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verschiedene Symbole, die für Forschungsdaten stehen
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What is research data?

= all data that is generated, collected, collated and analysed in the research process

Varies greatly depending on the discipline, e.g.

  • Measurement data
  • Laboratory data
  • Samples
  • Geo and environmental data
  • Audiovisual information
  • Source code
  • Texts
  • Image material
  • Survey data
  • Objects from collections

A distinction is made between the following types of research data based on their specific characteristics:

Characteristics:

  • Data is captured in real time
  • Mostly irreplaceable

Examples:

  • Sensor data
  • Survey data

Characteristics:

  • Mostly created in the laboratory
  • Reproducible, but expensive

Examples:

  • Gene sequences
  • Chromatograms

Characteristics:

  • generated by test models
  • Model and metadata more important than output

Examples:

  • Climate models
  • Economic models

Characteristics:

  • derived or compiled from other data
  • reproducible

Examples:

  • Text mining
  • 3D models

Characteristics:

  • Collection of small data sets
  • Mostly published

Examples:

  • Genetic sequence database
  • Primary text source

Characteristics:

  • Digital version of an analogue object
  • Reproducible as long as the original is available

Examples:

  • Manuscripts
  • Thin sections of rock

For documentation, permanent storage or long-term archiving (LTA) and making accessible, data that originates from non-reproducible observations or experiments or can only be reproduced with great effort is of particular importance.

Documentation

Who is the documentation for?

  • For the researchers themselves: good documentation helps to keep track of the data and clearly structure the activities
  • for other project participants and funding bodies, but also for interested colleagues or beneficiaries of the data, in order to be able to categorise the content correctly and understand the research work

What is documented?

  • Description of the context of development and the constraints and experimental conditions.
  • Description of the method used for data collection and processing including the tools used (devices and software).
  • Contents from test protocols, field reports, lab books.
  • device-specific information required for interpretation of the data.
  • Description of the measures and procedures implemented for quality assurance.
  • Information on the data source when using existing data (references, DOI).
  • Information about technical standards and calibrations.
  • Documentation and explanation of the parameters, variables, abbreviations and codes used, including column headings in data tables.
  • Documentation of the persons involved and their tasks.
  • Documentation of the framework conditions for the long-term storage and subsequent use of the data (licences, usage restrictions, embargo periods, deletion rules).
  • List all associated files and folders and a description of their formats and contents.
  • Links to publications in which the data are used or cited.

When to start the documentation

  • The structure of the documentation should already be in place before the data is collected.
  • The documentation should be complete at the end of the project.