1.4 Data levels and Best Practice Recommendations

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Data Levels

Modified from GLEON Best Practice Recommendations for Data Management and ODM2 core: Processing Levels

Level 0 – Raw data: unprocessed data and data products that have not undergone quality control. Depending on the data type and data transmission system, raw data may be available within seconds or minutes after real-time. Examples include real-time precipitation, streamflow, and water quality measurements

Level 0.1 – First pass QC: A first quality control pass has been performed to remove out of range and obviously erroneous values. These values are deleted from the record. E.g: Online Environment Canada streamflow data, laboratory data

Level 1Quality Controlled Data: Quality controlled data have passed quality assurance procedures such as 0.1 and 0.2. Data are reviewed by data managers, flagged and versioned. E.g. Data submitted to CanWIN

Level 1.5Advanced Quality Controlled Data: Advanced quality controlled data have undergone complete data provenance (i.e. harmonized). Metadata includes links to protocols and methods, sample collection details, incorporates CanWIN’s or another standardized vocabulary, and has analytical units standardized. Note: Standardized vocabulary and unit protocols are currently in development for CanWIN (as of April 12, 2018).

Level 2Derived Products: Derived products require scientific and technical interpretation and can include multiple collection types. E.g.: watershed average stream runoff derived from streamflow gages using an interpolation procedure.

Level 3 – Interpreted Products: These products require researcher (PI) driven analysis and interpretation and/or model-based interpretation using other data and/or strong prior assumptions. E.g.: watershed average stream runoff and flow using streamflow gauges and radarsat imagery

Level 4Knowledge Products: These products require researcher (PI) driven scientific interpretation and multidisciplinary data integration and include model-based interpretation using other data and/or strong prior assumptions. E.g.: watershed average nutrient runoff concentrations derived from the combination of streamflow gauges and nutrient values.

Data Processing Best Management Practices

  1. Get Level 0 (raw) data (e.g. downloaded instrument data ), Level 0.1 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 OR 
  4. Share Level 0, or 0.1 data through CanWIN. It will be archived and a dataset which had been quality controlled to CanWIN standards  (Level 1) will be produced
  5. If you produce additional results from the original dataset (level 0, 0.1 or 1.0), you can also add this to CanWIN under the same project as Levels 2,3 or 4 products. This helps showcase your work


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Last Updated On April 12, 2018