Below are some frequently asked questions related to data management plans, terminology, data sharing, federal requirements, and more.
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A Data Management Plan (DMP) is a written document that outlines how research data will be managed throughout the research process. They should include include information about what data is acquired, how it is stored and organized, who may access it, how it is documented, security and safety measures, software and device information, and post-research/long-term plans for sharing and preserving. A DMP is an effective planning device and can also be used to onboard new students and collaborators.
Data Management Plans submitted as part of a funding proposal are typically limited to two-pages so the contents are more of an outline or 'sketch' than a procedural document.
The short answer is "no", there are usually limits on which data must be preserved and shared. You should prioritize keeping and sharing data that:
If you're unsure what to keep you can contact the DataShare Team for help at email@example.com.
This guide uses the terms data, research data, and scientific data interchangeably as they all refer to data that was collected or generated for the purpose of research.
The Federal government defines ‘research data’ as:
The National Institutes of Health (NIH) use the term “scientific data” and defines it as:
The two definitions share the same focus (validating research findings) but do not set firm limits. NIH has provided clarifying language that makes it clear that relationship to a publication is not the sole determining factor under their policy and this understanding also aligns with the federal definition:
A simple definition of metadata is that it is "data about data" (how meta!). A better definition is that metadata is descriptive structured information that describes data. A simple example of metadata is to think of how you would describe a book you liked to someone else: title, subject matter, length, author, date of publication, etc. -- these are pieces of (meta)data that describe the book (data).
Data has no value unless there is also information about what it is, where it came from, and who made it. When done well, metadata fills in these gaps and makes data discoverable, accessible, and understandable.
Data repositories are devoted to sharing and keeping data accessible, safe, and secure. They use special software, metadata, workflows, and networks to meet these goals. Data repositories also help guarantee authenticity by providing control mechanisms and change logs. They are the best choice for research data sharing, distribution, and preservation.
Data repositories often have limits and restrictions governing which data they accept. Most have rules covering data formats and size limits, and require that data be documented. Some accept data from any research area, while others will only accept research from specific domains (such as "biology" or "social science"). The latter are known as disciplinary data repositories. Another type of specialized repository is the institutional data repository which focuses on collecting the outputs of select group, such as a university or federal agency.
The Data Sharing portion of the guide provides more information and resources for locating data repositories.