Citizen Sciences and Environmental and Ecological Sciences

Abstract

Citizen science is an increasingly recognized approach applied in many scientific fields, and especially in the environmental and ecological sciences, in which non-professional participants contribute to the collection of data to advance scientific research. We present participatory citizen science as a valuable method for scientists and practitioners within the environmental and ecological sciences, focusing on the entire life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight important 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 for data sharing. We also provide a range of examples to illustrate the variety of functions, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. Aspects of reproducibility and data sharing are considered, placing citizen science within an open and inclusive science perspective. Finally, we discuss the limitations and challenges that exist and present a perspective on the application of citizen science in different scientific fields.

Introduction

Citizen science, broadly defined as the participation of the public in scientific research and knowledge production, is becoming more developed and more respected and has global relevance and is used in a wide range of scientific fields1,2,3 . Much of this growth is driven by the availability of information technology infrastructure such as cell phones and low-cost sensors to collect and report data, the internet to share data, and cloud storage to host and store data4,5 . Increasing levels of literacy and educational attainment in many parts of the world allow many more people to contribute to the creation of knowledge in a meaningful way6,7. Read also : Los Alamos Faith And Science Forum Presents: Interaction With The Divine – A Statistical View By Dr. Chick Keller.

Citizen science initiatives involve the public in the research process to generate real scientific results8,9,10,11. These results include discoveries, for example in astrophysics12 and archeology13 projects; new insights, for example in epidemiological14 and sociolinguistics15 projects; evidence-based policy making, for example in pollution monitoring initiatives16,17,18; interventions such as public health research19; and environmental governance, including in ecological and biodiversity monitoring initiatives20,21,22. Citizen science research can fill important data gaps across both time and space23, which would not be possible without the contributions of many participants, including people with local and lay24,25 or indigenous knowledge26,27 .

The profile of citizen science is also growing as a central pillar of open science, which encourages scientific collaborations that benefit both science and society, and opens the processes of scientific knowledge creation, evaluation and communication to societal actors outside the public. professional scientific28. The range of benefits that citizen science can deliver beyond scientific results include societal impacts such as awareness of local issues and improved public health, policy impacts such as more effective legislation, political impacts including higher civic participation, economic impacts such as public spending which has a higher impact. and also personal benefits for the participants themselves, from the enjoyment of the activity itself, to new knowledge of a subject and stronger scientific literacy in general29,30,31,32.

The field of citizen science is more widely represented around the world, including established regional networks, such as the European Citizen Science Association, the US Citizen Science Association, the Australian Citizen Science Association and globally through the Global Partnership Citizen Science. Some of the main principles that form the basis of the good practice have been included by an international community of practitioners in ref.33 and the various factors that make up the unique features are described in ref.34.

The range of disciplines in which citizen science can be applied, as well as the different organizational and cultural contexts of these practices, results in a wide range of terms that can all be captured under the broader umbrella of citizen science. Examples include community science, participatory mapping, participatory science, community remote sensing, local monitoring and community-based monitoring3,8,26,35. It is also important to acknowledge the ongoing dispute over inclusive terminology when referring to citizen science participants in a way that recognizes the diverse expertise they bring and does not trivialize their work or exclude certain demographics36, 37. For consistency, we use the term ‘participant’ in this article. In addition, pioneering work in the field has developed typologies to describe activities in different ways, focusing, for example, on different models of public participation in scientific research38, levels of participation35,39 or the orientation and aims of the activities40. Participatory citizen science, as presented in some of these typologies, mainly involves participants in data collection activities and is a widespread approach used in the fields of environment and ecology41.

In this Principal we focus on applying citizen science approaches within the environmental and ecological sciences, where much of the recent growth in the field has occurred. Our main objective is to introduce participatory citizen science, as highlighted above, to scientists and practitioners new to the field. While we recognize the diversity of approaches and the wide range of potential applications, we limit our scope to contributory projects in the environmental and ecological sciences because they can provide a manageable entry point into citizen science practices, they have plenty of examples to draw on them, thereby allowing us to provide a more comprehensive overview and guidance on how to design and implement a citizen science initiative for the first time. We also intend that the Primer will serve as a useful review and general resource for those experienced in the field.

Experimentation

In this section, we provide an overview of the different stages of design and implementation of participatory citizen science projects in the field of ecology and environmental sciences, some of which will be described in more detail in the following sections. There are various guidelines for designing and implementing citizen science projects, covering aspects from data management to stakeholder engagement42,43,44,45,46,47,48,49. Examples of such guidelines are presented in Table 1. Here, we summarize some of the most relevant issues and considerations from these resources and provide additional insights. See the article : The ancient galaxy’s spin suggests the universe’s earliest stars rapidly coalesced into disks. Each of the steps presented in this section is interconnected and one step does not have to end to start another (Fig. 1). Each and every stage during the project cycle should be reviewed to actively incorporate changing factors, lessons learned and participant feedback. It is also necessary to remember that there is no one-size-fits-all approach to citizen science and that these steps need to be adapted to the context of the project.

Six iterative steps in designing and implementing a citizen science project from identifying the need or problem to evaluating the project, focusing on the fields of environmental sciences and ecology. Project teams should be focused on activities and designing and implementing citizen science initiatives.

Stage 1

Step 1 of the citizen science project lifecycle is identifying the need or problem, in which the need or problem the project aims to address is identified, and its boundaries are defined38,48. This may interest you : Maybe You Are Better In Science Than You Think, New ‘Citizen Science’ Education Outlines. Depending on the purpose and nature of the project, scientists, participants, other stakeholders or all of them can identify the problem or need together, based on models of public participation in scientific research38 and levels of participation35,39.

At this stage, it is useful to think about the key stakeholders and try to understand the problem from their perspective – especially stakeholders from target groups. Possible solutions to the problem and its limitations must be taken into account, and research questions and general objectives should be formulated with reference to these solutions and limitations. The work can be guided by acknowledging that stakeholders may not have identified a specific problem or research question, as is sometimes the case in ecological and environmental studies, but may have noted the need for baseline monitoring.

In addition, it is important to have an overview of similar projects and available methods that may be useful for the project. Within the rapidly growing field and literature of citizen science, it is likely that similar problems and needs have already been addressed by other initiatives. Some early considerations on the evaluation and sustainability of the project are also helpful to frame the overall idea of ​​the project and to establish a sound basis for the next steps48.

Stage 2

It is important to recognize that not all research projects can be addressed with a citizen science approach. This phase is about ensuring that citizen science is the right approach to address the problem and the research questions identified in the first phase45. The goal is to understand whether the participation of citizen science participants will help to achieve the desired results, while at the same time benefiting participants by addressing their needs or fostering new skills and expertise50. If those two conditions can be met, the citizen science approach is likely to be suitable for the project42.

Determining the right citizen science approach depends on various factors, such as the research questions, the spatial and temporal scale of the project, the type and amount of data required to obtain results, the level of expertise required to collect the data, the necessary training and coordination efforts, or the target groups of the project, such as the participants, policy makers, funders and scientific communities and practitioners44. Funding is a key consideration, and it is important to review the resources available and the requirements for the project’s objectives before starting42. This includes considerations related to human resources, including the skills required in the team and the tasks and responsibilities of the project team. Equipment, travel or training necessary to collect data should also be taken into account.

