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Data Management @ ODU

What is Data Management?

Data Management, sometimes called Research Data Management or RDM, is the process of gathering, processing, storing, preserving, and disseminating research data.

Managing your data will help make your research process more efficient. Other benefits:

  • You will be compliant with funder requirements
  • Your data will be more secure
  • Your research findings will be transparent
  • Collaboration and data sharing will be easier
  • Your data will be preserved so that others can use it

In 2013, the federal Office of Science and Technology Policy (OSTP) issued a memorandum directing that the results of publicly funded research (including data) be made publicly available. For most agencies, this means that grant applications must include a plan for how data will be managed during and after the proposed project.

Data Management Essentials

From the University of Wisconsin-Madison Research Data Services

There are six key recommendations for managing your data/digital materials to ensure their longevity and usefulness:

  1. store and back them up
  2. keep data/digital materials in sustainable formats
  3. include metadata to preserve contextual information about who collected/created it, the date, instrument settings, etc.
  4. organize and structure them using file naming/versioning conventions, ontologies/vocabularies, and/or databases
  5. keep them secure and implement procedures for keeping sensitive data private
  6. include explanations about how data may be re-used and how the source of the data should be acknowledged

From the DataOne Best Practices Primer

data life cycle

The 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 into a 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 or repository)
  • 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

See also:  Digital Curation Centre: Curation Lifecycle Model (CC-BY 2.5 Scotland)