A forensics and medical examiners lab may analyze anything from body fluids and bone fragments to metals and fire debris as part of their broad commitment to answering questions of interest to a legal system. These laboratory-based investigations see forensic scientists collect, preserve, and analyze these types of samples (i.e., evidence) using a variety of special laboratory equipment and techniques. This broad array of analytical techniques and set of legal implications means such labs turning to informatics solutions like the laboratory information management system (LIMS) will require their information management solutions to meet the specific needs of their lab. [Read More]
Artificial intelligence (AI) and machine learning (ML) are must-have applications within labs of the future. AI allows deep mining of data across databases from labs throughout the enterprise, and ML is integral in forging otherwise undiscovered insights and linkages among data points that can accelerate projects and, sometimes, point them in novel directions. Getting the most from these systems requires more than optimizing the AI system. The quality and thoroughness AI delivers depends largely upon your laboratory information management system (LIMS). [Read More]
There are two key factors that are essential to Organizational Change Management (OCM) succeeding. They are: Strong Executive Leadership & Commitment and Alignment of the Organization Around Measurable Goals. In this blog we discuss these two important factors that contribute to success ofOCM. [Read More]
Research Data Management (RDM) refers to the methods of recording, organizing, storing, processing, and caring for information that is produced from a research project or used during a research project. It is an iterative and continuous process. The decisions that are taken in the early stages will substantially affect what will be carried out in the later ones. It is the most efficient way to properly manage the data, rather than trying to reconstruct everything after anything occurs. [Read More]
Autoscribe Informatics has released a new case study focusing on how Matrix Gemini LIMS transformed AMS Testing and provided a solid quality framework for its metallurgical testing. Using the new LIMS avoids the need to manually collate and create final reports, enables the creation of quotes and invoices directly from the LIMS, and automatically generates management reports as required. [Read More]
As the promise of "smart farming" and "precision agriculture" begins to emerge, it is increasingly clear that—like other areas of research and industry—effective data management, analysis, and visualization is increasingly important. In the case of sustainable farming and other agricultural similar endeavors, this means tapping into wide varieties of data to improve operational efficiency, crop yields, and automated tasking. In this 2021 paper by Giray and Catal, a data management reference architecture providing common vocabulary and templated solutions for agriculture software developers is discussed. The authors note their reference architecture is based off three agriculture-specific use cases, as well as other related reference architecture studies. After describing the domain scoping and modeling aspects of their data management reference architecture, they discuss its validation and practical use. They conclude that while the study focused on sustainability within agricultural domains, "it can be extended to a larger context by covering other critical aspects of agriculture," demonstrating "that the proposed data management reference architecture is practical and effective."