Examples of projects suitable for a citizen science approach are observing the natural environment, including wildlife and species; detecting changes in land use and land cover through in situ monitoring, where observations are made at the site; classification of satellite images to identify deforestation; and monitoring water or air quality, or disease threats, among many others. Projects that may not be suitable for a citizen science approach may be those that require the use of expensive or highly technical equipment, or projects that require a large time commitment such as collecting detailed measurements every few hours hourly or every day over a season45.

Stage 3

In this design phase, the overall aims and objectives of the project must be clearly defined in close collaboration with the potential participants. For example, recommending policy change or collecting data to answer a scientific question or a combination of both could be the impetus for designing a citizen science project51. In many cases, practitioners may want to achieve additional outcomes beyond the project’s intended outcomes, such as social learning, behavioral change or increased interest in science and community building; such outcomes should also be determined1,2,52,53. A detailed definition of the project objectives will help to identify the data needs and data collection tools and formats, which could be a smartphone app or data sheets, among others. How these data should be collected — individually or in teams, with or without prior training — also depends on the aims and objectives of the project48.

It is also important to identify whether similar data collection formats and methods are available and whether existing data collection platforms can be reused. Table 2 provides some examples of existing citizen science platforms for reuse. Where and how to store the data and for how long, and how to share it must also be taken into account at the design stage. These aspects are discussed in more detail in the reproducibility and data deposition section below.

Particular attention should be paid to the sampling design and the expected methods of data analysis. For example, depending on the project, participants may collect data in a timely manner, without a standardized sampling design, which may lead to oversampling of certain sites48 and limited methods for data analysis. However, strategies such as providing additional incentives to visit specific sites or areas, where no or very little data are available, or performing appropriate statistical analyzes as part of the quality control process, can among other measures, help to avoid or reduce the impact of quality control. such problems54,55. Clearly communicating potential sampling biases to the audience will help improve data quality and increase credibility and data reusability51,56.

Deliberate training strategies should be developed, considering online or on-site training and the distribution of necessary materials such as manuals and how-to videos, among other things. Defining potential participants and setting out a communication plan for participants and stakeholders, including the communication methods and tools, is also part of the design phase. Periodical newsletters, social media, scientific papers, podcasts, and a website and project forum are examples of frequently used communication methods48,57. In addition, it has been shown that the establishment of partnerships with the media such as newspapers, television channels or radio stations has been successful in increasing participation in citizen science58.

There is also a need to define participant tasks in detail, identify benefits for participants, and address individual safety issues related to data collection. Decisions should be made about what learning outcomes or benefits will be provided to participants, and how safety and liability concerns will be addressed. For example, a project app can provide safety information when it is downloaded and platforms can offer educational tools even before the data collection activity takes place. Ideally, participants’ input should be taken into account when shaping these tasks and addressing safety issues, so that their needs can be continually assessed to allow for diversity and inclusion.

Stage 4

The next step is to develop a community building plan for the project. For successful community building, it is important to know the community and understand their motivations for providing time and skills to the project. The identification of the age groups, the levels of education and the interests of the community members, among other information, helps to get to know the community48. Motivation for participation can vary by community member and may include contributing to science in general and helping the environment, meeting others with similar interests or gaining new skills. There is a vast literature on what motivates participants to take part in a citizen science project, which can provide insights and guidance55,59,60,61,62,63,64,65,66.

At this stage, it is also important to look at ways of community participation. This can be done online or through workshops and in-person meetings, depending on the type of project and the number of participants. In many cases, identifying the role of citizen science enablers will help ensure success. Citizen science enablers are facilitators or third parties who often bring skills and expertise in facilitation and communication, public engagement or access to the public or funding. These research enablers or facilitators may help build relationships between all those involved, creating stronger collaboration67. After engaging with a community, participation in the project depends on how well the participation strategies are designed and implemented. Participation may also vary due to factors beyond the researcher’s control. For example, studying environmental topics or less popular species may attract less attention.

It is also crucial42,63 to decide on the recognition of community members’ participation in the project42,63 and participants should be involved. This includes giving credit to individuals for their contributions, for example, by including them as a co-author in scientific publications or by providing a visualization tool on the project website that shows the contributions of the participants, which would have implications for privacy policy the project68,69. It is also necessary to acknowledge the involvement of partners and stakeholders in the project.

When creating a community building plan, it is important to be inclusive. Efforts should be made to ensure the participation of people with different backgrounds, ethnicities, incomes and levels of education, and who have different access to and use of technology36. This is important not only from a social and environmental justice point of view, but also from a scientific point of view, to prevent biases in data collection, to reach inaccessible or remote areas, to increase geographical coverage and representation, as well good to tackle a wider range of. stakeholder perspectives and networks70,71,72,73,74.

Stage 5

This phase highlights the processes and steps involved in data management, which may apply to any research project. However, the features presented here reflect the characteristics of citizen science projects. These steps are not necessarily taken in a sequential order: some may happen at the same time, others more than once43. The stages of planning, collection and verification are presented in this section, but stages of analysis, description, preservation and integration are discussed in subsequent sections.

Planning

In this phase, a data management plan linked to the design phase of the project should be prepared, taking into account requirements such as laws and regulations regarding data privacy and ownership, and policies related to data access and sharing. Additionally, it is critical to define project ethical practices, such as how to attribute contributions while ensuring privacy and document them through a clear set of terms of use and privacy policy for the project, including which data which will be shared and documented. how75. It is also important to assess the sustainability of data management, identify the associated costs and ensure that resources are available to achieve successful data management.

In the planning phase, it is also important to make the final decision on the types of observations needed to achieve the aims and objectives of the project. Examples of observation types are images, videos, sounds, water samples, sensor data (such as temperature and noise) or people as sensors (for detecting smells, for example) and interpretive data (such as identification and classification), among others other45. When planning how to manage the data to ensure quality, decisions made in the design phase related to sampling, participant training, and evaluation should be reviewed and tailored to the changing needs of the project.

As part of the planning, it is important to be clear about the data to be collected and how to visualize this data, for example through graphs, summary tables or maps, to facilitate the interpretation of the results. The project team should monitor the results of the project and share these results with participants and other target groups, encouraging them at the same time to support the evaluation of these results and to communicate them to an audience. diverse, including decision makers.

Collecting

The collection phase refers to the type of information required to achieve the project’s objectives. This could be project-related information, such as observations of plants, trees and animals, as well as their locations and numbers, or additional information, such as the name, location and email address of participants to ensure that the contributions of the participants are properly recognized or the quality of data. It is important to consider the potential future use of data when deciding what type of observations and additional information to collect.

In ecological and environmental projects of the contributory nature, data are collected mainly using sensors, special equipment, standard protocols and in a timely manner (where no standards or sampling methods are used), or through a combination of methods. When collecting observations, using a smartphone app can increase quality, as data such as location, date and time can be automatically recorded. However, this method of data collection may exclude those who do not have access to such technologies66. To ensure inclusiveness, it is possible to use printed data sheets and smartphones at the same time for the participation of participants with different backgrounds and possibilities48. This phase is also when data collection training can be provided to ensure participants have all the information they need to help generate the necessary data.

