Data Categories and Levels

CanWIN Templates

Category Definition
Facility A site or organization from which multiple research activities are conducted. They may include companies, governmental or other organizations, or infrastructure such as a building, vessel or lab. Example, Fisheries and Oceans Canada (DFO), R/V William Kennedy, or Churchill Marine Observatory.
Platform A container or structure from which instrumentation is deployed. Example:  mooring or meteorological station.
Project A program occurs over 1 or more years and may or may not be made up of multiple smaller programs that inform the main "program" goal. Example: BaySys.
Sub-project Smaller projects that form part of the a larger program. Uses the Project template.
Campaign Occurs over a specific time period over which data is collected, either from a base camp or a facility.
Deployment The act of placing a platform or instrument in an area for a period of time longer than 1 day. Deployments of shorter than 1 day are not recorded via these templates due to logistical constraints.
Instrument Any device used for making measurements, alone or in conjunction with one or more supplementary devices - as defined by the Joint Committee for Guides in Metroloy (JCGM).
Publication Any type of document including grey literature, unpublished reports, fields notes, preprints, etc.

Additional Categories

Category Definition
Data File A single file from an instrument or analysis.
Dataset Consists of either a single data file or be a combination of data files from a single instrument. Example: Seabird.
Collections Made up of multiple datasets that may consist of the same or different types of data and are grouped together by a category or platform. Example: field campaign or field camp (categories). Example: mooring (platform).
Theme Grouping of ideas which share a related topic.

CanWIN Data Levels

Every Dataset in CanWIN is assigned a curation level based on data provider input. This system is key to determining what type of data you are providing or looking for and helps users understand how your data can best be used by them. 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 have 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 unites 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.