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A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Institut d’Investigació en Intel ligència Artificial (IIIA), CSIC, 08193 Cerdanyola, Spain
Department de Matemàtiques, Universitat de Barcelona, 08007 Barcelona, Spain
Citizen Cyberlab, CUI, University of Geneva, CH-1227 Geneva, Switzerland
Authors to whom correspondence should be addressed.
Academic Editor: Snezhana Gocheva-Ilieva
Mathematics 2021, 9(8), 875;
Received: 26 February 2021 / Revised: 7 April 2021 / Accepted: 13 April 2021 / Published: 15 April 2021
(This article belongs to the Special Issue Statistical Data Modeling and Machine Learning with Applications)
Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019. View Full-Text
Keywords: data quality; citizen science; consensus models data quality; citizen science; consensus models
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MDPI and ACS Style

Cerquides, J.; Mülâyim, M.O.; Hernández-González, J.; Ravi Shankar, A.; Fernandez-Marquez, J.L. A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data. Mathematics 2021, 9, 875.

AMA Style

Cerquides J, Mülâyim MO, Hernández-González J, Ravi Shankar A, Fernandez-Marquez JL. A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data. Mathematics. 2021; 9(8):875.

Chicago/Turabian Style

Cerquides, Jesus, Mehmet O. Mülâyim, Jerónimo Hernández-González, Amudha Ravi Shankar, and Jose L. Fernandez-Marquez. 2021. "A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data" Mathematics 9, no. 8: 875.

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