The University of California has created a website to help researchers consider how making small changes in how you manage your research data can make a big difference on the impact of your research. Take the time at the beginning of a project to think about how you will handle your data to optimize its reach and utility down the line. Visit Support Your Data at the start of your project to consider how you make your research last longer than the life of your project.
Two overarching representations of the research data management process are available:
Research Data Management is a continuum of practices. It continues throughout the course of a research project. You will likely jump around and move between phases in the lifecycle, but you should always start at the Plan & Design phase. During the Plan & Design phase, you will need to know:
Use a checklist to help plan and design your work:
Review the Support Your Data Project at University of California https://researchdata.org/ which presents a holistic look at data management best practices. The project provides a framework for thinking about data throughout the project lifecycle.
You may need to create a Data Management Plan during this phase. Additional guidance for creating a plan is available in this guide under the For Researchers tab.
Before launching a research project, design a model for capturing, storing, and organizing your data. Consider project
Design how you will store your data:
Store & Manage is a key component of the Data Lifecycle touching on every stage. Researchers will need to plan for:
Consider data storage requirements for the project.
Follow required retention and preservation requirements as established by your institution or funding agency. The Data Curation Network has developed extensive guidance on working with and keeping research data.
Find a repository for sharing and publishing:
Data publishing repositories should follow FAIR principles https://www.go-fair.org/fair-principles/
In general, raw data are considered facts and cannot be copyrighted. Community norms for data attribution and scholarly communication are often more successful in documenting origins of data than licensing restrictions when possible.
Data license considerations include the following: