The Backbone of Participatory Science: Reframing Citizen Observatories as Research Infrastructures

Abstract

Citizen observatories (COs) have emerged as essential research infrastructures for participatory science, supporting data collection and community engagement. They enable communities to monitor their environments, actively track indicators aligned with the Sustainable Development Goals (SDGs), and contribute valuable data to scientific research and evidence-based, informed policy-making. Despite their growing importance, COs remain conceptually fluid, with varying interpretations across disciplines and contexts. This paper examines the evolution of COs from their origins in the early 2010s to their current multifaceted roles, revealing three key dimensions: descriptively as socio-technical systems, instrumentally as research infrastructures, and normatively as advanced participatory science initiatives. We specifically highlight the critical role of COs as research infrastructures and propose a set of essential functions and characteristics. These functions range from providing technical capabilities for data collection and quality assurance to social dimensions, including community building and governance frameworks. Additionally, our analysis identifies two operational models: tailored COs designed for specific projects and open COs supporting multiple initiatives. Reframing COs as research infrastructures rather than isolated initiatives emphasizes the need for long-term institutional support, shared services, and coordinated policies to ensure their sustainability and maximize their contribution to both scientific knowledge and public participation, ultimately strengthening the foundations of participatory science.

1. Introduction

Citizen observatories (COs) play a key role in providing the digital spaces, technological platforms, and methodological standards that enable citizens to systematically monitor and document their environments, contributing valuable data for scientific research, policy-making [1], and the monitoring of indicators related to sustainable development goals (SDGs) [2,3,4,5,6]. Platforms like iNaturalist, eBird, MINKA, CitSci.org, and Observation.org host a diverse range of participatory science initiatives that have collectively generated millions of biodiversity and environmental observations, transcending geographical boundaries and temporal scales.

For the purposes of this review, we adopt a provisional working definition of a CO as a specific type of citizen science platform (CSP) that is primarily geared towards (i) supporting on site or in situ data collection and (ii) sustaining the online engagement of the people and communities that drive participatory science initiatives. This definition serves as an initial point of departure, and one of the aims of this paper is to unpack and refine the concept in the light of the systematic analysis that follows. In line with Liu et al. [7], we understand CSPs to be web-based infrastructures that provide a single point of access to multiple functions—ranging from data submission and management to visualization, learning resources, and community building—designed to enable and broaden citizen science practice. Although we sometimes distinguish between the narrower term CO and the broader label CSP, both kinds of infrastructure ultimately support participatory science endeavors.

While COs are commonly framed within citizen science, we adopt the broader label participatory science—the active involvement of non-professional scientists in genuine research—to encompass citizen science, community science, participatory monitoring, and related variants [8]. This choice recognizes the definitional complexity highlighted by Haklay et al. in “What is Citizen Science? The Challenges of Definition” [9]; this attempts to pin down a single, all-inclusive meaning for “citizen science” and inevitably struggles to capture the field’s wide range of practices, purposes, and contexts, yielding multiple, often inconsistent interpretations that say as much about the definer’s viewpoint as the phenomenon itself.

This approach also reflects the reality that COs already provide support to a diverse spectrum of participatory initiatives beyond citizen science. These initiatives can vary significantly in their approach, ranging from bottom-up, community-led efforts to top-down, institution-driven endeavors. In community-driven models, local participants, sometimes in collaboration with credentialed scientists, work together on specific projects, integrating both scientific expertise and local knowledge [8]. This approach includes methods such as community science [10] and community-based monitoring [1]. On the other hand, organization-driven projects, often referred to as citizen science, typically involve large groups of geographically dispersed participants who collect data about their local environment, public health, and additional matters of concern [8].

Although digital infrastructures for participatory science have expanded rapidly, a global inventory of either CSPs or COs is still lacking. In their global assessment of citizen science’s contribution to biodiversity monitoring, Chandler et al. (2016) [11] identified 114 portals—platforms that correspond to what we define here as CSPs. Taking a technology lens, Palacin-Silva et al. [12] reviewed the literature from 2015–2016 to produce a state-of-the-art study of COs and highlighted 108 environment-focused COs. Regional initiatives continue to fill in the picture: the WeObserve landscape map of COs [13], the catalogue of platforms in EU-Citizen Science [14], CIVIS [15], and other knowledge hubs are progressively pooling information, although they are in a preliminary state.

