Laboratory & data terminology at a glance

AI in the laboratory refers to the use of artificial intelligence for the automated collection, processing, and analysis of laboratory data in order to make research and testing processes more efficient, precise, and scalable.

AI Agents are autonomous software programs that capture relevant data from their system environment, make decisions, and act independently.

Artificial intelligence (AI) refers to the simulation of human intelligence in computer systems that can analyze data, recognize patterns, make decisions, and automate complex tasks.

Assistance systems are digital or AI-based applications that help the laboratory to perform complex tasks more efficiently, faster and with less errors.

Cloud Computing bezeichnet die Bereitstellung von IT-Ressourcen wie Speicher, Rechenleistung und Anwendungen über das Internet anstelle lokaler Server.

Laboratory data integration refers to the process of systematically combining data from different laboratory, analytical, and enterprise systems. The goal is to create a central, consistent data foundation that enables efficient analysis, improved decision-making, and seamless digital laboratory workflows.

Data management is the structured collection, organisation, and use of data to ensure accuracy, accessibility, and security.

Data synchronization refers to the process of automatically aligning and keeping data consistent across different systems, applications, or data sources. Its goal is to ensure that all involved systems always operate with up-to-date, complete, and consistent information.

A Digital Lab Assistant is an AI-powered software tool that helps researchers, scientists, and quality engineers automate tasks, manage data, and optimize laboratory workflows.

A digital twin is a virtual representation of a physical object or process, continuously updated with real-time data to simulate its behaviour and performance.

Digitalization refers to the process of transferring analogue, manual, or paper-based processes, data, and workflows into digital systems.

An Electronic Lab Notebook (ELN) is a digital tool that replaces traditional paper-based laboratory journals for documenting test procedures, results, and observations in research and laboratory environments.

Enterprise Resource Planning (ERP) is a comprehensive software system that integrates and manages core business processes such as finance, supply chain, production, and human resources.

Formulation refers to the process of designing, developing, and optimizing the composition of a material, chemical, or product to achieve specific performance characteristics.

A laboratory information and management system (LIMS) is a software solution for centrally managing, tracking, and organizing samples, laboratory data, and workflows to ensure efficiency, data integrity, and regulatory compliance.

Laboratory digitization refers to the transformation of laboratory processes through digital technologies, automation and AI-supported data management.

Laboratory informatics refers to the use of digital technologies, software solutions and data management tools to optimize laboratory processes, improve data quality and support efficient research and quality assurance.

Ein Manufacturing Execution System (MES) ist eine Softwarelösung, die Produktionsprozesse in Echtzeit überwacht, steuert und optimiert.

Material intelligence describes the systematic use and linking of relevant data, workflows and AI to enable better decisions in R&D and QA.

A material database is a centralized digital repository that stores, organizes, and manages data related to material properties, compositions, and performance characteristics.

Material development is the process of designing, optimizing, and testing new materials to meet specific performance, safety, and sustainability requirements.

Materials informatics refers to the use of data science, AI and machine learning and computational methods to accelerate the development, optimization and discovery of new materials in R&D.

Predictive AI refers to the use of artificial intelligence, machine learning, and statistical models to forecast future events, properties, or developments. It analyses historical and real-time data, identifies patterns, and generates predictions that help companies and research institutions make better strategic and operational decisions.

Raw data refers to unprocessed, original data that is captured directly from measurements, experiments, or analyses. It exists without interpretation, aggregation, or statistical processing and forms the primary basis for laboratory analysis, evaluation, and decision-making.

Trend prediction describes the process of analysing historical data, patterns, and external factors to forecast future developments in a specific industry or market.

User Experience (UX) refers to the overall interaction, satisfaction, and usability of a product or software from the user's perspective. It encompasses design, functionality, accessibility, and emotional response, ensuring that digital systems are efficient, intuitive, and user-friendly. A well-optimized UX improves workflow efficiency, reduces errors, and enhances engagement across industries.

A user interface (UI) is the visual and interactive level of a software application that users use to work with digital systems.

Visualization refers to the graphical representation of data, results, and relationships to make complex information easier to understand, compare, and interpret. In R&D and laboratory environments, visualization supports the rapid assessment of measurement data, trends, and patterns to enable informed decision-making.