This guide is intended to provide a brief overview of and introduction to the concepts behind data management and the basics for creating a Data Management Plan (DMP). A data management plan is a written document that describes the data you expect to acquire or generate during the course of a research project, how you will manage, describe, analyze, and store those data, and what mechanisms you will use at the end of your project to share and preserve your data.
Many funding agencies such as the National Institutes of Health (NIH) and the National Science Foundation (NSF) require that a DMP be included in the application as part of the funding process. But even without these requirements, having a good data management plan can be beneficial in many other ways. It facilitates the dissemination and sharing of research data and ensures that the data will be preserved for future use by other researchers. The information in this guide will provide an overview of the main concepts of data management and will include links to many useful sites.
Increase your research impact - Making your data available to other researchers can impact discovery and relevance of your research.
Save time - Planning ahead for your data management needs will save you time and resources.
Preserve your data - Depositing your data in a repository safeguards your investment of time and resources while preserving your research contribution for you and others to use.
Maintain data integrity - Managing and documenting your data throughout its life cycle will allow you and others to understand and use your data in the future.
Meet grant requirements - Many funding agencies now require that researchers deposit data collected as part of a research project.
Promote new discoveries - Sharing your data with other researchers can lead to new and unanticipated discoveries and provide research material for those with little or no funding.
Support open access - Be a catalyst for research and discovery. Show your support for open access by sharing your data.
Data Management can be generally considered as any activity involving data outside of actually using the data. Data management is best defined as any or all of the following examples:
* Organizing data into directories/folders and using meaningful filenames.
* Keeping backups of data in case you accidentally delete or lose data.
* Storing final state data in an archive.
* Making data available to others via an archive or website.
* Ensuring security of confidential data.
* Collaboratively creating and using data with other researchers.
* Synchronizing data between desktop, laptop, USB key, cloud storage, etc.
* Maintaining a bibliography and electronic copies of relevant literature.
Data management involves organizing, protecting and distributing the data. Typically, people only do data management when it is needed and therefore tend to use the most obvious methods. Using more advanced and automated methods will reduce the amount of time spent managing data.