A sample of 12 large-scale COs that provide open access platforms for biodiversity and environmental participatory science is presented here as an illustrative yet not exhaustive overview; its purpose is to show the range of infrastructure currently available rather than to rank it. The list deliberately includes major contributors of citizen science data to the Global Biodiversity Information Facility (GBIF)—eBird, iNaturalist, and Artportalen [16]—alongside platforms that aggregate hundreds of biodiversity projects and users, such as Pl@ntNet, MINKA, and Project Noah. Multiparameter infrastructures that support a wide spectrum of environmental projects, including CitSci and Anecdata, are likewise represented, together with region-specific systems such as Australia’s BioCollect and South Africa’s Cybertracker. Zooniverse, an iconic CSP [7], is excluded because its strength lies in online data processing workflows (e.g., image classification) rather than the field-based data collection that defines the observatories examined here. A summary of these COs and their basic statistics is provided in

Table 1. List of selected COs in the biodiversity and environmental field that provide open access and services to participatory science initiatives, mainly facilitating field-based data collection and digital engagement.

Citizen Observatory	Ref.	Host Institution and Country	Number of Projects Hosted	Number of Users-Accounts *	Number of Observations
Environmental and Biodiversity
CitSci.Org	[17]	Colorado State University. USA.	>1400	>17,000	>165,000 observations >2.1 million measurements
Anecdata	[18]	MDI Biological Laboratory, Maine, USA.	>340	>19,400	>160,600
BioCollect	[19]	Atlas of Living Australia (ALA). Australia.	760	-	>51 million
Biodiversity
eBird	[20]	Cornell Lab, Cornell University. USA.	-	>930,000	>1.6 billion
Observation.org	[21]	Observation International. The Netherlands.	-	>470,000	>269 million
iNaturalist	[22,23]	iNaturalist org. USA.	>180,000	>3.5 million	>226 million
ArtPortalen	[24] †	SLU, Swedish Environmental Protection Agency, Swedish University of Agricultural Science. Sweden.	>5000	>90,000	>110 million
Pl@ntNet	[25]	CIRAD, INRAE, INRIA and IRD. France.	-	>7.6 million *	>25 million
Project Noah	[26]	New York University. USA.	>1000	>450,000	>920,000
MINKA	[27]	EMBIMOS research group. Institut of Ciències del Mar (ICM-CSIC). Spain.	>300	>1.400	>380,000
Cybertracker	[28,29]	Cybertracker Conservation NPC (Non Profit Company). Cape Town, South Africa.	>1000 **	>50,000	-
India Biodiversity Portal (IBP)	[30]	IBP Consortium. India.	-	25,800	>1.61 million

* Data were collected between February and April 2025; the specific dates are provided in the references for each CO listed in the “Ref” column. ** Cybertracker projects were counted as sites (national parks worldwide) using the platform. † Updated data about users and projects obtained through personal communication with J. Liljeblad, 2025 [24].

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Despite their widespread use, COs remain an evolving concept with two major interpretations. One perspective views COs as advanced community-based monitoring systems or evolved citizen science initiatives designed to create social and political impact [31,32,33]. The other frames them as research infrastructures that enable the sustained generation, management, and integration of citizen-contributed data into formal research and policy ecosystems [34,35]. While much of the literature focuses on COs as advanced citizen science initiatives, this article argues that their role as research infrastructure is equally vital yet underexplored. We make the case for understanding COs as infrastructures by examining their origins, conceptual evolution, functions, characteristics, and typology, highlighting the importance of sustaining them as open platforms that make possible the expansion and impact of participatory science.

2. COs Concept: Origin and Roots

The term CO gained traction in the early 2010s, particularly within Europe, through a series of projects funded by the European Commission, including Citclops, Omniscientis, CITI-SENSE, COBWEB, and WeSenseIt [36]. This momentum has continued with more recent initiatives such as GROW, LandSense, SCENT, and Ground Truth 2.0 [37], along with infrastructure-oriented projects like Cos4Cloud [38], which aim to develop services to enhance CO platforms. This European investment positioned COs as key platforms for strengthening environmental and biodiversity monitoring systems through the integration of community-contributed data [37]. Even though citizens have been involved in environmental monitoring for decades, the specific term and framing of COs came with a double purpose: to empower communities [31,39] and to complement formal Earth observation systems [40]. This dual focus connects COs to both the social sciences and Earth system sciences.

However, CO-like initiatives—those involving multiple stakeholders to address social challenges and inform decision-making through monitoring mechanisms—have existed since the 1970s as mechanisms for public participation in research and public affairs [41,42,43]. They have been implemented in areas such as mobility [44], medicine, language, and the social sciences [45] in regions beyond Europe, with notable examples from Central and South America [46]. Their role has become even more important with the rise of digital technologies, which have made participation easier and more widespread, enabling unprecedented scales in the range, speed, and nature of participation. At the same time, these technologies have introduced new ethical, legal, and social challenges [47]. Over time, COs have become closely connected to areas like community-based monitoring (CBM) and volunteered geographic information (VGI) [37]. They have also been adopted in fields such as Earth observation, environmental governance, environmental justice, and citizen science. Each of these areas brings something different to how COs are shaped and used, but they also share common functions and characteristics, which we explore in the next sections.

These developments highlight the interdisciplinary foundations of COs. The social sciences offer concepts and practices related to public participation, co-production of knowledge, and community engagement, which are key to how observatories are designed to empower citizens. Environmental and Earth sciences contribute methods for participatory monitoring and provide frameworks to integrate local observations into environmental assessment and governance. Meanwhile, the field of information and communication technologies (ICTs) supplies the tools—sensors, geospatial platforms, and data management systems—that make the technical operations of observatories possible. More recently, the concept of COs has also been shaped by developments in open science and the growing recognition of research infrastructures as essential to knowledge production. In this light, COs are increasingly seen not only as projects or platforms but as ongoing services that support a wide range of participatory science initiatives over time. This convergence of disciplinary influences places COs in a rich epistemological space at the intersection of community action, scientific inquiry, and technological innovation [48].

This complexity also explains why there is no single, universally accepted definition of what a CO is [36,40,49]. As Hager et al. (2021) [50] pointed out, the term CO has no equivalent outside Europe, even though similar platforms and initiatives exist globally. Within Europe itself, the concept is interpreted and applied in multiple ways, particularly within the citizen science community [36]. What the European context has offered is a space where social, environmental, and technical elements were brought together under a common framework. However, the relevance of COs extends far beyond this context, as initiatives around the world continue to develop their own models—often under different names—that reflect similar purposes and values. In the following sections, we examine how these diverse interpretations have shaped the conceptual evolution of COs and what they mean for their future as research infrastructures.

3. COs over Time: A Multifaceted Conceptual Trajectory

The meaning of COs continues to evolve, with various definitions highlighting different aspects of the concept, ranging from methods and monitoring systems to their role as infrastructures. To better understand this evolution,

Table 2. List of selected definitions for the term CO from 2012 to 2021 focused on the fields of citizen science, environmental monitoring, and Earth observation.

Authors	Ref.	Definition Focused on the Main Role of COs	Year
European Commission	[51]	› Earth observation applications › Integrated network of community-based in situ observations › A framework to exchange knowledge and experience based on data repositories	2012
Ciravegna et al. in Mazumdar et al.	[52,53]	› A method, an environment, and an infrastructure supporting an information ecosystem for communities and citizens	2013
Liu et al.	[49]	› The citizens’ own observations and understanding of environmentally related problems, and, in particular, reporting and commenting on them › Participation of citizens in monitoring the quality of the environment in which they live › Essential tool for better observing, understanding, protecting, and enhancing our environment › Instrument to support community-based environmental decision-making	2014
Hunt et al.	[54]	› Framework for the definition, collection, and management of data › Geospatial Cyberinfrastructure	2015
Wehn et al.	[55]	› ICT-enabled initiatives that extend traditional citizen science through long-term engagement, policy links, and e-participation for social innovation.	2015
Palacin-Silva et al.	[12]	› Social computing applications › Big data systems	2016
European Commission	[56]	› Community-based environmental monitoring and information systems	2017
Grainger	[40]	› New type of Earth observation system › Next phase in the evolution of citizen science › Any use of Earth observation technology in which citizens collect data and are empowered by the information generated from these data to participate in environmental management	2017
Momino et al.	[35]	› Research infrastructures that allow for the development of citizen science projects › Infrastructures: technological platforms where tools are developed, such as web portals, smartphone apps, and electronic devices	2017
Montargil and Santos	[37]	› Open and shared information system	2017
Ferri et al.	[57]	› A particular form of citizen science	2019
Hemment et al.	[58]	› Innovation that connects people, science, and technologies to create collaborative data, knowledge, and action	2019
Ajates et al.	[5]	› Participatory initiatives led by transdisciplinary teams, training citizen scientists on data collection and analysis	2020
Groundtruth 2.0	[59]	› Particular examples of citizen science › Specific form of community-based environmental monitoring systems and knowledge co-production initiatives	2020
Gold et al.	[60]	› Initiatives that engage citizens and other stakeholders in community-based environmental monitoring	2020
Rathnayake et al.	[1]	› Socio-technical constellations that enable citizens to monitor their environment via the collection and sharing of data › COs as an application or iteration of citizen science	2020
Hager et al.	[50]	› An approach of participatory research in the field of citizen science	2021
WeObserve Consortium	[33]	› A particular form of citizen science and collective action with the aim to create evidence and knowledge and to apply the evidence for advocacy and place-based decision-making to reach environmental and societal impact	2021

presents a curated set of 18 definitions selected specifically for their relevance and diversity. This is not a full inventory but a deliberate selection aimed at capturing the range of interpretations that have emerged. The definitions span from 2012 to 2021 and were drawn from both peer-reviewed and the grey literature, with a focus on publications related to citizen science.

