The characteristics of citizen-science projects aimed at tackling COVID-19 indexed in SciStarter
| Citizen science project | Main purpose | Approach | Mechanisms | Outcomes |
|---|---|---|---|---|
| CoronaReport | Engaging citizen scientists in a distributed effort to collect data and evidence on the impact of Covid-19 on daily life | Distributed intelligence: lay people are involved in data collection, autonomously participating in systematization and reporting of relevant information. Lay people have the access to peers' information | The engagement of lay people is mediated by a mobile app, which enacts exchanges and communication among citizen scientists and between citizen scientists and expert scientists. Trust and motivation are embedded in the social exchanges among the peers | Large collection of data about the impacts of Covid-19 on individual and social life. Enhancement of lay people's awareness on the implications of Covid-19 on private life and social life throughout the world |
| Covid-19 Citizen Science Project | Collecting primary data about lay people's health conditions and exposure to risks associated with the Covid-19 pandemic | Crowdsourcing: citizen scientists primarily perform as self-reporter of personal data about individual health conditions and risk factors. A limited involvement in the co-design of research activities is experienced | The Eureka web-based platform and mobile app allow expert scientists to standardize and formalize the involvement of lay people in the process of data collection. Although the project intends to retain citizen scientists, there is only a limited use of soft mechanisms | Construction of a worldwide dataset to keep track of the evolution of the pandemic and get evidence about relevant risk factors. Establishment of a community of lay people interested to support expert scientists in their research activities |
| COVID Near You/Outbreaks Near Me | Enabling ley people to self-report their health conditions, in order to enhance the timeliness of Covid-19 and flu outbreaks' tracking | Crowdsourcing: citizen scientists are not involved in co-designing or co-delivering research activities in partnership with expert scientists. Rather, they primarily act as data contributors | The project web-site represents the key mechanism to recruit citizen scientists and to enable them to self-report their data. Although information and educational materials is included in the web-site, soft mechanisms are not exploited to empower/engage citizen scientists in advanced research activities | Timely and distributed reporting of Covid-19 and flu outbreaks. Visualization of Covid-19 cases to track the spread of the pandemic. Enhancement of public awareness about Covid-19 and flu facts |
| Covid Twitter Analysis | Involving citizen scientists in classifying large amount of data related to self-reported Covid-19 symptoms. Engaging citizen scientists in predicting future outbreaks of the pandemic | Participatory science: lay people perform as crowdsources, supporting expert scientists in the analysis of big data, and as value co-creators, having the opportunity to draw on crawled data to design innovative models and tools to predict the spread of the pandemic | The web-based platform is conceived of as the virtual space hosting the interaction among expert scientists and citizen scientists. Also, it stores data and materials to empower lay people and engage them in value co-creation. Soft mechanisms based on trust building and motivation are exploited to sustain the durable involvement of lay people in the project | Nimbler and timely analysis of tweets concerning Covid-19 symptoms to forecast future outspread of the pandemic. Lay people empowerment and engagement in value co-creation activities aimed at enhancing the systemic ability to tackle the Covid-19 pandemic |
| Covid Watcher | Engaging lay people in providing first hand data about Covid-19 symptoms, medical needs, resource needs, and behaviors during the pandemic to inform policy and decision making activities | Crowdsourcing: citizen scientists are not involved in value co-creation. They perform are self-reporters of personal data, providing policy makers and decision makers with fresh information to take timely decision aimed at tackling the pandemic | The project predominantly relies on hard mechanisms, consisting of a web-based platform and a mobile app. Since the project does not involve the participation of citizen scientists in accomplishing research tasks, soft mechanisms are virtually non-existent | Construction of an updated and comprehensive dataset about individual and collective medical and resource needs during the pandemic to inform policy making. Collection of reliable information about behaviors during the pandemic to tackle the spread of Covid-19 at the local level |
| Respiratory Health Study | Inviting lay people to collect data about individual behaviors and health conditions in order to build a large source of information to inspire empirical scientific research | Crowdsourcing: lay people do not collaborate with expert scientists to accomplish research tasks. They are asked to take part in surveys and personal data information sharing to provide expert scientists with first-hand information to advance current scientific knowledge about Covid-19 | The project is based on a mobile app, which enacts a bridge allowing lay people to share personal information with expert scientists. Few soft mechanisms are used to increase the engagement of citizen scientists in the project, including the assignment of control over contributed personal data to lay people | Collection of large amount of data about individual health conditions and risk factors. Involvement of lay people in data collection as representatives of the community in medical research. Empowerment of people and engagement in health promotion and risk prevention activities |
| World Community Grid | Lay people are invited to donate the spare computing power of their devices, but they do not participate in accomplishing specific research tasks and/or activities | Crowdsourcing: lay people donate their spare computing power, which is exploited to enact a distributed computing process enabling expert scientists to rely on a greater computing power to perform their scientific research | Soft mechanisms are completely absent. The unique mechanism used in the web-based interface allowing lay people to donate their spare computing power. Lay people do not have the possibility to actively participate in scientific research and to exchange data and/or information with expert scientists | Enactment of distributed computing by gathering the spare computing power donated by lay people |
