A consistent approach to assess and manage Data Quality (DQ) is currently critical for biodiversity data users. However, achieving this goal has been particularly difficult because of the idiosyncrasies inherent to the concept of quality.
DQ researches show that DQ assessment and management are founded on, mostly, DQ dimensions (or their opposite, e.i. DQ problems), which are used to define the meaning DQ in a given context and measure it. With an appropriate DQ measurement, data users or automated systems are able to select a subset of data that have a suitable level of quality for a determined purpose, and data managers can direct effort on the improvement of the subset of data that are not fit for use.
A conceptual framework has been developed to provide a context for describing biodiversity data quality, allowing users to make an informed assessment of DQ and its subsequent fitness for their use as well as allowing institutional assessment of DQ for management and improvement.
This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report, which can formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among data user communities. With this framework, we establish a common ground for the collaborative development of needs and solutions for DQ assessment and management based on data fitness for use principles.
In future work, we will use the presented framework to engage the biodiversity informatics community to formalize and share DQ profiles related to a number of data usages.
