instrument downtime, and audit stress. Modern laboratory software and LIMS boost efficiency, cut costs, improve accuracy, and drive profitable growth.
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Whether in pharma, diagnostics, or biobanking, lab success depends on meeting goals for speed, quality, and accountability. Yet manual workflows and fragmented data slow progress, while growing sample complexity, cross-site collaboration, and evolving regulations expose the limits of outdated sample management.
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Effective end of year lab management isn’t just about December—it begins in October. By using Q4 to review compliance, refresh inventory, recognize teams, update systems, align budgets, and plan strategically, labs reduce risk, improve efficiency, and enter the new year ready to thrive.
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Traceability in a laboratory is essential to ensure the quality and reliability of results. By accurately identifying samples and adopting advanced tracking systems, labs can strengthen traceability and boost operational excellence. Here are four proven strategies to build a more accurate, efficient lab.
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A Scientific Data Platform addresses the challenges of disorganized and siloed lab data. By centralizing information, it streamlines collaboration, enhances analysis, and automates workflows. The result is greater efficiency, fewer errors, and accelerated research across today’s fast-paced labs.
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In April 2025, clinical research entered a new phase as the FDA released its Roadmap to Reducing Animal Testing and the NIH launched ORIVA to scale human-relevant models. These efforts reflect a growing consensus that animal studies often fail to predict human outcomes and that safer, faster alternatives are needed.
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Laboratories today face growing risks to data privacy as AI, cloud platforms, and global collaboration expand. Protecting sensitive information requires secure systems, role-based access, encryption, automated compliance, and strong training programs to safeguard science, patients, and trust.
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Lab informatics may share core terms like samples and workflows, but each lab speaks its own “dialect.” QC labs focus on compliance, research labs value flexibility, clinical labs need speed, and forensic labs demand traceability. Recognizing these differences helps build systems that enable collaboration and innovation across domains.
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