1.4 Data levels and Best Practice Recommendations

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Data Levels

Modified from GLEON Best Practice Recommendations for Data Management (http://gleon.org/data/best-practices)

Level 0 – raw data

Level 1 – any automated QA/QC for large, obvious errors (e.g. instrument recalibration or laboratory data)

Level 2 – human intervention QA/QC

Level 3 – modeled or otherwise gap filled (not necessary to publically archive since everyone models differently)

Level 4 – aggregated, summarized data (i.e. data product)

Data Processing Best Management Practices

  1. Get level 0 (raw) data (e.g. downloaded instrument data ) OR level 1 data (e.g. from lab) from platform (Figure 1)
  2. Store and archive level 0 or 1 data as raw format or ASCII file if possible. You can use your own hard drive and/or CanWIN’s GitLab, DataHub or Alfresco servers for this
  3. Process level 0 data to produce level 1 data if possible
  4. You now have the option to share level 1 data through CanWIN. It will get archived and a dataset with harmonized data (level 2 data) will be produced
  5. If you produce additional results from the original dataset (level 0, 1 or 2), you can also add this to CanWIN under the same project as level 4 data.

 

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Last Updated On September 05, 2017