Lab informatics has traditionally been an ecosystem of specialized systems, each designed to address a distinct aspect of laboratory operations. From LIMS (Laboratory Information Management Systems) to ELN (Electronic Laboratory Notebooks) and QMS (Quality Management Systems), this “alphabet soup” of acronyms has long defined how labs manage data, workflows, and
Most labs are still running on legacy LIMS (Laboratory Information Management Systems). These systems were groundbreaking when they first rolled out. They’ve kept labs organized, compliant, and productive for decades. But here’s the thing: times have changed. Technology is moving fast, lab workflows are evolving, and regulations are
In any LIMS implementation, we can’t avoid making assumptions. These are guesses we make about how things will work—like thinking a client’s data will be easy to migrate, assuming our software will integrate smoothly with other systems, or expecting that everyone will learn the new software quickly.
When you’re setting up a Laboratory Information Management System (LIMS), you need to know it works. This process is called "validation," and it requires certain documents to prove that your LIMS is set up right and does what it should. In this article, we’ll go through
Old and reliable, or new and bold? This article explores the key differences between established and emerging LIMS platforms. Understand the trade-offs in reliability, customization, and cost to help make the best choice for your lab’s success and long-term growth.
Not all LIMS are the same. Some features are so important that, without them, the system wouldn’t work properly. Here are the key features that, if taken away, would make a LIMS useless. 1. Sample Tracking The most important job of a LIMS is to track samples. This means
Measuring the progress of your LIMS implementation through conventional metrics like budget adherence, timeline accuracy, and system performance is non-negotiable. But do these metrics give you a true sense of how well the project is going? Focusing solely on these conventional metrics can overlook deeper, more dynamic aspects of a