Data Management

"Data Management" is the compilation of many small practices you can take to make data easier to find and understand, less likely to be lost, and  more likely to be useful later. These actions involve:

• Planning data management
• Documenting data
• Organizing data
• Improving analysis procedures
• Securing sensitive data properly
• Having adequate storage and backups during
• Taking care of data after project
• Sharing data effectively
• Finding data for reuse in a new project

This doesn't mean you MUST do everything. But data management can be involved in every stage of your research data lifecycle, so even just a few actions you integrate in your research workflow will make your research healthier.

Reasons to manage your data:

1. You'll be protecting your data from loss
2. You can find your data when you need it
3. You'll be protecting yourself with privacy risk management--for instance, human subjects data won't get accidentally disclosed to those not permitted to access it
4. You will increase reusability of data for future projects or for verifying your own data
5. You're making it easier to share data (which is sometimes required to do anyway)