Assuring

The assurance phase is about ensuring the quality of the data generated as part of the project. Data quality is related to its fitness for purpose, meaning that the data is secure enough to be used for its intended purpose76. Data quality can be ensured through quality assurance (QA) processes, which are implemented before and during data collection, and quality control (QC) processes, which occur after data collection. For example, providing training to participants or developing standard protocols for data collection is part of QA, while marking outliers or checking photos sent by participants are examples of QC43,77. These and additional examples are discussed in detail in the results section.

QA and QC processes must be defined according to project aims and objectives, but also their scale. Checking the quality of expert submissions may be an option in a small-scale project but not in a larger one with thousands of participants. The project may incur additional costs from QA and QC, so resource implications should be considered. Clearly communicating the quality of the data, as well as the QA and QC processes, increases confidence in the data and improves its reusability78,79.

Stage 6

Evaluation is an essential step in any project including citizen science. There are different ways of evaluation, such as initial evaluation to collect baseline information, formative evaluation (done during implementation) and summative evaluation (usually done at the end of a project to identify its effectiveness)42,80,81 ,82. The best method of evaluation depends on the project, but it is recommended to think of evaluation as an ongoing effort, which allows for improvement at any time. In some cases, evaluation can be a requirement of funders, along with identifying the short and long-term consequences of the project. Critical to a successful citizen science project29,42,57 is agreeing on metrics to measure success, and potential emerging and future impact. A new approach to evaluation in citizen science projects is focusing on the individual impact aspects (in collaboration with the participants) and the socio-ecological benefits, which are worth considering when designing the evaluation methodology83. One example of this is the use of conservation management interventions resulting from citizen science projects as a proxy for their conservation impact66.

Results

In this section, we provide examples of QA and QC approaches, including training and testing of participants, community-based quality review, automatic control and statistical tools in participatory citizen science. We also provide examples of tools and methods to support data analysis in citizen science.

Training and testing participants is one way to improve data quality77 and is considered good practice84,85. Many projects offer tools and online training materials to improve the quality of participant observations, such as species identification guides or videos86,87. Some projects also provide tailored feedback to participants based on expert validation as training to provide higher quality contributions55,88,89. In addition, training can be done through community data consensus, where other participants cross-check and validate data90.

Another approach to improving data quality involves testing participants’ data collection and interpretation skills before or during the project, through quizzes and tests combined with tutorials, near real-time expert feedback or cross-checks and community-based validation90,91, 92. This can help assess data accuracy and support the project team to filter or weight the data based on participant performance77,93. These tests can be completed by asking participants to provide additional evidence related to their observations such as images94. Testing can also specifically focus on hard-to-find data, including identifying cryptic or rare species to assess participant skills86. Another approach is triangulation, which uses multiple observers, methods and data sources to improve quality and overcome biases arising from a single method, single observer and data source66,95.

Another approach to ensuring data quality is a community-based review of data quality, which may be conducted by experts or dedicated participants. For example, projects using the iNaturalist platform (see Table 2) can designate experts as curators or managers, who can review the shared observations96. At the same time, iNaturalist enables observations to achieve high reliability by providing research grade through community consensus, which can also be an effective means of ensuring data quality86,92. Another approach is to nominate expert participants based on the quality of past observations. Contributor-experts are participants who oversee the validation of comments recorded by other participants. This nomination can be done, for example, by an algorithm92 or by participants self-nomination97.

Data quality can also be improved through automatic control and statistical tools. For example, automatic filtering can help flag observations that fall outside the expected patterns98. Several statistical techniques have been proposed to ensure the quality of citizen science data23,99,100. These include inter-observer skill differences to correct bias in species distribution models101,102, combining opportunistic data with data collected through sampling efforts103 or pooling survey and collection data for many different species104, among others. In some projects, automatic filters are used to verify the internal consistency of the datasets105. Project teams decide to make these adjustments based on participant testing and the results of methods that assess data accuracy.

More sophisticated data science methods have also been used to improve quality in big data analyses98,106. In other cases, bias corrections are already integrated into the sampling tools and protocols. For example, ref.107 collected anonymized geographic data to correct for biases caused by uneven sampling efforts by participants in a disease-carrying mosquito monitoring project’s mobile app. Table 3 shows some of the issues and concerns related to citizen science data quality and methods often used to address them.

Analysing

Analysis of participant-generated data should be planned in advance according to project goals and data requirements. There are a variety of tools and methods to support data analysis in citizen science that largely depend on the type of observations.

Spatio-temporal distribution of species and natural resources

Many citizen science projects from the environmental and ecological sciences are designed to collect spatiotemporal distribution data for species or natural resources32. Data from these projects are usually analyzed using a qualitative or quantitative approach. For example, qualitative methods are used in studies to show the presence or absence of certain species in a certain area. Alternatively, spatial data can be quantitatively analyzed to generate patterns of abundance by counting observed numbers of species of a particular group, or individuals of a particular species. Different types of analysis may be required depending on the experimental design, which can be structured with prescribed sampling in space and time, semi-structured with minimal guidelines but adding supplementary data to each observation, or including unstructured data, providing opportunistic observations without a survey. implementing protocol108.

Each type of design structure has different advantages and challenges that can be overcome with careful analysis. In the case of structured data, citizen science protocols may determine the spatial distribution and resolution of observation sites, as well as the frequency of observations55,109. These data can be used to assess species abundance along a cross-section110 or within two-dimensional grids111,112. Structured data are commonly analyzed using tools from environmental and ecological sciences, such as species distribution models113 or the design of ecological indicators114. However, they are often taxonomically and geographically limited. Conversely, unstructured data — such as opportunistic biological records — are usually collected in higher quantities, but may require specific statistical tools to provide reliable indices of abundance from individual observational efforts115,116. In ref.23 a set of methods is proposed, based on filtering or data correction factors, to take into account the variability in recorder activity and uneven observation sites. Model-based data integration has also emerged as a powerful way to combine heterogeneous data sets117. Figure 2 illustrates a stylized workflow for analyzing quantitative measures of species abundance based on structured and unstructured data collection as part of citizen science projects monitoring biodiversity.

After training participants (1), data can be collected through structured or unstructured observations. Structured observations are produced through protocols that determine the spatial distribution and resolution of observation locations (for example, along a cross section), and/or the frequency of observations. Unstructured observations (mostly opportunistic biological records) may require specific statistical tools to produce reliable indices of abundance from individual observational efforts (3). In both cases, data are filtered to eliminate unreliable values ​​(2) and manipulated to calculate other indices such as abundance or local species richness (4) and then modeled (5) for scientific research and are visualization to guide conservation policies (6).

Dynamics of ecosystems

Citizen science data may also be analyzed to study the more complex dynamics of ecosystems, using statistical, computational or experimental tools. For example, citizen science data on insect abundance can be used to test spatial variation in insect flower affinity by taking the total number of taxa recorded in collections as a proxy for flower visitor richness118. By analyzing the occurrence of insects from different families on flowers of different morphologies, citizen science data can help assess the role of flower morphology in flower feeding119.

