Abstract
Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or “pillars”. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as “two sides of the same coin”. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data product for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g., error propagation, paving the route to improvements in the approach.
Highlights
Robust and fit-for-purpose evidence is at the heart of environmental policy and decision making in the UK government, as shown by the Department for Environment, Food and Rural Affairs (DEFRA)in their evidence strategy [1]
The use of Earth Observation (EO) to explore the extent of Invasive Non-Native Species (INNS) in Europe ( referred to as Invasive Alien Species (IAS) in combination with Citizen Science (CS) based data collection, allow projects to provide validation data to input into distribution models or habitat maps, which is recognized as an exciting new research area [11,33]
With the aim of providing relevant metadata for CS data along with the provenance of the metadata on data quality, i.e., the metaquality encapsulated in the Quality Assurance (QA) workflow, the results shown in the preceding sections are promising
Summary
Information of quality is needed for CS data, to give confidence in their re-use, and provide a rich evidence base for policymaking. The use of EO to explore the extent of INNS in Europe ( referred to as Invasive Alien Species (IAS) in combination with CS based data collection, allow projects to provide validation data to input into distribution models or habitat maps, which is recognized as an exciting new research area [11,33]. The process of “verification”, such as that used by the NBN: National Biological Network, UK, is a common practice, and is primarily used as a definitive way of assessing the data quality This involves manual verification by an expert of each observation, e.g., verifying the content of a photo that has been given as evidence of an invasive species occurrence. From ISO19157, metaquality describing wasCS obtained and eventually its variation across theinformation dataset. (at dataset level) is describing how the quality was obtained and eventually its variation across the dataset
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