Additional information to document the dataset

It is also recommended to include documentation providing additional information on the data structure, extraction process and analysis. This helps to navigate through the data.
The documentation related to datasets can be divided into three groups:
  • Structuring and interpreting data: helps you understand the structure of a dataset, the meaning of variables and the principles of classification. Examples: ReadMe files, data dictionaries, codebooks, classification schemes
  • Documentation of data extraction: provides information on the data collection process, tools and methods used. Examples: samples, forms, questionnaires, protocols, laboratory notes or logs, descriptions of methodology
  • Data analysis and interpretation of results: provides insights into data processing and links data to study results. Examples: analysis syntax or scripts, algorithms, code comments, links to reviews and publications (preferably with DOIs)
Metadata and documentation are sometimes used interchangeably. More broadly, documentation is also referred to as metadata, but it should be remembered that documentation is more human-readable than machine-readable.
The short form of the metadata allows interested parties to quickly consult the data and decide whether the information is relevant to what they are looking for and whether they need to focus on the possibilities of accessing the full dataset.
Additional documentation, such as codebooks or questionnaires, allows the researcher to understand the nature of the data and whether the dataset can be used for another study.

Additional information to document the dataset

It is also recommended to include documentation providing additional information on the data structure, extraction process and analysis. This helps to navigate through the data.
The documentation related to datasets can be divided into three groups:
  • Structuring and interpreting data: helps you understand the structure of a dataset, the meaning of variables and the principles of classification. Examples: ReadMe files, data dictionaries, codebooks, classification schemes
  • Documentation of data extraction: provides information on the data collection process, tools and methods used. Examples: samples, forms, questionnaires, protocols, laboratory notes or logs, descriptions of methodology
  • Data analysis and interpretation of results: provides insights into data processing and links data to study results. Examples: analysis syntax or scripts, algorithms, code comments, links to reviews and publications (preferably with DOIs)
Metadata and documentation are sometimes used interchangeably. More broadly, documentation is also referred to as metadata, but it should be remembered that documentation is more human-readable than machine-readable.
The short form of the metadata allows interested parties to quickly consult the data and decide whether the information is relevant to what they are looking for and whether they need to focus on the possibilities of accessing the full dataset.
Additional documentation, such as codebooks or questionnaires, allows the researcher to understand the nature of the data and whether the dataset can be used for another study.