Based on 18 published definitions, we ran a concise inductive thematic analysis: key phrases were open-coded, similar codes were clustered into broader themes, and the results were cross-checked chronologically (2012–2021) to trace shifts in emphasis. This process shows how researchers and practitioners have described COs over time and highlights the different roles they play within participatory science. The analysis reveals seven main conceptual approaches. These include the following:

COs as complementary Earth observation systems (2012–2017): Early definitions (e.g., by the European Commission [51] and Grainger [40]) frame COs as novel extensions of Earth observation networks. In this view, citizens act as a distributed network of sensors, providing community-based in situ observations that complement remote sensing and monitoring systems. The emphasis is on the technical integration of citizen data into existing Earth observation infrastructures for improved environmental information.

COs as a methodological framework (2013–2015): Authors such as Ciravegna [52,53], the European Commission, and Hunt et al. [54] describe COs as a framework or method for organizing citizen participation and data flows. This view emphasizes the structured process aspects, defining what data to collect, how citizens participate, and how data is managed and used. A CO, in this sense, is a socio-technical workflow for citizen science, lowering barriers for public participation while ensuring scientific rigor.

COs as a tool for monitoring and environmental governance (2014–2017): Several definitions (e.g., Liu et al. [49]; Wehn et al. [55]) situate COs within community-based environmental monitoring and decision-making processes. COs are seen as instruments for communities to observe, understand, protect, and manage their local environment. They are essential tools for environmental stewardship and empower citizens to participate in environmental management and policy. This governance-centric view stresses local knowledge, collective action, and the use of citizen-generated evidence to influence decisions, connecting grassroots monitoring to institutional frameworks.

COs as information systems (2016–2017): These definitions (e.g., Montargil & Santos [37]; Palacin-Silva [12]) emphasize COs as open information systems and social computing applications that facilitate mass collaboration. COs are portrayed as data-intensive systems characterized by large volumes of observations “coming and going” to potentially millions of users. The focus here is on information flow and interaction, with citizens and scientists sharing data in near-real time, enabled by web and mobile technologies. This approach highlights the big data aspect of COs and their reliance on ICT connectivity.

COs as research infrastructure (2015–2020): Another approach, highlighted by Momino et al. [35] and Hunt et al. [54], defines COs as platform-based research infrastructures that support the implementation of citizen science projects. Here, COs are essentially technological platforms (web portals, mobile apps, and sensor devices), offering data collection, management, and sharing services. The focus is on the infrastructural capacity, providing tools and standardized processes to sustain citizen science activities and data over multiple projects or long timeframes. Hunt et al. [54] liken COs to a geospatial cyberinfrastructure, combining computational resources and services to bring people together for science.

COs as socio-technical systems (2019–2020): COs from this perspective are defined by Rathnayake et al. [1] and Hemment et al. [58] as socio-technical systems that bring together people, technologies, data, organizations, and processes within a dynamic framework. The term socio-technical highlights this integrated nature, where tools like sensors, mobile apps, and databases are closely tied to the practices, values, and relationships that shape how data is generated, shared, and used. Viewing COs through this lens also draws attention to the need for strong governance models, clear participation protocols, and long-term sustainability, not only of the digital infrastructure but also of the human networks involved. Issues such as data quality, accessibility, and reusability are, therefore, not just technical matters but also social and ethical ones.

COs as advanced participatory science initiatives (2015–2021): The definitions by the WeObserve consortium [33], When et al. [55], and Grainger [40] characterize COs as a specific form of citizen science focused on environmental monitoring and collective action. COs are transdisciplinary, participatory initiatives that engage citizens alongside scientists to co-produce knowledge and drive advocacy or behavioral change. This approach synthesizes prior aspects, depicting COs as innovations connecting people, science, and technology to create collaborative data, knowledge, and action.

