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Research Data Management: Data classification

Data classification

While writing your dissertation you may (re)use existing data or create new data. These data sets may contain sensitive data. Sensitive data is frequently classified as: public, internal, confidential or secret. The data classification is important as it determines how you store, share and publish your data.

In the next paragraph a classification of sensitive data is provided that may help you think about how to manage your data. The activity looks at the considerations and actions required when sharing or publishing sensitive data.

Example of classification of sensitive data

Data classification

Description

Indicative examples for personal data

public

Public information on public websites and in public publications.

name, publications

internal

Not intended for the public but at the same time not harmful, should it fall into the wrong hands.

memo’s, instructions, guidelines

confidential

Indirect sensitive data which could harm people, organisations or governments.

Information about a person’s economic position (credit / debt).

secret

Direct sensitive data. Data that will immediately have severe consequences for people, organisations or governments.

Information about a person’s psychological, medical or criminal status.

Sharing data

Sensitive data identifies individuals, species, objects or locations, and carries a risk of causing discrimination, harm or unwanted attention. The Sensitive data decision tree (PDF) from the Australian National Data Service (ANDS) shows the data sharing considerations and actions required when sharing or publishing sensitive data.

View the decision tree and answer the questions:

  • Do you have sensitive data that you can share?

  • How can you share this data as openly and ethically as possible?

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