Laboratory & data terminology at a glance

AI in material development refers to the use of artificial intelligence to analyse, predict, and optimise material properties and development processes. The goal is to accelerate development through data-driven insights, improve understanding of relationships, and support informed decision-making in R&D.

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.

Bayesian optimization is a data-driven approach for iteratively optimising complex systems by proposing new experiments based on existing data. In chemical R&D, it is used to efficiently improve formulations, processes, and material properties.

Cloud computing refers to the delivery of IT resources—such as storage, computing power, and software applications—over the internet instead of local servers. It enables companies and laboratories to store, manage, and analyze data in a scalable and secure environment, reducing infrastructure costs while improving accessibility.

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 quality in R&D refers to the extent to which data is complete, accurate, consistent, and traceable, enabling it to be reliably used for analysis, decision-making, and development processes.

Data silos refer to isolated data sets that are stored in separate systems, applications, or departments without being connected. As a result, information cannot be used across systems, limiting transparency, efficiency, and decision-making in laboratory environments.

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.

Design of Experiments (DoE) is a systematic method for planning, conducting, and analysing experiments. Its goal is to evaluate the influence of multiple variables simultaneously and generate reliable insights with a minimal number of experiments.

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.

Formulation data management refers to the structured capture, management, and linking of data generated during the development, optimisation, and testing of formulations. Its goal is to make formulation data usable, reveal relationships, and enable data-driven development processes.

Generative AI refers to AI models capable of creating new content such as text, data, formulations, or simulation outputs. In R&D, it is used to support development processes, generate new approaches, and make more effective use of existing data.

An intelligent lab platform is a digital system that connects laboratory processes, data, and analytics in a central environment and actively supports them through AI, automation, and data integration. Its goal is not only to manage data, but to generate actionable insights and improve decision-making in laboratory environments.

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.

LIMS alternatives are software solutions and platforms that complement or replace the traditional functions of a Laboratory Information Management System (LIMS). They are used when laboratories require more flexible, data-driven, or research-oriented systems.

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.

Laboratory management software refers to digital systems used to plan, control, and document laboratory processes, data, and resources. It helps laboratories structure workflows, centrally manage data, and improve the efficiency and traceability of research and quality processes in the laboratory.

Large Language Models (LLMs) are AI models designed to process, understand, and generate natural language based on large volumes of text data. In industrial R&D and laboratory environments, they are used to analyse unstructured data, improve access to information, and support knowledge-driven decision-making.

A Manufacturing Execution System (MES) is a software solution that monitors, controls, and optimizes manufacturing operations in real-time. It bridges the gap between enterprise resource planning (ERP) and production equipment, ensuring efficiency, traceability, and quality management in manufacturing environments.

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.

R&D Management Software refers to digital systems used to plan, manage, and document research and development processes. It enables organisations to structure development projects, manage data and resources, and improve the efficiency and transparency of innovation processes.

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.

Replacing Excel in laboratories refers to the transition from spreadsheet-based workflows to specialised digital systems that structure and automate laboratory processes, data management, and analysis. The goal is to reduce manual errors, ensure data consistency, and enable more efficient and scalable workflows.

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.