The word 'data' can mean many things to many people, so defining context is important to conversations.
The NIH defines scientific data as:
This definition explicitly excludes laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects, such as laboratory specimens.
Key Takeaways:
Research Data Management (RDM) describes the best practices that make data easier to find and work with, both during a project as well as after the data is archived or shared with the wider research community. It encompasses activities like file organization, quality assurance, documentation, and storage.
The activities that support good RDM are often described with a lifecycle model to represent the important data management activities that occur before, during, and after the research ends:
Image source: Jisc Research Data Management Toolkit https://www.jisc.ac.uk/guides/rdm-toolkit
Why does the NIH require data management?
Data management increases the return on investment for research funding through both short-term and long-term benefits.
Short-term benefits of RDM include:
In the long-term, RDM improves the re-use value of data, making it an essential pre-requisite for meaningful data sharing. This also leads to other benefits, such as:
How do I comply with the data management requirements in the policy?
The data management information required by the NIH is described in more detail in the "Writing Your Data Management Plan" section of this site. Additional resources are provided below about the best practices underlying these requirements.