When you archive your data, you should provide metadata describing your dataset. In other words, you should provide information about your data, so that other potential users (humans or machines) know, for example, who created the data, where, when and with what purpose(s) the data were collected. Describing your dataset with good metadata helps to make it findable. Preferably, researchers use metadata standards that are common for the field they work in (see below).
If you want to archive your dataset in such a way that it is compatible with the FAIR-principles, you can use the information in this practical guide which describes how to implement the FAIR data policy and this table which matches metadata fields from different systems (these documents were written for the Faculty of Behavioural and Movement Sciences).
The Dutch Techcentre for Life Sciences has developed open source software code to enable you to make your dataset's metadata FAIR. The software is being developed through GitHub and full details on the FAIR Data Point Software are available there. The Dutch eScience Center also developed Fair Data Point software, of which full details are, similarly, available on GitHub.
Each discipline has its own metadata standards. Be sure to check the faculty or departmental policy document(s) for standards that are commonly used in your field. The UK Digital Curation Centre (DCC) provides an overview of metadata standards for different disciplines. The list is a great and useful resource in establishing and carrying out your research methodology. Go to the overview of metadata standards.
DataverseNL and DANS use the Dublin Core metadata standard. The VU Research Portal PURE uses the CERIF metadata standard. If you choose another data repository or registry, you should find out which metadata standard they use.
Most often, research groups use a discipline's standards to describe data objects. There are, however, other guidelines for naming conventions and document versioning which can be useful for all documents, independent of whether they are research data or not. The table below gives an example of this.
|Data Stage||Dataset description||Type of data||Versioning|
|Raw data||Consumer spending data||Text files||2017-02-23_ConsumerSpending_1.2.txt|
|Processed data||Anonymized Transcription of patient interviews||Word files, Excel||2014-11-17_RawTranscription_Checked1.docx|
|Analysed data||Photo Images with descriptions||TIFF files, Word file||C:\Images\Raw\2016-07-01_Subject1-V2.tiff