The FDA’s Case for Quality initiative seeks to shift the focus from validation as a check-the-box regulatory activity to one that centers around quality. The shift will ensure that patients and end users have access to safe, effective care. The FDA issued the Computer Software Assurance (CSA) draft guidance in September 2022 to make validation of regulated systems less burdensome and more strategic. This blog will look at what we know about CSA—the good news is that our clients shouldn’t notice many changes in our validation approach. [Read Now]

The types of testing found in a food and beverage laboratory can be quite diverse depending on the role the laboratory plays within the industry. Together, these labs and their strict test methods serve the populace by better ensuring the safety and security of the foods, beverages, and ingredients we depend on. [Read Now]

As laboratories continue to grapple with an increasing volume of data, implementing a solution becomes a necessity. LIMS is a solution designed to help laboratories manage their data effectively. With the increasing volume of data generated in labs, LIMS offers advanced features to streamline workflows and improve data accuracy. From sample collection to analysis, LIMS ensures seamless integration of data across the entire laboratory workflow. In this blog article, we will examine how LIMS optimises data management in laboratories.  [Read Now]

Establishing collection and sample management protocols for pervasive per- and polyfluoroalkyl substances (PFAS) is time consuming and complex for laboratories worldwide. PFAS testing requirements and legislation are rapidly developing as scientists build a better understanding of the impact of these chemicals. And the pressure is on laboratories to meet these changing demands. So, why is PFAS testing on the rise and how can we support analytical testing laboratories in meeting this challenge?  [Read Now]

A LIMS for material testing labs manages samples, data, quality control, compliance, and more for ensuring high-quality, reliable results. Materials testing can involve a multitude of test methods defined by a variety of accreditation and regulatory requirements. This FAQ examines the LIMS functionality most valuable to a materials testing laboratory, including specialty functionality that not all LIMS vendors may provide. [Read Now]

Is your lab still trapped by Excel spreadsheets or clinging to outdated LIMS? It’s time to embrace the future. A new, upgraded LIMS is the key to a tomorrow-ready lab. With the latest features at your disposal, it’s a game-changer that can skyrocket your lab’s operational efficiency. Say goodbye to rigid processes and hello to flexibility, saving significant time and money while achieving your lab’s specific goals.  [Read More]

STARLIMS proudly announces the launch of Life Sciences for Public Health (LPH) 1.0, our cutting-edge Laboratory Information Management System (LIMS) designed specifically for the Public Health sector. Its release is the first step toward a broader product roadmap that will help redefine the landscape of laboratory management in the Public Health domain. [Read More]

In this 2023 article published in the journal F1000Research, Williamson et al. identify and discuss "eight key challenges in data management that must be addressed to further unlock the potential of [artificial intelligence] in crop and agronomic research." Noting the state of the agricultural research landscape and growing potentials of artificial intelligence and machine learning in the field, the authors perform a literature review that better shapes the nuances of those eight key challenges: data heterogeneity, data selection and digitization, data linkages, standardization and curation of data, sufficient training and ground truth data, system access, data access, and engagement. After examining these challenges in detail, the authors conclude there's a definitive "need for a more systemic change in how research in this domain is conducted, incentivized, supported, and regulated," with aspects such as stronger collaboration, more efficient machine learning methods, improved data curation, and improved data management methods.