Managing Your Research Data

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Why is data management important?

• Ensures scientific integrity: Well-managed data are more reliable, traceable, and reproducible.

• Supports collaboration: Clear organization and documentation make it easier for others to understand and reuse your data.

• Meets funder requirements: Many funding agencies now require data management plans and open access to research data.

• Prepares for publication and sharing: Structured and well-described data are easier to publish and disseminate.

What you’ll find in this section:

• Data Cleaning
  Learn how to identify and correct errors or inconsistencies, and in your datasets.

• Document Your Data
  Discover how to create clear documentation (data dictionaries, metadata, README files) to make your data understandable and reusable.

• Organize Your Data – File Naming [link]
  Adopt naming conventions and folder structures that support clarity, consistency, and long-term access.

• Quality Standards [link]
  Understand the quality criteria and best practices relevant to your discipline or data type.

• Type of Data [link]
  Explore different data types (quantitative, qualitative, multimedia, etc.) and their implications for management and sharing.

• More Resources for Data Management [link]
  Access tools, guides, and training materials to deepen your knowledge and improve your data management practices.

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