Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains.
Mapping citizen science contributions to the UN sustainable development goals
The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.
How can citizen science reduce psychological distance to science? Insights from three projects in contested environmental contexts
Citizen science (CS) is a participatory mode of knowledge production, enabling non-scientific actors to contribute to and sometimes contest scientific agendas and interpretations, making it a way to bridge science and society. This paper examines how that potential unfolds by analysing the individual perspectives of citizen scientists through the lens of Psychological Distance to Science (PSYDISC). Drawing on three case studies of contested environmental CS, we identify which contextual aspects of CS shape citizen scientists' experiences of social, spatial, temporal, or hypothetical distance to relevant science, and how these experiences may relate to trust. Our findings underscore the role of science communication as both a channel for dissemination, and as a constitutive element of participatory research; crucial for reducing psychological distance and enabling socially robust knowledge production, especially in contested, policy-relevant science settings.
Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop Report
This report is an outcome of a Computing Community Consortium (CCC) visioning workshop on Grand Challenges for the Convergence of Computational and Citizen Science Research conducted on April 8-9, 2025, in Washington, D.C. as well as through several precursor virtual input-gathering sessions. These events brought together experts across relevant disciplines to develop a research agenda that brings to fruition the above vision on how humans and machines may team up to solve some of the world's most pressing scientific problems. Citizen science delivers measurable economic and national value. Public participation in scientific research generates millions of dollars in volunteer labor value, extends government agency capacity, and directly supports federal priorities in areas such as disaster management, public health, water, energy, workforce development, and many more. At the same time, 21st-century scientific infrastructure requirements for citizen science (from hardware and cyberinfrastructure to data and computational frameworks) mirror those for computational science more generally. The distributed, collaborative, long-term, and contextual nature of citizen science makes it a demanding real-world use case for a novel robust research infrastructure that accounts for security, privacy, resource adaptability, and transparency. In this report, we outline the key findings, future research directions, and recommendations that emerged from the April 2025 CCC Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop.
Practical recommendations from a multi-perspective needs and challenges assessment of citizen science games
Citizen science games are an increasingly popular form of citizen science, in which volunteer participants engage in scientific research while playing a game. Their success depends on a diverse set of stakeholders working together–scientists, volunteers, and game developers. Yet the potential needs of these stakeholder groups and their possible tensions are poorly understood. To identify these needs and possible tensions, we conducted a qualitative data analysis of two years of ethnographic research and 57 interviews with stakeholders from 10 citizen science games, following a combination of grounded theory and reflexive thematic analysis. We identify individual stakeholder needs as well as important barriers to citizen science game success. These include the ambiguous allocation of developer roles, limited resources and funding dependencies, the need for a citizen science game community, and science–game tensions. We derive recommendations for addressing these barriers.
A Polycentric Governance Lens on Data Infrastructures
Funding policies for data infrastructure promote open data sharing to drive positive social impact. However, concerns regarding the long-term management of data within and across distributed infrastructures can hinder data sharing. We draw upon the concept of polycentric governance to demonstrate how collaborative practices of data curation, in preparing and maintaining data for (future) sharing, provide a solid foundation for understanding data governance within data infrastructures. Based on a qualitative case study of a distributed ecological network, we investigate the conditions under which data are managed as a shared resource by local actors to ensure the long-term (re)usability of data. We contribute to CSCW by conceptualising data curation as a complex form of governance practice with multiple centres of decision-making, each of which operates with some degree of autonomy in data infrastructures. A polycentric governance lens on data infrastructures advances the CSCW conception of data curation as a collective governance practice that can cultivate a data democracy culture within and across organisations, empower individuals to be accountable for their data, and foster a mindset shift toward decentralised data governance.
iNaturalist accelerates biodiversity research
Abstract. Participatory citizen science is expanding, with iNaturalist emerging as one of the most widely used platforms globally. However, its application
An epistemology for democratic citizen science
Abstract. More than ever, humanity relies on robust scientific knowledge of the world and our place within it. Unfortunately, our contemporary view of scie
A Data Utopia for Science-of-Science
Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…
Assessing nature's contributions to people
Recognizing culture, and diverse sources of knowledge, can improve assessments , A major challenge today and into the future is to maintain or enhance beneficial contributions of nature to a good quality of life for all people. This is among the key motivations of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), a joint global effort by governments, academia, and civil society to assess and promote knowledge of Earth's biodiversity and ecosystems and their contribution to human societies in order to inform policy formulation. One of the more recent key elements of the IPBES conceptual framework ( 1 ) is the notion of nature's contributions to people (NCP), which builds on the ecosystem service concept popularized by the Millennium Ecosystem Assessment (MA) ( 2 ). But as we detail below, NCP as defined and put into practice in IPBES differs from earlier work in several important ways. First, the NCP approach recognizes the central and pervasive role that culture plays in defining all links between people and nature. Second, use of NCP elevates, emphasizes, and operationalizes the role of indigenous and local knowledge in understanding nature's contribution to people.
