Data structure and description

Answer toMetadata is data about data that provides information about the content, structure, provenance and format of a dataset. For example, the metadata of a photograph may include information about the date, location and camera settings. It is recommended to collect metadata ReadMe in a file or repository metadata record. Metadata is essential for other researchers to find, understand and use the dataset.

Answer: A metadata record is descriptive, structured and concise information about a dataset, describing the content and context of the deposited dataset for both machines and humans (machine and human readable) in plain language. The author of the study or the custodian of the dataset shall create a metadata record in the repository at the time of deposit of the dataset, including documentation. The more complete the metadata record, the better the dataset Located atand greater potential for re-use.

Answer toA: Data repositories are online data repositories or digital platforms designed to securely store, organise, share and update research data over the long term. They provide long-term preservation and accessibility for both data and metadata, while promoting compliance with good data management practices.

Answer: Data grouping should be thought of first in the context of its use and processing, not just as a formality for a data management plan. Grouping is essentially about creating logical sets of data – by how the data are collected, analysed and into what units they function in the course of the study. This structured approach not only helps to describe the data more clearly in the DPP, but also provides clarity for future research and documentation of results.
Recommendations for data grouping:
  • By content and research function, for example: a single dataset – results of one experiment; another – model input data
  • By data extraction or processing steps, e.g.: raw data, processed data, final results, re-used data
  • By format or technical structure if this facilitates storage or re-use, e.g. all CSV sets in one group
  • By responsible person or data source, if this helps to structure the data descriptions in the team’s work
The most important thing is that the grouping makes practical sense and reflects how the data are actually handled.

Data structure and description

Answer toMetadata is data about data that provides information about the content, structure, provenance and format of a dataset. For example, the metadata of a photograph may include information about the date, location and camera settings. It is recommended to collect metadata ReadMe in a file or repository metadata record. Metadata is essential for other researchers to find, understand and use the dataset.

Answer: A metadata record is descriptive, structured and concise information about a dataset, describing the content and context of the deposited dataset for both machines and humans (machine and human readable) in plain language. The author of the study or the custodian of the dataset shall create a metadata record in the repository at the time of deposit of the dataset, including documentation. The more complete the metadata record, the better the dataset Located atand greater potential for re-use.

Answer toA: Data repositories are online data repositories or digital platforms designed to securely store, organise, share and update research data over the long term. They provide long-term preservation and accessibility for both data and metadata, while promoting compliance with good data management practices.

Answer: Data grouping should be thought of first in the context of its use and processing, not just as a formality for a data management plan. Grouping is essentially about creating logical sets of data – by how the data are collected, analysed and into what units they function in the course of the study. This structured approach not only helps to describe the data more clearly in the DPP, but also provides clarity for future research and documentation of results.
Recommendations for data grouping:
  • By content and research function, for example: a single dataset – results of one experiment; another – model input data
  • By data extraction or processing steps, e.g.: raw data, processed data, final results, re-used data
  • By format or technical structure if this facilitates storage or re-use, e.g. all CSV sets in one group
  • By responsible person or data source, if this helps to structure the data descriptions in the team’s work
The most important thing is that the grouping makes practical sense and reflects how the data are actually handled.