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Research Data Management

When you are doing research, good data management practices and transparency are essential. This toolbox provides practical information and guidelines for both PhD students and researchers when working with research data.

Archiving & Metadata

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.

Metadata standards for your data

There are metadata on multiple levels but for the purpose of this LibGuide we will focus on the following three levels:

 

Terminologies and ontologies used in the research

Every academic discipline or field uses ontologies (CESSDA: "structured controlled vocabularies") to limit complexity and organise data into information and knowledge that can easily be understood by colleagues in the same field. Be sure to check the faculty or departmental policy document(s) for standards that are commonly used in your field. Using similar ontologies and terminology will also facilitate the exchange of data and ideas and also the combining of datasets. If you are working on new concepts or new ideas and are using or creating your own ontology/terminology, be sure to include them as part of the documentation in your dataset (for example as part of your codebook). There are several resources for terminologies and ontologies:
  • At the FAIRsharing.org website a large list for multiple disciplines is available
  • 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

Descriptions of data assets available in a dataset

Most often, research groups use a discipline's standards to also describe data objects using naming conventions. 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. Often 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
C:\Images\Clean\2016-07-01_Subject1-H1c.tiff
C:\Images\Clean\Descript\2016-07-01_Subject1-H1c.Docx

Registration or description of the dataset

When you want to make sure that your dataset is findable it is recommended that the elements of the description of your dataset are made according to a certain metadata standard that allows for more easy exchange of metadata and harvesting of the metadata by search engines. Many certified archives use a metadata standard for the descriptions. If you choose a data repository or registry, you should find out which metadata standard they use. At the VU the following standards are used:
  • DataverseNL and DANS use the Dublin Core metadata standard
  • The VU Research Portal PURE uses the CERIF metadata standard
  • 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.