| Citizen science project | Main purpose | Approach | Mechanisms | Outcomes |
|---|---|---|---|---|
| CoronaReport | Engaging citizen scientists in a distributed effort to collect data and evidence on the impact of Covid-19 on daily life | Distributed intelligence: lay people are involved in data collection, autonomously participating in systematization and reporting of relevant information. Lay people have the access to peers' information | The engagement of lay people is mediated by a mobile app, which enacts exchanges and communication among citizen scientists and between citizen scientists and expert scientists. Trust and motivation are embedded in the social exchanges among the peers | Large collection of data about the impacts of Covid-19 on individual and social life. Enhancement of lay people's awareness on the implications of Covid-19 on private life and social life throughout the world |
| Covid-19 Citizen Science Project | Collecting primary data about lay people's health conditions and exposure to risks associated with the Covid-19 pandemic | Crowdsourcing: citizen scientists primarily perform as self-reporter of personal data about individual health conditions and risk factors. A limited involvement in the co-design of research activities is experienced | The Eureka web-based platform and mobile app allow expert scientists to standardize and formalize the involvement of lay people in the process of data collection. Although the project intends to retain citizen scientists, there is only a limited use of soft mechanisms | Construction of a worldwide dataset to keep track of the evolution of the pandemic and get evidence about relevant risk factors. Establishment of a community of lay people interested to support expert scientists in their research activities |
| COVID Near You/Outbreaks Near Me | Enabling ley people to self-report their health conditions, in order to enhance the timeliness of Covid-19 and flu outbreaks' tracking | Crowdsourcing: citizen scientists are not involved in co-designing or co-delivering research activities in partnership with expert scientists. Rather, they primarily act as data contributors | The project web-site represents the key mechanism to recruit citizen scientists and to enable them to self-report their data. Although information and educational materials is included in the web-site, soft mechanisms are not exploited to empower/engage citizen scientists in advanced research activities | Timely and distributed reporting of Covid-19 and flu outbreaks. Visualization of Covid-19 cases to track the spread of the pandemic. Enhancement of public awareness about Covid-19 and flu facts |
| Covid Twitter Analysis | Involving citizen scientists in classifying large amount of data related to self-reported Covid-19 symptoms. Engaging citizen scientists in predicting future outbreaks of the pandemic | Participatory science: lay people perform as crowdsources, supporting expert scientists in the analysis of big data, and as value co-creators, having the opportunity to draw on crawled data to design innovative models and tools to predict the spread of the pandemic | The web-based platform is conceived of as the virtual space hosting the interaction among expert scientists and citizen scientists. Also, it stores data and materials to empower lay people and engage them in value co-creation. Soft mechanisms based on trust building and motivation are exploited to sustain the durable involvement of lay people in the project | Nimbler and timely analysis of tweets concerning Covid-19 symptoms to forecast future outspread of the pandemic. Lay people empowerment and engagement in value co-creation activities aimed at enhancing the systemic ability to tackle the Covid-19 pandemic |
| Covid Watcher | Engaging lay people in providing first hand data about Covid-19 symptoms, medical needs, resource needs, and behaviors during the pandemic to inform policy and decision making activities | Crowdsourcing: citizen scientists are not involved in value co-creation. They perform are self-reporters of personal data, providing policy makers and decision makers with fresh information to take timely decision aimed at tackling the pandemic | The project predominantly relies on hard mechanisms, consisting of a web-based platform and a mobile app. Since the project does not involve the participation of citizen scientists in accomplishing research tasks, soft mechanisms are virtually non-existent | Construction of an updated and comprehensive dataset about individual and collective medical and resource needs during the pandemic to inform policy making. Collection of reliable information about behaviors during the pandemic to tackle the spread of Covid-19 at the local level |
| Respiratory Health Study | Inviting lay people to collect data about individual behaviors and health conditions in order to build a large source of information to inspire empirical scientific research | Crowdsourcing: lay people do not collaborate with expert scientists to accomplish research tasks. They are asked to take part in surveys and personal data information sharing to provide expert scientists with first-hand information to advance current scientific knowledge about Covid-19 | The project is based on a mobile app, which enacts a bridge allowing lay people to share personal information with expert scientists. Few soft mechanisms are used to increase the engagement of citizen scientists in the project, including the assignment of control over contributed personal data to lay people | Collection of large amount of data about individual health conditions and risk factors. Involvement of lay people in data collection as representatives of the community in medical research. Empowerment of people and engagement in health promotion and risk prevention activities |
| World Community Grid | Lay people are invited to donate the spare computing power of their devices, but they do not participate in accomplishing specific research tasks and/or activities | Crowdsourcing: lay people donate their spare computing power, which is exploited to enact a distributed computing process enabling expert scientists to rely on a greater computing power to perform their scientific research | Soft mechanisms are completely absent. The unique mechanism used in the web-based interface allowing lay people to donate their spare computing power. Lay people do not have the possibility to actively participate in scientific research and to exchange data and/or information with expert scientists | Enactment of distributed computing by gathering the spare computing power donated by lay people |