What is research data management?
  • What is research data management?

What is research data management?

What is research data management?

Research Data Management (RDM), or data management for research, involves the planning, organization, storage, documentation, archiving and sharing of research data throughout the lifecycle of a research project. It refers to the practices and processes applied to manage research data effectively and efficiently so that it is usable, sustainable and accessible.

RDM aims to support researchers in preserving, managing and sharing their data according to international standards and best practices. It focuses on ensuring the quality, integrity, security and availability of research data throughout the entire research process, from data collection to analysis, publication and long-term storage.

Some key aspects of Research Data Management include:

  1. Data planning: Creating a data management plan (DMP) at the start of the research project to describe how the data will be collected, stored, secured and shared.

  2. Data organization: Organizing the data according to a structured format, with clear names and descriptions, so that it can be easily identified and understood.

  3. Data Verification and Integrity: Implementing measures to ensure the accuracy, validity and reliability of the data, such as validation procedures, double checking and version control.

  4. Data security and privacy: Taking steps to protect the confidentiality, integrity and availability of research data, including managing access rights and anonymizing sensitive data.

  5. Data storage and archiving: Choosing appropriate storage infrastructure and applying good archiving practices to ensure the sustainable storage and long-term accessibility of the data.

  6. Data sharing and reuse: Promoting the sharing of research data with other scientists, either through public repositories or controlled access, to increase the transparency, reproducibility and reusability of research.

RDM is becoming increasingly important in the research community as it contributes to more transparent and efficient research practice, promotes collaboration and knowledge sharing, and meets the requirements of grantors and publishers that require data management.