What is it? Why is it important?

Metadata describes and gives information about data. Also referred to as “data about data”.

Metadata provides basic information on data. Its aim is to help organise, find and understand data (e.g. images, videos, or spreadsheets).

Metadata can be evaluated and displayed by search engines during searches.

Metadata can explain the origin, the purpose, the time, the geographic location, the author, the access, and the terms of use regarding data.

Examples of metadata information include descriptive-, structural-, administrative-, explanatory-, statistical-, keywords information (see added explanations under more)


Examples of metadata information

  • Descriptive information: Intellectual content or origin. Allows the data to be searched and retrieved (e.g. title, abstract, author, keywords)


  • Structural information: Internal structure, technical description (e.g. page, sections, chapter numbering, indexes, table of content, variable names, dataset location)


  • Administrative information:Long-term and short-term management and processing (e.g. technical data, access control, user requirements, archiving)


  • Explanatory information: Provides context to statistical data (e.g. the methodology used for data collection and aggregation or, methods used for data quality check)


  • Statistical information: Processes used to collect, process, and describe statistical data


  • Keywords: Provides the search engine with additional information

What do I need to do?

As a SP-INV, make yourself familiar with:

  • Data formats including its applicable metadata (e.g. definition of codes and classification schemes). Example of data formats: 31st January 1999, 31/01/1999 or 31.1.99 99.01.31, 31011999, or today
  • The use of standardised- / international data formats (e.g. CDISC format)
  • Metadata requested by authorities (e.g. SNF, FDA, EMA)


Examples of study data and their metadata

  • Gender: female = 1; male = 2, unknown = 99
  • Primary study endpoint reached: yes = 1, no = 0
  • Missing values: D = participant dropped out of the study, L = participant lost to follow-up
  • BMI: algorithm = weight (kg) / height (m2)


Data sharing and the comparability of study results are increasingly facilitated through the use of standardised formats, including the information provided through metadata.

Where can I get help?

Your local CTU can support you with experienced staff regarding this topic

External Links

CDISC – see in particular

  • Define-xlm – It transmits metadata that describes any tabular dataset structure

DataCite – see in particular

  • DataCite Metadata Schema – List of core metadata properties chosen for an accurate and consistent identification for citation and retrieval purposes + recommended use instructions
  • CDISC – Clinical Data Interchange Standard Consortium
  • CTU – Clinical Trials Unit
  • EMA – European Medicines Agency
  • FDA – Food and Drug Administration
  • SNF – Swiss National Foundation
  • SP-INV – Sponsor Investigator
Basic ↦ Data Handling ↦ Study Database ↦ Metadata

Provides some background knowledge and basic definitions

Basic Monitoring
Basic Drug or Device

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Drug or Device

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Drug or Device

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Ethics and Laws
Set-Up Quality and Risk
Set-Up Drug or Device

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Drug or Device

Starts with last study visit completed

Ends after study publication and archiving

Completion Statistics
Completion Drug or Device
Current Path (click to copy): Basic ↦ Data Handling ↦ Study Database ↦ Metadata

Please note: the Easy-GCS tool is currently under construction.