Other citizen science data can be relevant examples, such as faecal pellets, leaves or soil samples120. These can be analyzed following biological, chemical or physical laboratory protocols such as using visual interpretation keys, DNA extraction, amplification and sequencing121,122. Projects that collect such samples may blend the aforementioned citizen science data analyzes with common analytical tools such as those used in bioinformatics.

Data visualization is also critical to the initial understanding and exploration of citizen science data123. This can be done using open source software such as R or QGIS, or their (proprietary) counterparts such as Stata, SAS and ArcGIS. These tools, as well as many others, such as the Data Visualization Overlay from the citizen science platform SPOTTERON, the open source JavaScript library CesiumJS124 or the open source tool CWDAT125 can be used to explore data, form hypotheses and future research guide, to link a person’s contribution to the entire project data set or identify data gaps126. Data visualization also allows participants to be more active in different stages of data collection and analysis127.

Applications

The application of citizen science as a practice in the natural sciences dates back to the beginning of scientific inquiry itself and is now spread across the globe, resulting in some of the longest time-series data sets in function in phenology, ornithology and meteorology128,129. In this section, we illustrate the variety of applications of participatory citizen science with examples from biodiversity research, Earth observation and geography and climate change research, where citizen science has an established focus and can be considered an established method3. We complement these examples with applications that fall within the environmental field but outside the box of contributory projects from the Global South, emphasizing the potential and intricacies of community-led citizen science (Box 1), as well as citizen science at the interface of education. and environmental activism (Box 2).

Box 1 Community-led citizen science in ecology and environmental sciences

When non-residents began foraging for bushmeat in the Itagutwa Village Forest in Tanzania, the residents began to monitor the forest’s resources. “It shows them that during this forest,” said a woman when asked why she kept an eye on the forest’s resources60.

When community members are concerned about the environment and the status of natural resources in an area, they sometimes want to guide and drive the research process related to their concerns. Community-led citizen science programs involve members of the public at various stages of the research process beyond data collection, and professional scientists may provide advice and training41,229.

CCS can be demanding in terms of the time and effort required of participants and scientists, but the potential benefits for those involved are enormous. The fully participatory approach can provide time- and place-specific data at low cost that can be trusted by the people concerned and involved230. This approach can provide important natural resource management inputs. The approach can act as a vehicle for ongoing engagement between local communities and scientists, to improve communities’ scientific understanding and the feeling of being heard and acknowledged24. In addition, CCS can contribute to transparency, accountability and local ownership of resource management initiatives, by empowering community members and encouraging locally meaningful activities66,231.

CCS programs require ecological and facilitation expertise from researchers, careful consideration and long-term planning. The approach is used in the Global South205 and the Global North232, including the Arctic233, in environmental justice234 and other community-based initiatives. The Community Based Monitoring Library provides good practice examples from the Arctic and lessons learned for practitioners. CCS is particularly suitable when policy environments allow full or partial community control over resource management66. Programs related to Indigenous communities may benefit from Indigenous knowledge24, using Indigenous indicators235 or scientific methods adapted for non-specialist use236. Sampling accuracy can be optimized66 by triangulation across communities, community members and methods. Data management should protect sensitive personal data and respect local data ownership and the sovereignty of indigenous knowledge95.

Among the challenges CCS programs sometimes face are the following: getting authorities to respond to data and proposals and overcoming their reluctance to relinquish authority237; ensure collective action and public participation164, especially when programs are driven by external research and not by the communities themselves238; or address scientists’ perceptions that participant data are unreliable, which may hinder the use of results239, despite demonstrations across many ecosystems and socio-political settings85 that citizen science can provide reliable information and results supply (for example, regarding status and trends in species abundance and information on natural resources). Disturbingly, there is also increased persecution of participants who engage in CCS and related advocacy165,240. Between 2002 and 2020, more than 2,200 people were reported killed, mainly in countries with authoritarian rule, to protect their lands and the environment241; some of them were killed while monitoring the environment and the status of natural resources165. Researchers undertaking CCS need to take such sensitivities, risks and challenges into account.

A comparison of the observations of community members and trained scientists

66,85

Box 2 Collaborative Creation of Scientific Knowledge

Error bars indicate the standard error. Image reprinted from ref.66, CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

The Cientificos de la Basura (Litter Scientists) program is a research alliance between marine scientists, school teachers and school children from Chile and other Latin American countries, investigating the extent and causes of marine litter. It is a participatory citizen science program with a strong focus on education and environmental protection242. Each school year one research topic is identified, for example a type of macroplastic or microplastic on beaches, litter in rivers or interactions between litter and organisms. Several learning modules introduce the aquatic environment, ecological relationships, anthropogenic threats and the scientific method. Specifically designed educational materials present the topic and prompt a specific research question. Standardized sampling methods are carefully introduced242 and implemented by school children who take specific roles during the research activity and work in small teams (Supplementary Figure 4). In a follow-up class activity, they assess their own data and interpret their results. They are also encouraged to communicate their findings within their community and implement small local mitigation actions. The school children know that they are part of a wider scientific investigation of an important environmental problem and this knowledge can be very motivating for them243. However, participation in citizen science activities had only limited effects on science literacy and pro-environmental behavior, and it is emphasized that these activities should be part of broader and more comprehensive programs that foster scientific learning and which promotes environmental stewardship244,245.

The school teachers are the main allies in the program; they are regularly trained by the professional scientists and personal communication is maintained during the program activities. Teachers also present the observations to the science team and will receive the collective results first, ideally in a timely manner so that they can share them with the school children who participated in the research. The scientific team also evaluates the data to answer the scientific questions, interprets the results, prepares them for scientific publication and shares them with decision makers and the public through media outlets246,247,248.

Biodiversity research

The data helped to formulate or improve national waste management laws249. The data is validated and maintained by the scientific team and is available on request, along with all educational and scientific materials. The approach of the Cientificos de la Basura program has been replicated in Germany250,251, where the joint program — Plastic Pirates — is currently expanding to work with schools from other European countries.

MammalWeb

Research related to biodiversity is common among citizen science projects3. At the same time, citizen science projects are an important part of biodiversity research, historically130,131 and now22. For example, citizen science projects related to species monitoring have contributed at least 50% of observations to international and global biodiversity databases, such as GBIF132,133. Despite this contribution, citizen science has sometimes not been recognized as a major contributor to these efforts134. One format for participatory citizen science activity focused on biodiversity that has gained popularity and traction in recent years is the BioBlitz. The Great Southern BioBlitz includes more than 270 local and regional initiatives in the Southern Hemisphere contributing over 190,000 biodiversity observations across the Southern Hemisphere in 2021. Citizen science offers enormous opportunities for applications in biodiversity research, including taxa and under-sampled regions in the Arctic and Global South35,74,85,135,136, using more widely secondary, image-based data to extract relative ecological information or for automated abundance modeling using citizen science data that is updated regularly137. We illustrate the use of citizen science for species diversity, abundance, distribution and habitat research with three examples: MammalWeb, Spipoll and the Participation Guide of Marine Species in the Metropolitan Area of ​​Barcelona.

