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Research Data Management
It is becoming more and more common for granting institutions and publishers to require that investigators make a plan for how they will collect, describe, store, and protect data, as well as for how they will share the data after publication. Having complete, well-described data can improve accuracy and spur scientific discovery when the data is shared with the broader community. This guide shares some best practices and tools for making data management plans, as well as for finding and sharing data in repositories.
Why Use Data Management Best Practices?
- Avoid data loss
- Help ensure that data is complete and accurate
- Comply with funder and publisher mandates
- Promote re-use, discovery, and citation
- Preserve for future access
Research Data Management Course
DataONE Data Life Cycle
The DataONE data life cycle has eight components:
- Plan: description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime
- Collect: observations are made either by hand or with sensors or other instruments and the data are placed a into digital form
- Assure: the quality of the data are assured through checks and inspections
- Describe: data are accurately and thoroughly described using the appropriate metadata standards
- Preserve: data are submitted to an appropriate long-term archive (i.e. data center)
- Discover: potentially useful data are located and obtained, along with the relevant information about the data (metadata)
- Integrate: data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed
- Analyze: data are analyzed
DataONE. Data Life Cycle. https://www.dataone.org/data-life-cycle.
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