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A Method for Conveying Confidence in iNaturalist Observations: A Case Study Using Non-Native Marine Species. | LitMetric

AI Article Synopsis

  • Citizen science initiatives, while cost-effective and capable of gathering vast amounts of data, face challenges regarding the quality and reliability of the information collected, particularly due to unverified observations.
  • A study developed a confidence scoring protocol for observations from iNaturalist, focusing on marine species in South Africa, revealing a strong relationship between observation accuracy and confidence scores.
  • The research underscores the need for expert verification of citizen science data, suggesting that the new confidence score can streamline the verification process and help minimize biases in data assessment.

Article Abstract

Concerns and limitations relating to data quality, reliability and accuracy hamper the use of citizen science initiatives in research and conservation. Valued for their cost-effective and large data acquisition potential, citizen science platforms such as iNaturalist have been highlighted as beneficial tools to supplement monitoring using traditional data sources. However, intrinsic uncertainties in unverified observations stem from the nature of species being identified, the quality of uploaded media and georeferencing; these factors can limit the value of the data as they can result in inaccurate records. Verification of data prior to use is critical. This process can, however, be laborious and time-consuming, with bias associated with the individual responsible for the task. To address this challenge this study developed a protocol for assigning confidence in iNaturalist observations, using marine alien and cryptogenic species observations from South Africa as a case study. A positive relationship was found between the accuracy of observations and confidence score. The inherent data quality assessment in iNaturalist, termed quality grade, was found to be an inadequate proxy for accuracy. The results of this study highlight the importance of the expert verification phase when using citizen science data. The confidence score facilitates a streamlined approach to the verification process by reducing the time taken to validate records, while assessing the three levels of uncertainty within observations and reducing researcher bias. It is recommended that this confidence score be used as an essential tool when using citizen science derived data.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461752PMC
http://dx.doi.org/10.1002/ece3.70376DOI Listing

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