MammalWeb started in 2015 in the north east of England, United Kingdom, and has expanded to include participants in many European countries. It applies a participatory citizen science approach to wildlife monitoring to fill data gaps in mammal biodiversity and distribution138. To participate in the project, participants provide their own motion camera traps or borrow one from MammalWeb and deploy these cameras for observations (Supplementary Figure 1). Collected photos and videos with spatiotemporal metadata are uploaded to MammalWeb and classified by registered users. Images of people are removed from the classification pool immediately after they are flagged. Multiple classifications are obtained for each photo and video sequence, and subject matter experts classify a subset of the data. These two groups of classifications can be aggregated into consensus classifications with confidence levels139. Time stamps from the camera trap observations enable profiling of the daily and seasonal temporal patterns of the various species. Spatial data analyzes have improved the understanding of the diversity and distribution of wild mammals, revealing temporal patterns in animal behavior, and may aid future analyzes and estimates of population structure through occupancy modeling140. The dataset is also designed to train machine learning algorithms for automated wildlife identification141.

Other conservation organizations are now hosting camera trap projects on MammalWeb. This increases geographic coverage, decentralizes the organization of participants and demonstrates its ability to stimulate participation through the novelty of new wildlife observed. MammalWeb was also introduced to a local secondary school where students designed and implemented related engagement activities142 and directed a professionally produced documentary143.

Spipoll

More than 270 participants across eight European countries have contributed data to MammalWeb. The network includes more than 50 schools and 20 additional organisations. Participants provided more than 340 years of cumulative observation time, collecting more than 620,000 photo/video sequences and approximately 2 million photographs. Participants helped identify potentially invasive species including raccoons and coots144. Engaging with nature through MammalWeb improved the mental health of student participants, especially during the COVID-19 pandemic 145. Several of the MammalWeb participants have independently initiated multiple community-led spin-off projects, including one that has resulted in the declaration of a local nature reserve146. Key insights for participatory citizen science include an increased recognition of the value of partnering with organizations and schools to expand data coverage and participation, an advanced understanding of human classification statistical modeling to improve the accuracy of the data obtained as well as the realization that most people are engaged in. the participants are highly motivated and move towards citizen co-creation science, a development that scientists should welcome.

The French National Museum of Natural History (MNHN) and the Office of Entomological Information (OPIE) launched a Photographic Survey of Flower Visitors, Spipoll, in 2010 to study the changes in plant-pollinator interactions in space and time across the French147. Participants follow a standardized protocol, which requires no prior knowledge of insects. Wherever participants find a flowering plant – from dense urban centers to natural areas – they photograph each invertebrate landing on its flowers during a 20-minute period. After identifying insects and plants using a dedicated online identification tool, participants upload their photos and associated identifications, as well as the date, time and location of observations and climate conditions to the Spipoll website. Initially, the quality control was carried out only by expert entomologists, who confirmed the identification of insects; however, since 2019, a collaborative quality control system has been implemented, which now allows participants to validate comments submitted by others (Supplementary Figure 2). Data sets are analyzed to quantify visiting insect communities, depending on the flower family and environmental factors. These results are then interpreted in terms of plant-insect interaction characteristics as a function of time and environmental factors, such as urban and natural land use affinities of frequent and irregular taxa within several insect orders118,148.

To ensure the long-term participation of the participants, annual meetings are organized with researchers from MNHN and community managers from OPIE. Weekly news, scientific results and other information are shared on the project website, and a monthly newsletter is sent to participants informing them of the overall progress. In addition, participants can comment on the views of others on a dedicated website, leading to a social network, which promotes scientific learning, increases data quality and contributes to community building97.

Participatory Guide of the Marine Species in the Barcelona Metropolitan Area

Data from Spipoll has led to new scientific knowledge on the effects of urbanization on community composition118,119, contrasting relationships of pollinators with different land use147 and the role of domestic gardens as favorable pollinator habitats148. Data sets are available under open access licenses. Spipoll’s online communication spaces for participants greatly contribute to the constitution of the friendly learning community, help to retain participants in the long term and help to improve data quality97. As such, Spipoll demonstrates the key role of such online interaction and participant support tools in achieving the multiple objectives of participatory citizen science.

Biodiversity conservation near urban beaches is challenged by increases in anthropogenic and climate impacts. The Participatory Guide to Marine Species in the Barcelona Metropolitan Area (URBAMAR) project, a collaboration between an academic institution, the Institute of Marine Sciences (ICM) and a private company, Annellides Environmental Services, engages participants to monitor and understand the factors that affect them . biodiversity on beaches around Barcelona Metropolitan Area. Sightings are mostly collected during guided snorkeling trips offered by Anellides Environmental Services, which provides local knowledge and logistical support, such as masks and underwater cameras. Photos collected during snorkeling events are then added to an online project platform that allows participants to share the views for comments, identification and collaborative validation. The community-based validations are then reviewed by the ICM data custodian. ICM researchers analyze data to identify differences in the composition of ecological communities and link these to anthropogenic impacts. The first estimate of species richness149 was obtained with an approach based on unique observations and the species list150.

The project was promoted mainly through the social channels of Annellides Environmental Services, taking advantage of the guided scientific snorkeling trips as a market opportunity. Most participants had no prior knowledge of marine organisms. Trips with guided specialists ensured the correct use of equipment and safety conditions, as well as the most suitable places to explore depending on the sea conditions (Supplementary Figure 1). In some cases, they also provide schools with innovative learning activities151.

Earth observation and geography

The project resulted in the first Participatory Guide to Marine Biodiversity in the Barcelona Metropolitan area152 and helped to provide a baseline data set for the unknown extent of marine biodiversity in Barcelona’s urban coastal waters. The Barcelona City Council has included some of the results as a new marine component, a layer of fish species, in its Atlas of Barcelona Biodiversity. The project provides a successful example of the Quintuple Helix innovation model applied in citizen science with the participation of academia, industry, government and civil society153. The participation of different actors — and participants in voluntary work in particular — facilitated a new societal perspective on marine biodiversity in the urban environment that could influence coastal management policies in the city in the future. This shows the collective impact that participatory citizen science can have.

FotoQuest Go

In the fields of Earth observation and geography, the practice of citizen science appears under different terms. These include voluntary geographic information154, crowdsourced geographic information155 and more recently, citizen geographic science156. In this area, applications range from bottom-up projects, such as OpenStreetMap, where hundreds of thousands of participants create a free map of the world157, to scientist-led projects that support extended seismograph networks in regions prone to landslides and earthquakes. but not much is covered by seismic stations158. We demonstrate the application of citizen science in earth observation and geography with the FotoQuest Go project for land use and land cover change research.

FotoQuest Go aims to collect ground observations of land use and land cover across Europe. A specific aim was to identify whether citizen science participants can collect observations of the same quality as those collected by professionals at lower cost and with higher temporal and spatial frequency, for the Land Use/Coverage Area Framework Survey (LUCAS) , complete a professional survey. conducted by EuroStat on land use and land cover across the EU every three years159. Participants are encouraged to visit specific locations provided in the FotoQuest Go app, take photos and answer questions about how the land is used at that location. In addition, the FotoQuest Go app collects personal data such as name, age, gender, email and location, time and date of observations. When a participant submits an observation, professional scientists check the quality, comparing it to LUCAS data using the FotoQuest Near-Real-Time Feedback Tool55. The tool allows scientists to send customized messages to participants about the quality of their submission and how it can be improved. Short training videos provide information on how the app works, how participants can make and submit quality comments and how to identify different types of crop to further improve data quality (Supplementary Figure 1).

