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Best Practice: Describe method to create derived data products

BEST PRACTICE

Best Practices by Data Life Cycle




Describe method to create derived data products

Data Life Cycle stage(s): Analyze   Describe

When describing the process for creating derived data products, the following information should be included in the data documentation or the companion metadata file:

  • Description of primary input data and derived data
  • Why processing is required
  • Data processing steps and assumptions
    • Assumptions about primary input data
    • Additional input data requirements
    • Processing algorithm (e.g., volts to mol fraction, averaging)
    • Assumptions and limitations of algorithm
    • Describe how algorithm is applied (e.g., manually, using R, IDL)
  • How outcome of processing is evaluated
    • How problems are identified and rectified
    • Tools used to assess outcome
    • Conditions under which reprocessing is required
  • How uncertainty in processing is assessed
    • Provide a numeric estimate of uncertainty
  • How processing technique changes over time, if applicable

Additional Information

Bourque, Linda B., Clark, Virginia A. Processing Data: The Survey Example (Quantitative Applications in the Social Sciences), Sage Publications, Inc. (December 14, 2008), ISBN 08056781901

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Cite this best practice:

DataONE Best Practices Working Group, DataONE  (July 01, 2010) "Best Practice: Describe method to create derived data products". Accessed through the Data Management Skillbuilding Hub at https://dataoneorg.github.io/Education/bestpractices/describe-method-to on May 24, 2019


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