Each dataset in the Data Catalogue is assigned a curation level based on data-provider input. This system is key to determining the level of processing or quality control applied to a dataset, and helps users understand how the data can best be used. Here is a quick reference table identifying different levels of data published on this site.
| Level | Type | Description |
|---|---|---|
| 0 | Raw data | Unprocessed data/products that have not undergone quality control. Example: real-time precipitation, streamflow, and water quality measurements |
| 0.1 | User provided or historical data | Data provided to CanWIN by a user or is historical with unknown provenance and will not be quality controlled by CanWIN, hence quality of data is unknown, but will have metadata applied to the best of CanWIN's knowledge. |
| 1.0 | First pass QC | A first quality control pass has been performed to remove erroneous or out of range values and deleted from the record. Example: laboratory data provided to user. |
| 1.1 | Quality controlled data | Data that have passed quality assurance procedures (1.0) and further quality controls by provider before submission to CanWIN. Example: Idronaut data with upwelling data removed for only downwelling data to be shared. |
| 1.2 | CanWIN curated data | Data that has undergone initial quality control from provider and has been further curated by CanWIN Data Curator. Example: data cleaning script applied. |
| 1.5 | Advanced quality controlled data | Data undergone complete data provenance (i.e. standardized) in CanWIN. Metadata includes links to protocols, methods, sample collection details, and incorporates CanWIN's or another standardized vocabulary, and has analytical units standardized. |
| 1.6 | Combined data product | Data has through data cleaning process (1.5) and has additional data combined with it. Example: AVOS data combined with incubator data. Dataset then provides better context for user when combined pre-sharing through the site, but individual datasets may also be available. |
| 2 | Derived products | Derived products require scientific and technical interpretation and can include multiple data types. Example: watershed average stream runoff derived from stream-flow gauges using interpolation procedure. |
| 3 | Interpreted products | Products require researcher (PI) driven scientific interpretation and/or model-based interpretation using other data and/or strong prior assumptions. Example: watershed average stream runoff and flow using streamflow gauges and radarsat imagery. |
| 4 | Knowledge products | Products require researcher (PI) driven scientific interpretation and multidisciplinary data integration and include model-based interpretation using other data and/or strong prior assumptions. Example: watershed average nutrient runoff concentrations derived from the combination of stream-flow gauges and nutrient values. |