Citizen Science (CS) is a longstanding practice, dating back thousands of years to ancient China where residents tracked locust outbreaks. In modern times, the Christmas Bird Count, initiated in 1900 by ornithologist Frank Chapman, replaced the "side hunt" with citizens counting birds, establishing a lasting tradition of recording bird abundance and distribution.
This project was presented in January 1989 in an MIT Technology Review article titled 'Lab for the Environment', which highlighted Audubon's use of volunteers in a 'citizen science' capacity. Since then, citizen science gained attention in various research fields, leading to the formulation of different definitions. Ornithologist Rick Bonney emphasized CS as a method for collecting scientific data through public collaboration with professionals. Sociologist Alan Irwin proposed an alternative definition in 1995, focusing on the expertise held by individuals traditionally deemed 'ignorant,' considering citizen science as an intrinsic skill or quality. Recently, citizen science activities have been focused on the role of communities in the scientific process and how their involvement can enrich science and bring impact to society.
Collaborating with the public or volunteers from diverse backgrounds and interests can spark innovation and shed light on previously ignored directions. In this light, citizen science can act as an inclusive tool for social good, benefitting from collectively acting together for a common goal. Citizen science can be applied in several areas, including education, sustainability, environment and climate change, and healthcare.
Technology plays a pivotal role in Citizen Science (CS) projects. Solutions should be designed and developed to facilitate long-term participation, and data collection and data accuracy – fundamental for the success of a citizen science project. Solutions can include mobile apps, web platforms, or hardware, developed for a specific need or for general purpose. Existing solutions often exploit smartphone built-in sensors (e.g., camera, microphone, GPS), ah-doc sensors (e.g., air quality low-cost stations), AI for classification, and others.
This special track aims to provide an alternative forum for researchers and practitioners to discuss current applications and future directions related to citizen science for social good, with the aim to help the creation of a community around this topic. We encourage authors to explicitly define social good in the context they study or engage with citizen science, detailing how specific CS activities may benefit certain stakeholders. We invite contributions from different subject areas to facilitate multidisciplinary discussions across academicians, practitioners, and policymakers, leading to high-impact and transformative research.
In the broad area of Citizen Science for Social Good, topics of interest in the scope of GoodIT are:
Case studies of Citizen science applications for social good
Citizens’ engagement and motivation
Innovative solutions supporting CS and social good (e.g., Virtual Reality, Augmented reality, smart sensors)
Data collection, accuracy, and trustworthiness
Ethics and privacy issues
Artificial Intelligence (AI) for Citizen Science
Gamification and Game Thinking
Data visualization and data literacy
Crowdsourcing and crowdsensing