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🎓 Purpose

Data integrity is essential to research, regardless of data classification, since unauthorized modifications to your data can disrupt a project, produce incorrect results, undermine trust in academic research, and affect your reputation. It is important that throughout the research workflow and lifecycle that the integrity of the data is verifiable.

👥 Audience

🔖 Contents

Initial considerations

💽 Establish a resilient backup strategy and back up your data.

• Should unauthorized modifications take place, it is important that you have a “clean” (unaffected) backup to revert back to.

  • Back up data

🖥️ Departmental and divisional resources and recommendations.

• Contact your local IT group supported or recommended file verification and data integrity tools that are available to you.

  • https://uoft-infosec-cf.atlassian.net/wiki/spaces/ISH/pages/4948958/Additional+help#%F0%9F%96%A5%EF%B8%8F-Information-Technology-(IT)

\uD83D\uDCD8 What can I do?

🔢 Compute and check checksums on your integral data files.

• Hashing algorithms provide a unique output sequence for every unique input. This means that even if a single bit of a file has been altered, a radically different output sequence will be produced, even if the input file might initially appear unmodified.

Windows

• File Checksum Integrity Verifier

  • https://en.wikibooks.org/wiki/File_Checksum_Integrity_Verifier_(FCIV)_Examples

MacOS

• SHA Checksum command

  • https://ss64.com/osx/shasum.html

Linux

• SHA256 Checksum command

  • https://www.man7.org/linux/man-pages/man1/sha256sum.1.html

🗂️ Maintain version control.

• Practicing manual version control or leveraging a version control system allows you to properly manage and retain the numerous revisions to a file that might take place over the course of a task or project.

  • https://onesearch.library.utoronto.ca/researchdata/organizing-your-data#Version%20control