The seven approaches to defining COs demonstrate that the concept has undergone significant evolution over the past decade. Early definitions described COs primarily as technical tools, often focusing on their role in data collection through ICT-based platforms. Over time, however, the concept has expanded to encompass more complex socio-technical systems and research infrastructures. This shift reflects a growing recognition of the interdependence between the social and technical dimensions of COs. Initially, participation was often framed through a top-down lens, where citizens contributed data defined by institutional needs. In contrast, recent approaches emphasize COs as platforms for collective action, citizen empowerment, and transformative societal change. This evolution also mirrors broader trends and discussions within the participatory science community, where the growing influence of the social sciences and humanities has brought equity, inclusion, governance, and the ethics of participation to the forefront, highlighting the increasingly intertwined nature of technical systems and social values [8,61,62].

To better understand this evolution, we applied a framework developed by Haklay (2021) [9] for categorizing the definitions in citizen science: descriptive, instrumental, and normative. The descriptive aspect refers to how a definition attempts to explain the type of activity. The instrumentalist aspect highlights how definitions are often shaped by the goals and objectives of the actors or organizations providing them; definitions can be instrumental in achieving specific aims. The normative aspect encompasses the expectations, assumptions, and values embedded within a definition. These normative elements can set expectations for the various actors involved in a project. This framework helps to explain the multiplicity of CO definitions and the challenges in reaching a single, unified understanding. It also shows how COs, as part of the broader participatory science landscape, reflect similar conceptual tensions. The approaches and dimensions of CO definitions are summarized in Figure 1.

The figure illustrates the seven conceptual approaches developed over time to define a CO, culminating in three main dimensions that can be categorized according to Haklay’s framework of descriptive, instrumental, and normative aspects. COs, under the descriptive element, are viewed as socio-technical systems; under the instrumental element, they serve as research infrastructures; and under the normative element, they represent an advanced participatory science initiative.

Descriptive definitions present COs as socio-technical systems that bring together digital tools (e.g., apps, sensors, and platforms) and social practices (e.g., collaboration and community engagement) to co-produce knowledge. These definitions highlight the integrated nature of COs, where technology and society interact dynamically to enable data collection, sharing, and use.

Instrumental definitions focus on COs as research infrastructures; they are viewed as technological platforms that support the development of participatory science projects by providing tools, services, and standards and hosting online communities. This view often emphasizes scalability, interoperability, and the capacity of COs to facilitate distributed data collection across projects, geographies, and disciplines.

Normative definitions place COs as advanced participatory science initiatives. Here, the role of COs extends beyond data collection to include fostering local empowerment, supporting evidence-based decision-making, and promoting environmental justice. In this framing, COs act as mechanisms for community-based environmental governance, enabling citizens not only to observe and report issues but also to use this evidence to drive local action and influence policy.

Previous attempts to analyze the concept of COs, such as those by Gold et al. [36], have debated whether COs are a subset of citizen science or represent a distinct model. What this analysis shows is that the concept of COs is used across all three definitional dimensions—descriptive, instrumental, and normative. The lack of clarity often stems not from conceptual weakness, but from the varied use of different facets in different contexts and for different purposes. The challenge, then, is not to impose a single definition, but to acknowledge that COs are multi-dimensional and that each dimension serves specific functions depending on the context.

This article focuses primarily on the instrumental definition: COs as research infrastructures. Understanding COs from this perspective allows us to examine what the functions, characteristics, and typologies of these infrastructures are.

4. COs as Research Infrastructures: Essential Functions

COs are essential research infrastructures, as they serve as complex systems integrating both technological and social elements to enable long-term, large-scale citizen participation in scientific research and policy-making. COs comprise a set of facilities that provide resources and services, including platforms and networks, for research communities to conduct research and foster innovation in their fields [35,63,64]. They provide the technological backbone for data collection, visualization, and sharing, as well as the creation of online communities, and shape how citizens, scientists, and organizations collaborate [65].

COs support ongoing citizen participation across thematic areas and geographies, beyond individual project timelines or closures [35]. They blend human participation with technology, creating systems in which social and technical elements are deeply intertwined. Modern COs are not mere data-collection apps or sensor networks; they are complex constellations of people, devices, data flows, and organizations [61]. The design and operation of a CO must integrate community dynamics (interests, motivations, trust, and local knowledge) with technical components (platform architecture, data standards, and analytical tools) [66]. This socio-technical integration is what enables COs to function as sustainable, adaptive systems [61].

