Archive and share data

Archive and share data

Backing up your data is different from archiving. Backups can be discarded after a certain amount of time. Archiving is used to preserve a file as-is at the end of a project and acts as a static, final record. (Source: Oregon State University).
What data to keep?

Data can be archived and preserved locally or shared in a public data repository. Note that archiving can be costly and there may not be enough space to archive everything. Researchers should carefully identify which data to preserve. Consider the following:

  • Does the data support published research?
  • Are the data likely to be reused?
  • Are the data unique or historically significant?
  • Are there funder or institutional requirements?
  • Are the data difficult to reproduce?
  • Are there any ethical issues to consider?
  • Are the data in support of a patent application?

Examples of data that should be kept by discipline (from Stanford University).

Best practices when preparing data for archiving and sharing
File formats Choose long-term storage file-formats, preferably non-proprietary, to overcome software obsolescence. More information
Documentation Add it alongside your data to make it understandable, reusable. More information
Ownership and privacy If sharing data, make sure that:
Data integrity If keeping a local copy, avoid bit rot through refreshment (copy data on a new drive every 2-5 years) and replication (maintain three copies of the data, on two forms of storage with one in an external location).

Preparing sensitive data for sharing

Some data cannot be shared for legal or ethical reasons. However, if sharing the dataset is required, ensure that this has been stated in consent forms and cleared with the Research Ethics Unit. More information.

It may also be possible to retain multiple versions of the data: one for public release that has been de-identified, and one that is available on a highly restricted basis.

De-identification is the process used to remove identifying data. Identifiers can be direct, which point directly to an individual, or indirect, which point to an individual when combined with other data (see examples from Stanford University)

Below are two methods of data de-identification, with their benefits and drawbacks. (Source: UBC)

See also:

Updated: Thursday 4 June 2020
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