Data documentation during the project

Tip: Why is this topic important
» Understanding, analyzing and reusing data depends on the practice of documenting, structuring, naming and other ways of describing data
» The inclusion of metadata, providing data about the data used in the project, secures the usefullness of the data, both internally and externally of your project
» Interpretation of project results requires an understanding of the data provenance/data lineage, i.e. where the data originates from and how it was processed.
» Collecting data documentation should start as early as possible and prepares for making data accessible accompanied by structured metadata.

About this chapter

This chapter collects information about how metadata and other accompanying information will be handled in the active phase of the project.

Question-specific guidance

How will you link data and metadata?

Multiple selections will often be applicable to a project. Basic descriptive techniques will be relevant to many projects: structured naming of files and folders, using a readme-file to provide information, using embedded metadata in files or using a seperat metadatafile (a sidecar file for each file in the dataset). More advances techniques that can be relevant is using a database system for linking metadata and data, or establishing a data/variables dictionary for the data in the project.

Supplementary info: Almost all computer systems will provide some system metadata embedded in files, and provide info on creation date and who has write or read access to the file. Most systems will also provide users with some possiblilities for adding user metadata that can be embedded in files, like descriptive tags.

Do suitable metadata standards exist for your data?

You should be awere of, and choose, a metadata standard as soon as possible when planning and doing your project. Many research fields has established a metadata standard that is used widely within the field. At the same time, most repositories for research data will implement specific standards (often using Dublin Core as a starting point), so planned use of an archive will often lead to planned use of a metadata standards. When in doubt about relevant metadata standards within your field, the Dublin Core standard will often be a good choice.

Will you use existing vocabularies/ontologies/terminologies to describe your data?

If you want to facilitate interoperability of your data with data you get from elsewhere, and facilitate re-use of your own data later, it is handy if you use standard ways to encode certain data. Such standard encodings are sometimes called terminologies or ontologies.

Supplementary info: Please concider what vocabularies, ontologies or terminology that has relevance within your field of research, and apply these to highten precition when describing research data. When in doubt of relevance, please look for usage by others within your field, like in published published journal articles og connected to published datasets.

How are the rights of the collected data arranged?

Discussing make agreements regarding use rights and potential intellectual property rights prior to collection. Not all data are covered by the copyright act, and some data will be databases which are related rights. If Intellectual Property Rights are defined by a contract/agreement, make sure to include it in the answer.

Supplementary info: Defining rights and providing licenses to collected data will often reduce the potential for later conflicts around internal and external use (and reuse) of research data within and after the project period.

Are there any use restrictions for these data?

Are there any limitations on the data use e.g. only for research on certain types of diseases, sharing only within certain geographical boundaries, etc.? Describing data use in a formalised way by using the data use ontology (DUO) greatly improves the data reusability.

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Will a license be assigned to your data as early as possible?

It is not always clear to everyone in the project (and outside) what can and can not be done with a data set. It is helpful to associate each data set with a license as early as possible in the project. A data license should ideally be as free as possible: any restriction like ‘only for non-commercial use’ or ‘attribution required’ may reduce the reusability and thereby the number of citations. If possible, use a computer-readable and computer actionable license.

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