From this socio-technical perspective, COs are evolving systems that recognize the need for both technological and social integration. Albagli et al. (2020) [65] argue that infrastructure is a relational entity that exists at the intersection of diverse actors and knowledge systems, which is exactly the case for COs, as they bring together citizens, scientists, and other stakeholders in a co-productive manner. Similarly, Corsín’s (2014) [67] notion of the "right to infrastructure" resonates with the idea that COs provide citizens with access to the tools and data necessary for participation in environmental governance, further reinforcing the importance of COs as a public resource.

In their role as research infrastructures, COs are shaped by multiple factors. COs are shaped by social, environmental, and organizational factors [68]. Environmentally, they are influenced by legal norms and political discourses, such as data protection laws and science policies. Socially, they integrate into research practices by engaging users and stakeholders. Organizationally, they depend on operational models and internal resources, like team structure and funding, to adapt and succeed.

To fulfill their role as research infrastructures, COs must perform essential functions for the participatory science community that span both technical and social dimensions. In this article, we propose a spectrum of functions that range from the technical to the social side, as shown in Figure 2. On the technical side, they provide tools for data collection (e.g., smartphone apps and sensors) and management, ensuring interoperability, data quality, and adherence to standards. Socially, COs focus on recruiting and training participants, moderating interactions, and maintaining motivation through feedback and recognition. They also establish governance frameworks, ensure ethical practices, and align projects with community interests and policy needs.

5. Technical Functions

Providing a base for technical infrastructure: Establishes and maintains a stable and reliable technical infrastructure, including servers, databases, and applications, for data storage, long-term preservation, and high-performance processing. This infrastructure must be resilient enough to support large volumes of citizen-generated data while enabling secure backup and integration with other systems.

Facilitating data-driven research through data collection and management: Develops user-friendly tools, such as mobile apps, sensors, and web interfaces, to make data collection accessible and accurate. These tools ensure that data are properly recorded and stored, providing a seamless experience for participants while facilitating the management and organization of the collected data.

Ensuring data quality: Implements systems for validating and ensuring the quality of data, including collaborative validation, automated checks, and expert review mechanisms. Data quality controls are essential to maintain the integrity and reliability of the collected information, allowing for effective scientific use and integration with other data sources. Offering tools that allow for coordinated data validation, enhancement, and integration.

Supporting communication, interaction, and the co-creation of knowledge: Provides platforms for effective communication between participants, scientists, and other stakeholders, fostering collaboration, knowledge exchange, and co-creation. These platforms enable users to interact, share insights, and discuss data, enriching the scientific value of the observations and ensuring that the project aligns with community needs.

Supporting open science, data interoperability, and mobilization: Facilitates the sharing and integration of CO data with other systems to ensure interoperability. Adhering to established standards (such as Darwin Core or others), COs ensure that the data collected can be integrated into regional, national, and global systems or repositories, contributing to broader scientific efforts and policy discussions.

6. Social Functions

Creating and sustaining online communities: Facilitates the development of active, engaged online communities where participants can collaborate, share knowledge, and support each other. This involves moderating interactions, fostering a sense of collective purpose, and creating spaces for ongoing communication and collaboration among citizens, scientists, and other stakeholders.

Provides training to participants: Ensuring they understand how to collect data, interact with the platform, and contribute effectively to the scientific process. This function is essential for empowering citizens, ensuring they are equipped with the necessary skills and knowledge to contribute meaningfully to CO activities.

Moderating community interactions: Facilitates positive, respectful, and productive communication between citizens, scientists, and other stakeholders within the CO platform. This includes managing discussions, preventing conflicts, and creating a collaborative environment that fosters knowledge exchange and a collective sense of purpose.

Sustaining motivation through feedback and acknowledgment: Maintains long-term engagement by providing ongoing feedback to participants on their contributions, acknowledging their efforts, and highlighting the significance of their involvement. Recognition can include public acknowledgment, rewards, data citation, or notifications about how the data collected contributes to scientific knowledge, further motivating participants to stay active.

Establishing governance and ensuring ethical practices: Defines the framework for decision-making within the CO, ensuring transparency in how data is used, managed, and shared. This governance structure addresses ethical considerations, such as safeguarding participants’ privacy, respecting cultural sensitivities, and aligning with both national and international regulations (e.g., data protection laws and intellectual property rights). It also determines who has authority over data access and how data will be utilized for research, ensuring all practices align with community values and ethical standards.

7. COs as Infrastructures: Essential Characteristics

Drawing from the literature on infrastructures, particularly the works of Star and Ruhleder (1996) [69] and Heaton (2022) [61], COs exhibit the key characteristics of infrastructures: they are embedded in social practices, often become transparent in use, and have a broad reach across different geographic areas and over extended periods. As Fecher et al. (2021) [68] suggest, research infrastructures are relational systems, and COs are no exception, as they rely on both technical components and social actors working in concert. They are also built upon existing systems and technologies, utilizing open data standards and protocols that enable integration with professional scientific networks, further enhancing their role in the research landscape. Considering this theoretical background, a set of essential characteristics is proposed, as shown in Figure 3.

