Data Management

The following practices are best utilized in a formal data management plan – a document that contains the basics of how you will acquire, manage, describe, analyze, and store your data, and what mechanisms you will use at the end of your project to share and preserve that data.

Image via Nature -- Data Management Made Simple

• Create and document a data backup policy: A backup policy helps manage users’ expectations and provides specific guidance on the “who, what, when, and how” of the data backup and restore process. 

• Create, manage, and document your data storage system: Data files should be managed to avoid disorder. To facilitate access to files, all storage devices, locations and access accounts should be documented and accessible to team members.

• Define expected data outcomes and types: In the planning process, researchers should carefully consider what data will be produced in the course of their project. 

• Define roles and assign responsibilities for data management: In addition to the primary researcher(s), there might be others involved in the research process that take part in aspects of data management. By clearly defining the roles and responsibilities of the parties involved, data are more likely to be available for use by the primary researchers and anyone re-using the data. Roles and responsibilities should be clearly defined, rather than assumed; this is especially important for collaborative projects that involve many researchers, institutions, and/or groups.

• Define the data model: A data model documents and organizes data, how it is stored and accessed, and the relationships among different types of data. The model may be abstract or concrete.

• Identify data sensitivity:

  1. Determine if the data has any confidentiality concerns.

  2. Document data concerns identified and determine overall sensitivity (Low, Moderate, High).

  3. Develop data access and dissemination policies and procedures based on sensitivity of the data and need-to-know.

  4. Develop data protection policies, procedures and mechanisms based on sensitivity of the data.

• Identify suitable repositories for the data: Shaping the data management plan towards a specific desired repository will increase the likelihood that the data will be accepted into that repository and increase the discoverability of the data within the desired repository.
• Plan data management early in your project: When you develop hypotheses and the design of sample collection for your new project, you should also plan for data management. Careful planning for data management before you begin your research and throughout the data’s life cycle is essential to improve the data’s usability, and ensure data’s preservation and access both during the project and well into the future.

• Plan for effective multimedia management: Multimedia data present unique challenges for data discovery, accessibility, and metadata formatting and should be thoughtfully managed.

• Provide budget information for your data management plan: As a best practice, one must first acknowledge that the process of managing data will incur costs. Researchers should plan to address these costs and the allocation of resources in the early planning phases of the project.

• Revisit data management plan throughout the project life cycle: The plan will be created at the conceptual stage of the project. It should be considered a living document and a road map for the project, and should be closely followed.

(From DataONE Best Practices)

Resources

• Data Carpentry Lessons
• DataONE Data Management Primer
• DataONE Best Practices
• Princeton University Library Research Data Services
• Princeton University Library Data Lifecycle Guide
• Princeton University Library Resources
• Stanford University Library Research Data Services
• Stanford University Library Data Management Plans
• Stanford University Library Tools and Resources
• Penn State: The Ethics of Data Management