Research Data Management @FGB: Definitions

Archiving package (a.k.a. data package):

• The materials, such as data, metadata, code and other documentation that need to be archived after a research project is complete and/or a research article based on these materials has been published.
• Is also referred to as a “data package”, which is now the primary and preferred terminology for this concept.

Archiving research data:

• The long-term storage of research data in a manner that prevents modification, loss, damage or obsolescence of those research data in order to: uphold research integrity; allow for the replication of research results and; where appropriate, ensure the research data remain (re)useable well into the future.

Confidential data:

• Data which may only be accessed by authorized individuals. Personal data are one type of confidential data, but the term also applies to information about a business and its management, intellectual property, proprietary information and so forth. When determining the extent to which data must be kept confidential, the risks to the organization (such as VU Amsterdam) need to be considered (whereas when assessing privacy risks, the risks to an individual person are considered).

Data asset

• A collection of data in a similar form with a similar purpose. Examples are found at the bottom of this page from the RDM Libguide. Additionally examples relevant to FGB are described under Step 1 of the De-identification guide.

Directly identifying data (a.k.a direct identifiers):

• Data, such as name, address, photographs/video recordings of faces, for which little to no effort and no additional information are required to determine to whom the data belong.

NB: There isn’t really a perfect delineation between direct and indirect identifiers, but, rather, more of a sliding scale. Something may be a direct identifier because of it’s uniqueness (e.g. a unique tattoo) or based on context (e.g. the current Prime Minister of the Netherlands). It is important to be aware of any of these less commonly considered direct identifiers when you are working on de-identifying your data.

FAIR principles:

• Guiding principles for managing research data, and the associated metadata, so that the (meta)data can be properly understood, interpreted and reused. FAIR (meta)data should be findable, accessible, interoperable and reusable.

Indirectly identifiable data (a.k.a indirect identifiers):

• Data that require more effort as well as additional information to determine to whom the data belong. Indirectly identifiable data include genetic information, data that are unique to an individual (e.g. MRIs, EEGs), datasets with extreme or unusual values (e.g. extreme physical measurements unique to elite athletes, highly unique employment history) or any other characteristics about a person (e.g. ethnicity, gender, occupation and/or education) that when combined into one record, can single out that person as unique in your dataset. Indirectly identifiable data may not immediately identify an individual, but they do provide the potential for identification of that individual. More information on important aspects to consider about indirectly identifiable data can be found in the Privacy Risks guide.

Interoperable (meta)data:

• (Meta)data that are findable, accessible and reusable by machines regardless of the software or operating system in use.

Key file:

• A dataset containing the information necessary to re-identify the research subjects in your research dataset(s). Generally, a key file consists of:
  • A random identification code that together the information about each research subject that is present in the key file and the research dataset(s)
  • Directly identifying data about the research subject, often data that isn’t important for the research analysis
  • Details about what aspects of the research project that the research subject consented to, if applicable

Logbook/laboratory notebook:

• Research documentation that is an essential part of data provenance and research integrity. A logbook ensures that:
  • There is a record of problems, decisions and solutions made during the progress of your research
  • There is a record of all data corrections/manipulations
  • There is an easily retrievable record of decisions made by the research team
  • It is possible to continue the work you started (because of the clear record of what already happened)
• This page gives more background on logbooks/lab notebooks and provides an example of how to design a logbook

Metadata:

• Data that describe and explain details about your research data so that this research data can be found, properly understood and, if appropriate, reused well into the future.

Personal data:

• Any data that can be linked to an individual person. Personal data may be directly identifying or indirectly identifiable.

NB: Personal data do not necessarily need to be considered “sensitive” data to receive protection under the GDPR. If your data are considered personal, based on the definition above, they must be handled in accordance with the GPDR, regardless of whether or not you consider the data sensitive.

NB2: Almost all data about human subjects are considered personal data under the GDPR. The vast majority of the data collected at FGB are about human beings and should therefore be treated as personal data unless you have confirmed with an expert that the data are anonymous. This reference card on anonymity from the National Coordination Point for RDM further explains this concept.

Processed data:

• Data that have been modified in order to: 1) correct for errors or data entry mistakes, 2) convert the data to a form that can be more readily analysed and/or 3) reduce the identifiability of the data.

Pseudonymous data:

• Generally, the term pseudonymous data is synonymous with indirectly identifiable data, with some legal caveats.

Publishing research data:

• An extension of archiving research data: published data are also findable, accessible and reusable for new research by other researchers. This does not mean that data are open access; access to the data can be restricted and granted upon request. In such a situation, there must also be adequate information provided in the published metadata explaining how one can gain access to this data.

NB: Even if access to research data is restricted, the metadata and documentation about a research project should be published openly, as long as there is no confidential or privacy-sensitive information present in these materials. Doing so helps to make data findable, informs others about how data can be accessed, and promotes data as a form of research output for which researchers can receive recognition.

Raw data:

• Information and/or materials that are directly collected from a source, such as a research subject. These are the basis of your research and, in 99.9% of cases, an unchanged copy of this data should be preserved for research integrity purposes.

Research project:

• The research activities described in a research proposal that is submitted for ethical review. If ethical review is not applicable, a research project is any research activity aimed at answering a hypothesis; all activities aimed at answering one overarching hypothesis can be described as a single research project.