[MUSIC] At this point, you have a good understanding of what is required to effectively manage data as you move through the research life cycle. As you know, data management can seem quite the undertaking, when you consider all the aspects of data management best practice. But data management does not have to be overwhelming, if you have a data management plan. A data management plan or a DMP for short is a formal document that describes the data produced in the course of a research project. And outlines the data management strategies that will be implemented both during the active phase of the research project and after the project ends. If you have been involved in a research project supported by a major funding agency, you probably have a good idea of what should be included in a DMP. Many funding agencies, particularly federal agencies, now require grantees to submit a data management plan as part of the proposal package. If you think you or your clients may be applying for a grant from the National Science Foundation, the National Institutes of Health, the Alfred P Sloan Foundation, NASA, the Institute of Museum and Library Services. The National Endowment for the Humanities, the Gordon and Betty Moore Foundation, the Institute of Education Sciences and the Economic and Social Research Council, the Science and Technology Facilities Council, the Wellcome Trust, and while the list goes on you will need to know how to write a comprehensive DMP. Even if you won't be participating in funded research, writing a DMP will encourage you to think carefully about the data management needs of your research data and plan accordingly. Remember, like planning for your taxes, planning for data management will save you time and headaches in the long run. By the end of this lesson, you will be able to identify the components of a good DMP, understand the importance and purpose of DMPs, and be familiar with the DMP policies of several funding agencies. Also, you will add to tour toolkit some great resources and tools for data management planning. Let's go ahead and get started. Just as I mentioned, many funding agencies now require grantees to submit a data management plan as part of the proposal package submission. This gives assurances to the funding agency the project data will be accessible and usable over the long term. They want to see that you have established a strategy for managing your data throughout the research lifecycle. Making plans for how you will collect, document, organize, and preserve your data are all part of the data management strategy. This is important for funding agencies, for two primary reasons. The first reason is transparency and openness. It is an expectation that publicly funded research data will be discoverable, accessible, and reusable to the public. Since they underwrite federally funded grants, it is a government responsibility to ensure citizens can see where their tax dollars are spent and allow them to capitalize on the products of these grants. A data management plan that illustrates how data will be made discoverable, accessible and reusable supports this responsibility. The second reason is return on investment. Making data discoverable, accessible and reusable, maximizes the research potential of the data and provides greater returns on public investments and research. Well-managed data allows for verification or refinement of published research results, reduces the potential for scientific fraud, promotes new research through the use of existing data, provides resources for training new researchers and discourages unintentional redundancy in research. By planning for data management, these benefits are more likely to be realized. [MUSIC]