RDMkit: A research data management toolkit for life sciences

Area of Science:

• Life Sciences
• Data Science
• Scientific Research

Background:

• Data-driven research necessitates effective research data management (RDM).
• Implementing RDM presents challenges due to generic guidelines and overwhelming disciplinary practices.
• Maintaining and disseminating RDM guidelines is difficult.

Purpose of the Study:

• To introduce the RDMkit as an open, community-led resource for RDM in life sciences.
• To provide accessible knowledge, tools, training, and resources for RDM.

Main Methods:

• The RDMkit aggregates best-practice guidelines for common RDM tasks.
• It offers domain-specific data management solutions for life sciences.
• Tool assemblies are provided to support the research data life cycle.

Main Results:

• The RDMkit serves as a gateway to a wealth of RDM information.
• It delivers practical guidelines and domain-specific solutions.
• The resource is built on an open infrastructure for guideline creation.

Conclusions:

• The RDMkit simplifies RDM implementation for researchers and data stewards.
• It promotes good scientific practice through accessible and adaptable RDM resources.
• Organizations can utilize the RDMkit blueprint to develop their own guidelines.