An essential characteristic of COs is the development and use of technologies that respond to societal needs. These technologies are designed for non-experts, ensuring that anyone, regardless of technical background, can engage with data collection and analysis processes [44]. Central to this idea is the integration of ICTs as enablers of openness, interoperability, and accessibility. COs often employ user-friendly mobile apps, web portals, and low-cost sensors that enable citizens to gather and submit data effortlessly, such as georeferenced images or environmental measurements [70]. These platforms are not only tools for engagement but also serve as open data repositories, allowing for real-time data aggregation and analysis.

From an information infrastructure perspective, COs are large-scale systems that co-evolve alongside their users and organizational contexts. They unite diverse actors—citizens, scientists, and policy-makers—each with distinct goals and knowledge, facilitating their interactions through technology. As Heaton (2022) [61] notes, COs create “chains of participation” that redefine scientific work, blurring the lines between data collection and decision-making. The data platform of a CO not only stores data (technical function) but also shapes how participants collaborate and communicate (social function). Similarly, the policy interface involves both technical integration and a social negotiation of trust and data credibility. CO designers focus on user experience and community management, as technologies influence participation, access, and data sharing. This creates a dynamic relationship where citizens co-create technologies while also being shaped by the available functionalities and flows.

In operational terms, COs establish multidirectional information flows [40] between citizens, decision-makers, and scientists. Participants actively contribute data and information—reporting observations and submitting data via mobile phones or web forms—while receiving feedback, analysis results, or responses from authorities. This continuous exchange builds a sense of collaboration and ensures data relevance [49]. The communication framework of a CO is often as important as the data collection framework. Many COs include dashboards, alerts, or discussion forums to facilitate this ongoing dialogue.

COs, thus, operate as dynamic learning environments [35] that can accommodate a range of participation models—from highly structured campaigns led by authorities (top-down) to organic initiatives arising from citizen groups (bottom-up). They are often designed to be adaptable across different thematic areas and scales. For example, the same platform architecture might support a biodiversity mapping project in one region and a water quality monitoring project in another, simply by configuring different protocols and engagement strategies.

8. CO Types: Tailored and Open COs

COs can operate under different models, ranging from tailored, project-specific platforms to open, multi-project platforms, depending on their purpose and how they are designed. This distinction is primarily based on the creation of the infrastructure—whether it is built to address specific, localized needs or to serve as a broader infrastructure that supports multiple projects over time.

In the tailored CO model, a platform is specifically developed for a particular community or project. It focuses on addressing particular challenges, such as monitoring air quality or noise pollution in a city, with custom features to meet the project’s needs. Illustrative examples of tailored COs include Aire Ciudadano [71], which focuses on monitoring air quality in Bogotá, Colombia, allowing community members to track pollution levels in their neighborhoods. Likewise, Ictio [72] represents a specialized platform developed for documenting fish observations throughout the Amazon Basin, linking indigenous and local knowledge with conservation efforts across multiple countries. The data collected, such as observations or sensor readings, are processed and used for immediate, localized outcomes, such as community reports or decisions about local actions. While this model allows for high relevance and effective results during the project’s lifespan, it faces challenges in sustainability. Once funding or initial enthusiasm fades, maintaining the technology and engaging the community can become difficult, which may result in the platform becoming inactive or unable to scale beyond its initial scope. The key risk of this approach is that many tailored COs end up operating as isolated silos, responding only to specific projects and local needs. Without broader integration or long-term support, they may fail to contribute to a larger knowledge ecosystem, and their impact may remain limited, with data or momentum lost once the project concludes [34].

In contrast, the open CO model is designed for long-term scalability and widespread usage. These platforms are typically managed by organizations (e.g., universities, museums, or NGOs) and support multiple projects simultaneously. They provide common tools for data entry, storage, visualization, and analysis, without needing to develop infrastructure from scratch. Platforms such as eBird, iNaturalist, CitSci.org, and Observation.org, for example, act as open repositories where diverse participatory science initiatives can collect, upload, and share their data in one place. This model allows for data aggregation at regional or global scales, which can complement formal scientific datasets and inform broader policy decisions. As the data flows through these open platforms, it can contribute to large-scale scientific efforts, such as biodiversity assessments or climate change monitoring. Open platforms also allow for integration with global research infrastructures like GBIF or Copernicus, which amplify the impact of individual observations beyond the local context. They act as “providers, facilitators, mediators, and platforms” for effective data management [73].

