The data lifecycle represents the stages through which research data passes, from initial creation to long-term preservation and sharing. Each stage is crucial for maintaining the integrity, accessibility, and reusability of the data. Here’s a breakdown of the typical data lifecycle stages:
Plan
Before starting your research, create a Data Management Plan (DMP). This plan outlines the types of data you will collect, how you will organize and manage it, what tools you’ll use, and how you will ensure ethical and legal compliance, especially regarding data sharing and security.
Collect or Capture
During this stage, gather or generate data through methods like surveys, experiments, simulations, or observations. Ensure data collection processes are well-documented and follow established protocols to guarantee accuracy and reproducibility.
Analyze
Analyze the collected data to draw conclusions, test hypotheses, or gain insights. Throughout the analysis, track any transformations or changes made to the data to ensure transparency and replicability of results.
Manage, Store, and Preserve
Organize, label, and securely store your data. Use appropriate file formats, backup procedures, and secure repositories to preserve data for future use. Long-term preservation ensures that data remains accessible, safe from loss, and ready for potential reuse.
Share and Publish
Publish your data in an appropriate repository, ensuring it is well-documented with metadata that makes it easy to find and understand. Data sharing promotes transparency and allows others to benefit from your work, contributing to the broader research community.
Discover, Reuse, and Cite
Your published data is now discoverable by other researchers who can reuse it in new studies, generating novel insights. Properly published data allows for proper citation, giving credit to the original creators and contributing to ongoing research impact.
Gaelen Pinnock, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
