For example, unstructured data will require a non-relational system versus a relational one, and larger datasets will require more compute power compared to smaller ones. The type and timing of the data will generally inform its storage and access to third-parties. how often it will be updated and over what period of time. It should also contain information around the timing of data-i.e. It will likely address the data source of any existing data or the approach that will be taken to create new data, such as an experiment. Data collection and access: This section of a DMP highlights how data will be collected, stored, and accessed from a data repository. Creating and abiding by pre-defined metadata standards throughout the data acquisition process will also ensure a more consistent collection and smoother integration process.ģ. Some of this information may also be held within the metadata, typically labeling data by its data sources and file formats. Data definitions: Data descriptions help end users and their audiences understand naming conventions and their correspondence with specific datasets. It should clearly outline the question that the team is attempting to answer with this dataset.Ģ. Statement of purpose: This explains why the team needs to acquire specific types of data over the course of the project. For example, if sensitive data is used within a project, is it appropriate to re-use that data for future projects? Depending on the sensitivity of that data, it may not be appropriate, or it may require additional user consent.Įach component of a data management plan focuses on a particular piece of information, we’ll delve more into each one.ġ.
#Project management template how to#
The data management plan also addresses how to manage that risk. A data management plan typically has five components:Įach of these focus areas enables research agencies and research funders (or perhaps your data management team) to assess the amount of risk associated with a given project.
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