Backup & Versioning

Effective data backup is essential for protecting research assets from loss, corruption, or accidental deletion. One widely recommended strategy is the 3-2-1 backup approach, which offers a resilient framework for safeguarding data. We recommend researchers maintain three copies of their data: two stored on different types of local storage media, and one stored offsite. This structure minimizes the risk of complete data loss due to hardware failure, human error, or localized disasters such as theft, fire, or flooding.

One local backup can reside on an external hard drive or a university-maintained server, while the second backup should ideally be on a different medium. The offsite backup is crucial for disaster recovery and often takes the form of cloud storage or an institutional repository. Offsite backups should be ideally encrypted and regularly updated to ensure data remains secure and current.

At UCSB researchers have several backup and storage options including Box@UCSB, a secure cloud-based file-sharing service with version control, collaboration tools, and automatic backup features. For information on available backup and storage solutions visit campus IT: https://it.ucsb.edu/best-practices-employees/data-backup-and-storage

Version control is a foundational component of responsible academic research. It safeguards data integrity, enables efficient collaboration, and supports reproducibility and compliance. By selecting appropriate methods and implementing best practices, research institutions and individual scholars can enhance the quality, transparency, and longevity of their data assets.

Ensuring Data Integrity

Data integrity is fundamental to responsible and reproducible research. In academic settings, the ability to preserve original datasets while documenting all subsequent modifications is essential. Data versioning provides a systematic framework for tracking changes over time, allowing researchers to monitor the evolution of data and revert to previous states when necessary. This enhances transparency, facilitates reproducibility, and upholds the credibility of scholarly outputs.

Preventing Data Loss

Loss of research data can have significant implications, including delays in publication, compromised findings, and irreproducibility. Implementing versioning practices—such as maintaining successive versions of datasets—helps mitigate these risks. Each version serves as a safeguard against accidental deletion, corruption, or overwriting, ensuring that valuable data remains intact and recoverable throughout the research lifecycle.

Facilitating Data Recovery

When errors or inconsistencies arise, versioned datasets allow researchers to restore prior states efficiently. This capability reduces downtime, prevents duplication of effort, and supports a seamless research workflow. By systematically recording changes, researchers can identify the source of errors, correct them, and maintain the continuity of data analysis.

Enhancing Collaboration

Collaborative research demands clear communication, shared access, and accountability. Data versioning supports these goals by providing a documented history of changes, making it easier for teams to coordinate and integrate their contributions. By documenting who made each modification and when, version control facilitates efficient teamwork, reduces conflicts, and supports data stewardship.

Tracking Changes and Contributions

Tracking modifications helps teams monitor progress, assess the impact of changes, and ensure that all contributions are acknowledged. Data versioning provides a complete audit trail, enabling informed decision-making and improving research oversight. This promotes a culture of accountability and encourages best practices in data handling.

Methods of Data Versioning

Manual Versioning Techniques

Manual techniques are often used in smaller projects or early-stage research environments. While simple, these methods require discipline and can become inefficient as data complexity increases.

• Naming Conventions: A basic method where versions are distinguished by appending dates or version numbers to filenames. This approach is intuitive but relies on consistency and may be prone to errors.
• File Duplication: Creating file copies at key stages provides a rudimentary form of versioning. Though easy to implement, it can result in storage inefficiencies and confusion without careful organization.

Automated Versioning Systems

Automated systems offer scalable and efficient solutions for managing complex datasets and collaborative research.

• Version Control Software: Tools such as Git and Apache Subversion (SVN) track changes automatically and support concurrent access. These systems are widely used in research software development and increasingly adopted for data versioning, offering robust features for conflict resolution and historical tracking.
• Cloud-Based Solutions: Platforms like Box, GitHub, Open Science Framework (OSF), and Zenodo, provide versioning with cloud accessibility, security, and collaboration tools.