Biodiversity Data Mobilization Course

A. Ability to identify the type of digital data that can be extracted from a source of biodiversity data (i.e. that can be published using the GBIF network)

Can identify only the most evident data types from common sources of biodiversity data (e.g. occurrences from natural history collection specimens). Shows little understanding of potential for online publishing using GBIF.

Can frequently identify correctly, at least one digital data type that can be extracted of common sources of data. Has difficulty identifying which ones can be currently published using GBIF.

Can always identify one (or more) types of digital data that can be extracted from common sources of data. Can identify which one of those types can be currently published using GBIF.

Can always identify one or more types of digital data that can be extracted from common and uncommon sources of data. Can identify which one of those types can be currently published using GBIF and which ones are under discussion. Can identify data cores and extensions used for publishing those data types.

B. Capacity to extract relevant information from a source of biodiversity data into simple data structures (e.g. spreadsheets) that follows international standards

Can only extract large pieces of obvious information (e.g. all geographic information as a single unit) which are evident in the data source. Shows little knowledge of current standards for recording biodiversity data.

Can retrieve several information items from the data source (but not all) and can disaggregate them into meaningful pieces. Shows some basic knowledge of the most common standards (e.g. DwC) and the most used data fields in those standards.

Can identify all valuable information in a data source, and extract the mandatory elements in a standard data structure (e.g. a spreadsheet based on Simple DwC). Can identify missing information and infer from existing information (e.g. derive a country name from a province).

Can identify all valuable information in a complex data source, and divide it into meaningful pieces which then translate directly into international standards. Can identify critical information missing in the source and infer it from the existing data or from additional information about the source (metadata).

C. Ability to understand and apply basic principles of data quality to the data capture process

Shows limited understanding of how applying simple data quality principles can have a large impact on the final product, preventing additional required cleaning afterwards.

Knows some of most generic principles of data quality (e.g. avoid misspellings) but shows limited knowledge on how to apply more specific principles to the data capture process.

Knows all the basic principles of data quality and how to apply these in simple ways to the data capture process. Uses formats consistently during the data capture process (e.g. in dates, country names). Documents all procedures and changes connected to data quality in a simple manner.

Shows good knowledge of all common principles of data quality and how to use them to improve the data capture process. Uses data formats consistently and can use gazetteers, reference lists, or software-specific features to improve quality from the original. Documents clearly all changes and decisions taken in connection to data quality.