FotoQuest encourages each participant to visit several places. This implies that the observations provided by the same participant are not independent of each other. At the same time, the closer the locations are to each other, the higher the spatial autocorrelation. To acknowledge the non-independence of the data, generalized linear mixed models were used, including random effects for participant and site. The models were used to match the data collected in FotoQuest Go to the reference data, LUCAS. All model assumptions were checked on the outcomes55,160.

Social media is used extensively to reach out to participants. The project website includes a forum to enable communication between scientists and participants and among participants. In addition, in the FotoQuest Go 2018 campaign, each successful submission was awarded monetary compensation of €1–3, based on the distance of the visited center to the nearest road. FotoQuest Go’s privacy policy, accessible through the project’s website and mobile app, expressly states why personal and other information is collected, how it is stored and used and how it can be retrieved. In addition, FotoQuest Go was designed to comply with the EU General Data Protection Regulation (GDPR), based on professional legal advice161.

Climate change research

The results of the FotoQuest Go 2018 campaign showed that FotoQuest can complement LUCAS by enabling a large amount of high-quality and higher-density in situ data at a much lower cost than official LUCAS55 data, demonstrating the economic and scientific benefits of the that contributing citizen. science can have them. Data from FotoQuest Go is open and freely available in the IIASA Data Repository, and in open access academic paper form55. In addition, FotoQuest Go shows how elements of gamification, targeted incentive schemes and direct feedback from experts can influence participants’ motivations and behavior as well as the quantity and quality of data.

Western Redcedar Dieback Map

Citizen science is also widely used in research on climate change mitigation162,163, adaptation164,165, effects and impacts166,167. Citizen science is being applied across many topics, including — but not limited to — investigating soil moisture120, groundwater168, flood levels169, sea ice170, snow depth35 and snow algae blooms and observing changes in local phenological patterns171, bird migration116, cloud formation172 or coral reefs. damage173. Data collection and analysis, as well as target groups and contact methods, vary greatly depending on the subject and the respective research questions. We illustrate the use of citizen science in plant ecology for forest-related climate change research, where projects have studied the effects of climate change through phenological patterns174, distributional shifts175 and responses to novel wildfire events176, among others. .

The Western Redcedar Dieback Map (WRDM) project was launched in Washington State, USA, as a pilot project of the Forest Health Watch program. It was designed to attract participants to accelerate research and create a common understanding of western redwood surrender. The project was co-designed with researchers from state and federal agencies to reveal the distribution of unhealthy trees and general patterns of tree death in relation to climate change. It aims to identify important environmental factors (such as climate, soils and topographical data) to classify trees as healthy or unhealthy. WRDM was launched on iNaturalist because of its accessibility and usability features (allowing any user to export data), the stability and usability of the mobile application, the built-in support for community agreement as quality control for species identification and the strong user community that already there. . iNaturalist users contribute to the project by sharing comments including photos to identify tree species, answers to custom questions and GPS coordinates. These coordinates were applied to collect additional environmental data, such as climate data using the ClimateNA tool177 and soil data from the SSURGO database178. Data shared on iNaturalist is combined with ancillary environmental data to explore the factors associated with the health of the western redcedar using a random forest classification algorithm. Collections of observations of both healthy and unhealthy trees have helped to overcome a common challenge in biodiversity studies, that of confidently documenting the absence of organisms179.

The Forest Health Watch program recruited participants through presentations and maintained interest by hosting monthly research updates to add transparency, brainstorm project improvements and discuss details and updates on project progress. Many participants were first-time users of iNaturalist, and joined the platform out of an interest in accelerating research on the death of the western red. Some participants were recruited directly through iNaturalist by commenting on relevant observations outside the project. Overall, the recruitment and retention activities were time intensive, and the dedication required to recruit participants should not be underestimated.

Reproducibility and data deposition

Citizen science can provide valuable complementary data to climate change research, especially where there is ancillary environmental data. The WRDM project provides an example of an approach to combining environmental data with empirical data collected through iNaturalist in a participatory citizen science project. As of April 2022, more than 1,400 observations have been collected from nearly 200 participants for the WRDM project (Supplementary Figure 3). Tree health assessments conducted by citizen science participants were critical to identifying environmental predictors of western redwood yield. The approach used in this study can be applied in other participatory citizen science projects designed to study the relationships between environmental factors and organisms.

Describing and preserving

Recording and preserving data is essential for its discovery, reproduction and reuse. Here, we discuss aspects of reproducibility and reuse and provide recommendations on how to preserve data. In addition, we discuss the integration of data from participatory citizen science with other data sources to help address complex societal issues.

Data and other outputs from citizen science should be described, documented and shared with permissions to ensure re-use and reproducibility180, but it is important to consider what data to share and how to share it133. For example, it could help illegal poaching by sharing the exact location of endangered species. Citizen science data sharing also creates ethical issues regarding data privacy181 and data sovereignty of individual participants, which means that participants have the right to have full control over their own data35. The solutions include lowering coordinate resolution in spatial data or hiding personally identifiable information, both of which facilitate data sharing to serve multiple scientific and policy objectives related to content and maintaining participant privacy. These objectives include the characterization of the spatial variation of natural resources with global changes111 including in poorly sampled regions of the world137, modeling the extinction of species182, assessing modifications to the biological composition of communities112 and training machine learning algorithms183. Additional outcomes of open data sharing include providing information for policies, such as biodiversity conservation by influencing the delimitation of conservation zones, identifying illegal fishing or hunting practices110, assessing the impact of policies conservation and participate in official monitoring of natural resources32.

Describing the data — referred to as metadata — is essential to facilitate data sharing and reuse. There are various metadata standards that can be used in citizen science. For example, Public Participation in Scientific Research (PPSR) Core is a set of metadata standards developed especially for citizen science, and Darwin Core is a standard that aims to facilitate the sharing of biodiversity information184. A recognized metadata standard can help maximize the value of data by offering a common format for data storage, description, and interoperability and integration with other datasets. Rich metadata and data practices also support the FAIR data principles (available, accessible, interoperable and reusable), as well as the ten ECSA principles of citizen science and the ECSA characteristics of citizen science33,34,185.

Protecting sensitive citizen data and respecting local data ownership and the sovereignty of indigenous knowledge are important aspects of data management for citizen science programs35,95. The CARE (common benefit, authority to control, responsibility and ethics) principles of Indigenous data governance provide a framework to support Indigenous data goals that complement global efforts to promote open data186. Furthermore, it is important to highlight that citizen science projects inherently involve diverse participants and that ethical publishing practices181,187,188 should be implemented. It is also good practice to involve participants in the design of the data management plan, for example when deciding how to assign. Figure 3 illustrates the process of publishing outputs from a citizen science project.