These two models—tailored and open—are not mutually exclusive but represent endpoints on a continuum. Some COs begin as tailored platforms and later expand into open systems, while others might start with open infrastructure and support specific, localized projects. The choice between the models often depends on the intended purpose of the CO, the community it serves, and the need for scalability and long-term sustainability.

From an institutional perspective, the models also imply different organizational structures. Tailored COs are typically run by temporary project teams, such as collaborations between universities, community groups, or local authorities. They tend to rely on specific project funding or volunteer efforts for ongoing operation. In contrast, open CO platforms are managed by more stable organizations or consortia, which provide continuous support and resources. These platforms may secure funding through a mix of grants, institutional backing, and user contributions, which helps ensure their longevity [34].

Each model offers distinct advantages and faces its own set of challenges. Tailored COs are highly adaptable, allowing for quick innovation and the ability to address specific local needs by leveraging local knowledge and relationships. However, they often face difficulties with long-term sustainability and community engagement once the initial project concludes. Open CO platforms, by contrast, spread development costs across many initiatives and foster knowledge sharing at scale, but their size brings new hurdles: they must stay flexible for diverse projects while enforcing common rules for data quality, privacy, and governance; they must curate mixed datasets, harmonize workflows, and upgrade constantly to match new data collection methods; and they must manage digital inequalities, participant turnover, and the risk that projects drift into silos. Concerns about data sovereignty also appear on open platforms, so many now use clear licensing policies to reassure contributors that their data remain transparent and properly credited. In short, tailored COs wrestle with limited resources and long-term survival, whereas open COs grapple with complex, large-scale integration and governance challenges—but both models need thoughtful design, steady funding, and strong community support to succeed.

Ultimately, COs can form an interconnected ecosystem where tailored and open platforms interact and exchange data. A tailored CO platform could share its data with an open platform, contributing to global research efforts, while the open platform could offer tailored outputs for local actions. This network of network models allows data and knowledge to circulate across various levels, creating a collaborative environment that benefits both local and global research initiatives. Through interoperability and collaboration, COs are becoming an essential part of a larger data ecosystem, where citizen-generated data complements formal scientific research and informs policy decisions at multiple scales. Figure 4 summarizes the core elements of each type [4]. Exploring the replicability of tailored approaches, the evolution of open platforms, and the rise of hybrid configurations that mix elements of both remains an important avenue for future work and may reveal additional categories beyond those described here.

9. Conclusions

We must emphasize the role of COs as infrastructures, as they already function in this capacity. COs support participation, data generation, and engagement across various projects, themes, and regions, providing the necessary foundation for scaling participatory science. Viewing COs only as individual initiatives underestimates their systemic role. Framing them as research infrastructures shifts the focus to what makes participation sustainable, scalable, and impactful, emphasizing the need for coordination, shared services, and institutional support for long-term success.

Despite their inherent value, many COs face sustainability challenges. Tailored observatories risk obsolescence once specific projects end, and open COs must secure stable funding, adapt to evolving user needs, and maintain consistent data standards. Moreover, few national or regional policies explicitly recognize COs as infrastructures, complicating long-term planning and excluding them from infrastructure roadmaps and science funding schemes. This lack of institutional recognition hinders support and contributes to fragmentation within the ecosystem.

Our conceptual analysis reveals that attempting to impose narrow definitions or rigid structures on COs may limit their potential; adaptability is their strength. This article contributes to the clarification of the conceptual dimensions under which COs operate: descriptively, as socio-technical systems; instrumentally, as research infrastructures; and normatively, as advanced participatory science initiatives. We outline the essential social and technical functions and characteristics of COs as infrastructure and propose an approach of tailored and open COs. Rather than advocating for rigid, one-size-fits-all definitions, we propose context-specific characterizations that acknowledge the multifaceted roles of COs.

Looking ahead, future research must expand on the operational models of COs, documenting detailed use cases and examining the data flows within CO networks. Understanding how data moves between different COs—whether tailored or open—is crucial for improving their integration and scalability. This knowledge will be essential for strengthening CO networks and enhancing their collective impact. To support the continued growth of participatory science, it is not enough to focus on individual participatory science projects; we must also invest in the infrastructures—COs—that make them possible and sustainable. This includes further exploration of the terminology used to describe COs. As they increasingly function as research infrastructures for participatory science, the term participatory observatory may offer a more accurate and future-oriented conceptualization—an idea that warrants dedicated investigation.