Raw data

Definition

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.

Expanded Explanation

In laboratory environments, raw data is generated by analytical instruments, sensors, test systems, or manual data capture. This includes measurement values, time series, spectra, images, or process parameters. Raw data reflects the original state of the captured information and is free from assumptions or modelling.

Traditionally, raw data is often stored locally or across multiple formats, making later reuse difficult. Without structured management, contextual information may be lost, data may be overwritten, or results may no longer be traceable. Systematic capture and management of raw data is therefore essential for reproducibility, quality assurance, and data-driven development.

Raw data serves as the foundation for subsequent steps such as data processing, analysis, visualisation, and the application of AI-powered models. The quality, completeness, and traceability of raw data directly determine the reliability of all downstream insights and decisions.

Key aspects of raw data:

• Unprocessed state
  Direct outputs from measurements or experiments
• High level of detail
  Contains full original information without aggregation
• Traceability
  Enables reproducibility and auditability
• Foundation for analytics & AI
  Starting point for evaluation, modelling, and prediction
• Context dependency
  Requires metadata such as time, instrument, method, or sample

Relevance to LabV

LabV ensures that raw data from analytical instruments and laboratory processes is captured in a complete, structured, and traceable manner. Rather than storing raw data in isolated or unstructured formats, LabV links it with metadata, samples, formulations, and quality information. This ensures that raw data remains usable over time and provides a reliable foundation for Material Intelligence, AI-powered analytics, and informed decision-making in R&D and quality assurance.

FAQ

Why is raw data important in laboratories?

Raw data enables transparency, reproducibility, and objective evaluation of results. Without access to raw data, analyses cannot be reliably verified or reused.

What is the difference between raw data and processed data?

Raw data is unmodified and uninterpreted, while processed data has been filtered, aggregated, or statistically analysed.

How should raw data be stored?

Ideally, raw data should be stored centrally, versioned, and linked with relevant metadata to enable long-term usability, comparability, and analysis.

Synonyms & Related Terms

Primary data, original data, measurement data, unprocessed data, source data

Internal Links

Laboratory Data Integration, Data Synchronization, Laboratory Informatics, AI in the Laboratory, Material Intelligence