The steps shown fall within the data description and preservation step in Step 5 of a citizen science project plan: gathering outputs, pre-publication preparation, organizing and formatting outputs and publishing in long-term repositories. Input should be sought from participants when creating and implementing the plan. GBIF logo reprinted with permission from GBIF ( https://www.gbif.org ). Dryad logo reprinted with permission from Dryad (https://datadryad.org/stash). Zenodo logo reprinted with permission from Zenodo ( https://help.zenodo.org/ ). OSF logo reprinted from OSF, CC0 1.0 Universal ( https://creativecommons.org/publicdomain/zero/1.0/ ).

Equally important are software and hardware outputs. Good practice of reproducible code should be followed, as demonstrated by The Zooniverse and iNaturalist, who publish the full source code of their servers and mobile applications on GitHub189. The process is similar to hardware design190.

All outputs — such as data, software, hardware and others — should be published in a dedicated data repository. For environmental and ecological sciences, this could be GBIF or Dryad. Multidisciplinary repositories such as OSF or Zenodo are also suitable. These repositories provide a Digital Object Identifier (DOI), which allows a dataset to have a permanent, referable reference. The Registry of Research Data Repositories provides a list of additional data repositories187. Although software and hardware design often happens on version control platforms such as GitLab or GitHub, copies should be deposited in these repositories. The Cos4Cloud project hosts various online services to help citizen science data interoperability and reproducibility for inclusion in the European Open Science Cloud.

Integrating

Open licenses should apply to all output, formally granting users the above-mentioned reuse permissions. The choosealicense.com website provides guidance for software, while the CERN Open Hardware Licenses apply to hardware designs. Creative Commons Attribution (CC BY), Attribution-ShareAlike (CC BY-SA) or the public domain attribution (CC0) licenses are usually used for data and other publications, such as the educational material accompanying the Snapshot Safari project191. Ideally, citizen science projects should publish all their output in this way, not just data.

Limitations and optimizations

Data integration involves combining data from different citizen science projects or combining citizen science data with other data sources to address complex research questions43. For example, the Global Earth Challenge Marine Debris Data Integration Platform harmonizes and publishes citizen science data on beach and shoreline debris collected through three citizen science initiatives, the National Oceanic and Atmospheric Administration’s (NOAA) Marine Debris Monitoring and Assessment Project, the Environment Agency’s European Marine Litter Watch, and the Ocean Conservancy’s International Coastal Cleanup (ICC) Litter Information and Data for Education and Solutions (TIDES) database192. Picture Pile, a web-based and mobile citizen science application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photos that participants can quickly classify, combines Earth observation data and ingest citizen science to monitor make the environment193. Detailed reporting of data using standard methodologies can support successful data integration.

Citizen science has several limitations, including the wide range of skills required outside the research topic, fostering participation, biases in data collection and analysis, sensor calibration issues and various data privacy regulations around the world, among other things. In this section, we elaborate on some of these limitations and give examples of possible solutions.

Designing and implementing citizen science projects requires a unique set of skills and knowledge beyond the research itself, such as communication planning and execution, community building and participant management. Gathering these skills may require significant investment depending on the project and the expectations of the participants. Some of these costs can be reduced by using free or low-cost software and establishing partnerships with other project teams and stakeholders who carry out similar activities42.

Lack of participant involvement can be another limitation. In some cases, this may be related to design, which is under the control of the project team62. In other cases, it may be beyond the control of the project team (such as when the proposed research topic is not interesting to the audience. In addition , in some contexts of high social inequality, citizen science may only address certain segments of society, and may fail to historically underrepresent groups, less affluent members of society and people including individuals and communities from certain socio-economic, racial and ethnic groups. This raises concerns about the relevance of citizen science to different communities and may affect the quality of the results by excluding important perspectives in the projects70,194. It is important understand participants’ motivations at the design stage, create tasks that appeal to different motivations, ensure that the tasks these in line with the expectations of the participants, to facilitate feedback and exchange of participants throughout the project, as well as to integrate co-design processes into the project depending on the availability of time, resources and necessary skills62,195,196.

All scientific data — including data obtained using citizen science — is subject to biases. It is important to be aware of these biases and work to mitigate them through careful design and data analysis. A common bias in citizen science concerns whether non-professional participants can produce accurate datasets. As previously discussed, there are a variety of approaches that can be used to ensure data quality through the design and implementation of iterative projects, and there is a growing literature showing that citizen science projects can produce reliable data comparable to those professional scientists produce55,77,79,85,197. .

Another type of bias in citizen science has to do with population density; areas with larger populations are likely to be monitored more frequently198. Road networks also influence the places that can be easily accessed and monitored199. There are also temporal biases due to higher rates of monitoring during daytime hours and at weekends56,200,201. Furthermore, there are common biases related to the contributions of the participants, where a relatively small number of participants provide much of the data202. Participant demographics — including education level, age and gender — can influence the coverage and wider impact of a project203.

When making sensor-based observations, citizen science projects will mostly use low-cost sensors, often those integrated with smartphones or appropriated for other purposes (such as automotive applications). The limitations and biases of these sensors must be addressed within the context of their purpose in the study, such as raising awareness of a certain issue within a community or contributing to policy making, among other things204. Existing literature can help identify and resolve these sources of bias.

Another major limitation to the adoption of citizen science is the changing data protection laws in different countries. When deciding what data to collect and share within a project, it is important to identify and comply with the relevant laws and regulations of the country or countries in which the project operates. It is recommended to look at the details necessary for the project at the design stage of the project. For example, the General Data Protection Regulation of the European Union prescribes the principle of data minimization, which means that the collection of data must be limited to what is necessary in relation to the purposes for which they are processed and must be kept for as long as necessary161.

In addition, there may be limitations to the design and implementation of citizen science projects in remote and unsafe areas, where crime levels are high and political risks, or where mobile network coverage is poor, access to smartphones and low electricity and illiteracy levels among participants. high Co-design and a community-based approach can address such challenges and ensure a high level of engagement with participants205 (Box 1).

Outlook

Finally, risks associated with data collection can also be a limitation of citizen science. For example, if the project requires participants to visit specific locations to make observations, the potential risks to the participants should be assessed and clearly communicated to them as well as information on how to avoid these risks. Table 4 lists potential limitations of citizen science and strategies to overcome them.

The fields of application for citizen science methods and approaches continue to expand in terms of content and increase in terms of improved methodologies. More examples of citizen science research are entering the mainstream scientific literature. The principles described in this paper have been successfully applied to a wide range of research areas, contributing to the development of best practice and new approaches within the ecological and environmental sciences206,207.

Centralized training and knowledge sharing within research organizations is helping to spread citizen science practices across disciplines, for example at the Center for Citizen Science at the University of Zurich, providing new opportunities for cross-disciplinary research. Practitioner-oriented knowledge sharing platforms such as EU-Citizen.Science, CitSci.org, and the AfriAlliance Knowledge Hub are facilitating knowledge exchange across institutions and regions. There are emerging networks and associations of citizen science practitioners at the national, regional and global level — especially in the Global South, including the Citizen Science Africa Association, CitizenScience.Asia and the Iberoamerican Network of Participatory Science (RICAP) — further supporting the sharing of knowledge and skills, and fostering collaborations across disciplines and across borders. In the coming years, these associations are likely to continue to expand into underrepresented countries and regions to connect grassroots practitioners with the wider community of practice, bringing in insights from unique geographic contexts and diverse stakeholder groups. . This is extremely important, not only for social inclusion, but also for reaching those parts of the world where there are the biggest data gaps in environmental knowledge. Achieving this requires significant investment in citizen science, as well as appropriate guidance for setting up initiatives in such settings.