Democratizing Data
Democratizing Data builds a community-driven data ecosystem by identifying how datasets are used and reducing barriers to accessing high-quality public data. The initiative enhances the discoverability, usability, and relevance of data for researchers, policymakers, and stakeholder communities. A suite of tools and strategic partnerships supports this work by connecting users to the data, insights, and networks needed to inform decisions and generate impact.
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community · Issue 2.2, Spring 2020
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Request PDF
Request PDF | On Jan 1, 2013, Jessica McCallum Breen and others published Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Find, read and cite all the research you need on ResearchGate
Professionalising Community Management Roles in Interdisciplinary Research Projects
In this article we discuss community management in interdisciplinary research teams, focusing on recognising and professionalising roles referred to here as the Research Community Managers (RCM). Drawing insights and examples from research and data science projects, we discuss how RCM roles address some of the researchâs most pressing challenges, from promoting best practices for open research and reproducibility to engaging diverse stakeholders in community-led research and ensuring fair recognition for their contributions. We offer a Community Maturation Indicator and share examples of projects from The Alan Turing Institute, the UK's national institute for data science and Artificial Intelligence (AI), where institutionally supported RCM roles were established. With the aim to integrate RCM expertise in teams involved in data science and AI research, we provide an RCM Skills and Competencies Framework. We also propose a roadmap for professionalising RCM roles by improving recognition and rewards, potential career paths and organisational support structures. To systematically sustain and progress these roles, we recommend institutional investment in establishing RCM teams that are empowered to prioritise collaboration, transparency and community-based approaches in interdisciplinary projects, such as in data science and AI. As a team, RCMs are well placed to connect disparate teams, initiatives and resources across the organisation, building more resilient research communities that can achieve greater innovation, improved project outcomes and a strongly connected ecosystem, with impacts extending beyond their narrow contexts.
Knowledge infrastructures for the Anthropocene
The technosphere metabolizes not only energy and materials, but information and knowledge as well. This article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. In the 19th and 20th centuries, major systems were built for monitoring both the environment and human activity of all kinds, for modeling geophysical processes such as climate change, and for preserving and refining scientific memory, i.e. data about the planetary past. Despite many failures, these knowledge infrastructures also helped achieve notable successes such as the Limited Test Ban Treaty of 1963, the ozone depletion accords of the 1980s, and the Paris Agreement on climate change of 2015. The article’s second part proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials and information, but also integrate those techniques with new accounting practices aimed at sustainability. Scientific examples include remarkable recent work on long-term socio-ecological research, and the assessment reports of the Intergovernmental Panel on Climate Change. In terms of practical knowledge, one key to effective accounting may be ‘recycling’ of the vast amounts of ‘waste’ data created by virtually all online systems today. Examples include dramatic environmental efficiency gains by Ikea and United Parcel Service, through improved logistics, self-provision of renewable energy, and feedback from close monitoring of delivery trucks. Blending social ‘data exhaust’ with physical and environmental information, an environmentally focused logistics might trim away excess energy and materials in production, find new ways to re-use or recycle waste, and generate new ideas for eliminating toxic byproducts, greenhouse gas emissions and other metabolites.
Knowledge infrastructures for the Anthropocene
The technosphere metabolizes not only energy and materials, but information and knowledge as well. This article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. In the 19th and 20th centuries, major systems were built for monitoring both the environment and human activity of all kinds, for modeling geophysical processes such as climate change, and for preserving and refining scientific memory, i.e. data about the planetary past. Despite many failures, these knowledge infrastructures also helped achieve notable successes such as the Limited Test Ban Treaty of 1963, the ozone depletion accords of the 1980s, and the Paris Agreement on climate change of 2015. The article’s second part proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials and information, but also integrate those techniques with new accounting practices aimed at sustainability. Scientific examples include remarkable recent work on long-term socio-ecological research, and the assessment reports of the Intergovernmental Panel on Climate Change. In terms of practical knowledge, one key to effective accounting may be ‘recycling’ of the vast amounts of ‘waste’ data created by virtually all online systems today. Examples include dramatic environmental efficiency gains by Ikea and United Parcel Service, through improved logistics, self-provision of renewable energy, and feedback from close monitoring of delivery trucks. Blending social ‘data exhaust’ with physical and environmental information, an environmentally focused logistics might trim away excess energy and materials in production, find new ways to re-use or recycle waste, and generate new ideas for eliminating toxic byproducts, greenhouse gas emissions and other metabolites.
👀 "Rather than viewing citizen science merely as crowdsourced data collection or computational tools as autonomous infrastructure, we advocate for human-machine systems in which citizen scientists, including distributed publics, participate as monitors, interpreters...and co-producers of knowledge."
