Mapping sustainable development goals to citizen science projects'a comparative evaluation of automatic classifiers

Abstract

Traditional data sources provide insufficient knowledge for measuring the United Nations Sustainable Development Goals (SDGs). Data related to SDGs are sourced primarily from global databases maintained by international organizations, national statistical offices and other government agencies. Recent studies show the value of using data from Citizen Science (CS) for assessing the SDGs. There is an important online presence of CS programs, professional networks for CS and online communities of citizen scientists, leading to the generation of several CS platforms. In this context, the role of computational data science is key. This paper explores and exemplifies opportunities for combining web-data mining techniques and automatic classifiers to enhance the understanding of the inter-relation between CS and the SDGs. An analysis of different automatic classifiers is presented by comparing the results obtained from their application in a sample of 208 CS project descriptions. The results of this study indicate the benefits and limitations of these techniques (nCoder, ESA, OSDG and BERT), but also provides a discussion of the potential benefits of using data from CS projects to map the 17 SDGs.

This work has been partially funded by the EU project `CS Track' under the H2020 program (grant id: 87252), the Spanish projects PID2020-112584RBC33 and PID2023-146692OBC33 and the Ramón y Cajal programme (P. Santos) funded by the Ministerio de Ciencia, Innovación y Universidades (Spain).