These connections are particularly important, as is support for bottom-up initiatives. Although we have focused on participatory citizen science projects in this Primer, which are often initiated by institutional scientists to crowdsource data collection or processing, many high-impact examples arise from grassroots initiatives that challenge established citizen science paradigms208. For example, concerned citizens founded Public Lab209 and Safecast210 in response to the 2010 Deepwater Horizon oil spill and the 2011 Fukushima Daiichi nuclear disaster. With no connection to academic or institutional actors, both initiatives have grown into global citizen science networks that enable communities to seek environmental and social justice.

We have similarly focused in this Keynote on the scientific value of citizen science approaches, but it is important to note that citizen science is also recognized to have educational value30,38,42, environmental value35,66 and societal value211 ,212. For initiatives with an ecological or environmental focus, six pathways were identified through which citizen science approaches can have a positive impact on the issues examined, namely, providing insights for better environmental management, provide data evidence for policy making, encourage behavior change through increased awareness. and support a social network, empower political advocacy and community action213.

The number and range of these environmentally impactful projects are likely to increase in the coming years, including those initiated by grassroots community groups and in collaborative multi-stakeholder partnerships from scratch. As mentioned by ref.214 when advising on a too narrow definition of citizen science, the application of citizen science approaches goes beyond the development and testing of hypotheses, and includes activities related to environmental observations and solving complex problems, among other things.

Some of the barriers to citizen science becoming a more mainstream research practice include low levels of awareness of the value and impact of citizen science, lack of support and recognition for career researchers to pursue a citizen science approach, and low access to research funding215 . Recommendations from the European community for citizen observatories to address issues of awareness, acceptability of citizen science data and long-term sustainability of citizen science initiatives can be summarized in five main areas. These are (i) developing effective multi-stakeholder alliances and communities of practice for knowledge exchange, (ii) developing strong data value chains aligned with existing standards, (iii) fostering a sustainable growth market for citizen science through give the data requirements. local authorities and policy makers, (iv) further develop open access tools and technologies, and (v) integrate citizen science data with official data frameworks and open data systems216.

Another powerful opportunity to mainstream a citizen science approach within scientific research and knowledge production is the global shift towards open science, which embeds public engagement with science alongside other key pillars of open science such as open access, FAIR data and open education. At the 40th session of the UNESCO General Conference in 2019, the 193 Member States unanimously adopted the Recommendation on Open Science, which contained specific proposals to improve societal access to science by increasing collaboration between scientists and societal actors, and the making science more participatory, inclusive and inclusive. accessible to all members of society through new ways of collaboration such as crowdfunding, crowdsourcing and scientific volunteering28.

In 1998, the Aarhus Convention217 was adopted, giving people in Europe the right to participate in environmental decision-making. In 2021, two new legal instruments were ratified within the Aarhus Convention to support citizen science at the national governance level: namely, the recommendations on the more effective use of electronic information tools218, which expressly promote citizen science as a way to collect environmental information; and in the appendix to the recommendations219, which describes the value of citizen science and citizen observatories, and expressly recommends the PPSR-Core data set and metadata standards for citizen science initiatives and public participation in scientific research.

Citizen science initiatives are also providing data that informs policy and underpins decision-making at local, national, regional and global scales, for example, contributing directly to the monitoring of the United Nations’ Sustainable Development Goals (SDGs), where at least 33 % of it. its 231 unique indicators can be supported by citizen science data1. In addition, citizen science data can help address looming data gaps, such as 58% of the 93 environment-related SDG indicators, which lack sufficient data to assess global progress220. Examples of good practice — for example in Ghana, where citizen science data on beach litter are integrated into official SDG relevant indicator monitoring and reporting — are now emerging and demonstrate the potential of citizen science in SDG reporting in developing countries221.

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• The work of C.B.H. supported by a Brandeis University Provost Research grant. The work of D.F. supported by the European Union Vision 2020 Research and Innovation Program EU-Citizen.Science (under grant agreement number 824580). The work of F.D. with support from the European Union Vision 2020 Research and Innovation Program INTAROS, CAPARDUS and FRAMEwork projects (under grant agreement numbers 727890, 869673 and 862731), and the Danish Agency for Science and Higher Education through the Urgent Thematic Network on Collaborative Resource Management. The work of G.H. support from the European Union Vision 2020 research and innovation program EU-Citizen.Science and FRAMEwork (under grant agreement numbers 824580 and 862731). The work of J.M.H. is supported by AFRI Postdoctoral Fellowship grant 2020-67034-31766 from the USDA National Institute of Food and Agriculture. JP’s work was supported by the European Union H2020 Research and Innovation Program Cos4Cloud project (under grant agreement number 863463); JP also acknowledges the accreditation of the ‘Severo Ochoa Center of Excellence’ (CEX2019-000928-S). The work of M.H. received support from the European Union ERC Advanced Grant project ‘European Citizen Science: Analysis and Visualisation’ (under grant agreement number 694767); and the European Union Vision 2020 Research and Innovation Program projects EU-Citizen.Science and TIME4CS (under grant agreement numbers 824580 and 101006201). The work of M.T. support from the MINKE project of the European Union’s Vision 2020 Research and Innovation Program (under grant agreement number 101008724). The work of P.-Y.H. with support from P. Stephens, Department of Biological Sciences, Durham University, UK; Belmont Community School, Durham, UK; Durham Wildlife Trust, Durham, UK; UK Heritage Lottery Fund (grant numbers OH-14-06474, OM-21-00458 and RH-16-09501); of Brea thin Ecological Association; UK Economic and Social Research Council Impact Acceleration Account, Durham University (grant numbers TESS–ESLE2012 and 030-15/16, and Doctoral Scholarship); UK Natural Environment Research Council (NE/R008485/1); the European Food Safety Authority (grant numbers OC/EFSA/ALPHA/2016/01–01 and OC/EFSA/AMU/2018/02); HMP and YOI Operational Innovation Award, and the Royal Society (grant number PGS2192047).
• Novel Data Ecosystems for Sustainability Research Group, Advanced Systems Analysis Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
• Dilek Fraisl & Gerry Hager
• Center d’écologie et des sciences de la conservation, Musée national d’Histoire naturelle, Paris, France
• Faculté de droit, University of Namur, Namur, Belgium

Citizen Science Laboratory, Leiden University, Leiden, The Netherlands

How can we as responsible citizens protect the environment essay?

Department of Mechanical Engineering, University of Bath, Bath, UK

Why are citizen science projects important?

Nordic Foundation for Development and Ecology (NORDECO), Copenhagen, Denmark

Department of Biology, Brandeis University, Waltham, MA, USA