We collected information through an online questionnaire, available as Supplementary Material, aimed at the ILTER network. To draft and validate it, we held a workshop at the 2019 ILTER Open Science Meeting and asked the 14 attending science professionals to create a preliminary list of reasons why scientists would participate in CS, as well as a list of associated challenges. Building from this and related literature and inventories (e.g., Riesch et al., 2013; Golumbic et al., 2017; Tredick et al., 2017; Besley et al., 2018; Robertson Evia et al., 2018; Stylinski et al., 2018), the first version of the questions was developed and then pilot-tested by a group of 14 environmental scientists who were not involved within ILTER (10 were non-native English speakers). Based on the feedback from the pilot scientists, the questionnaire was revised and implemented on Qualtrics ³ , an established online questionnaire tool. The different phases of the survey development are shown in Figure 1.

The final online questionnaire consisted of 35 single-answer/multiple-choice, multiple-answer/multiple-choice or open-ended questions, subdivided as follow.

1) Attitudes of scientists towards CS and other public engagement (4 questions on communication objectives, reasons, willingness and barriers)

In this paper, we focus on the analysis of the inventory and demographic items and on the level of involvement of Citizen Scientists, while the others are described and analyzed in L’Astorina et al., 2023. To provide an in-depth viewpoint, the questionnaire respondents were asked to focus only on one specific CS initiative (current or past) in which they have been most active and to answer a number of questions to describe it. Questions addressed the following features.

i) Spatial and temporal scale of the CS initiatives

The questionnaire was accompanied by an informative email specifying the purpose of the study and the use that was to be made with the data that the respondents provided. Respondents were free to choose whether or not to answer any questions. The average survey duration was approximately 20 min.

A recruitment email with a link to the questionnaire was sent to all the ILTER site managers through the ILTER secretariat contact list, which encompasses 850 email recipients. The email stated the purpose of the study and asked recipients to complete the questionnaire and share the link with other scientists at their ILTER site/platform. The questionnaire remained open from the end of February to mid-September 2020, with two reminders sent within this period.

In total, we received 163 responses with completeness higher than or equal to the 75% (i.e., all of these respondents completed at least the 75% of the questionnaire). We assumed that site managers either filled out the questionnaire or passed it on to a scientist at their site. Thus, our pool of possible respondents was 850, and our response rate is 17%. This response rate appears consistent with those reported for other online questionnaires of expert communities (e.g., Scott et al., 2011; Dudo and Besley, 2016).

We used bar plots to visualize the percentage of responses for each feature of the selected CS initiatives and a matrix plot for the level of involvement of the citizens in the different activities. In the bar plots, each bar represents the percentages and the number of respondents that selected multiple choice option. We conducted mean comparisons (paired two-samples Wilcoxon test) to test for significant differences between the various demographic groups. Geographical distribution of CS activities was represented per bio-geographical region (see Olson et al., 2001 for the full list of regions). We also looked for common themes in the open-response questions. The statistical software R (version 4.1.2) was used for all the analyses (R Core Team, 2021). The performed analyses by using the R software are available as open code on GitHub (Oggioni and Bergami, 2022), while the questionnaire results are accessible on Zenodo (Bergami et al., 2022).

We compared the six main features of the ILTER CS initiatives with the “Ten Principles of Citizen Science,” which cover a broad range of characteristics of CS, from scientific outcomes and benefits for the citizens to data sharing and ethical issue. To simplify this comparison, we grouped the Ten Principles into three key issues, covering several principles and connected to the six features: i) Scientific outcomes and societal/policy impact, ii) Types of volunteers’ involvement, iii) Sharing of data and findings, feedbacks and acknowledgements (Table 1; Figure 2).

The most common demographic selections were male (61%), ILTER site managers (38%) and senior scientists (44%) and aged between 50 and 59 years (25%) (Table 2). Forty-seven percent of the respondents declared that they have been involved in at least one CS initiative during their scientific career. The total number of current and past CS initiatives carried out at the LTER site or LTSER platform by all the respondents was 392 with an average of 4.6 per respondent. Seventy-six were the selected CS initiatives of which the respondents described the main features.

Eighty-three percent of the respondents gave information about geographic context, which was useful for defining the provenance ILTER network. They primarily worked at LTER sites/platforms in Europe (58%) with additional respondents working in the US (10%), East-Asia-Pacific (7%), Central and South America (4%) and Africa (3%) (Figure 3). Most of the initiatives were distributed in Temperate and Mediterranean climate biogeographic regions (79%) with only 16 in other climate regions: two in Subtropical Arid climate, seven in Humid climate (at equatorial or tropical latitudes), four in Boreal climate and three in Warm Temperate climate (Figure 3).

No statistically significant relationships were detectable in the responses between the various demographic groups.

CS initiatives have a genuine science outcome, and this distinguishes them from purely education and outreach programs (Bonney et al., 2009). At the same time, it is very relevant their—real or potential—societal and policy impacts, even though this is not always properly developed and evaluated (Robinson et al., 2018; Turbè et al., 2019; Stylinski et al., 2020).

As expected from the conceptual background and overall motivations of the ILTER network (Mirtl et al., 2018), the main research foci of the ILTER CS initiatives were biology and environmental science. The scientific questions addressed biodiversity, mainly the distribution of specific groups of organisms, but also water quality, ecosystem services and climate change (e.g., effects, mitigation). The ILTER community seems therefore to diverge, at least partially, from what is reported by reviews of environmental CS projects in other context, where the monitoring of biodiversity dominates (Schade and Tsinaraki, 2016; Pocock et al., 2017; Hecker et al., 2018; Turbè et al., 2019). Indeed, ILTER CS initiatives focusing on the ecological state and on the use of natural resources (air, water, land), and on ecosystem services and environmental management, conservation and protection are fairly well represented.

All the initiatives described in the questionnaire are potentially relevant for addressing a number of policy and conservation issues, such as biodiversity targets achievement and environmental Directives implementation. Furthermore, research conducted at the ILTER sites and platforms often addresses critical questions regarding natural resources management so that the data and knowledge produced are fundamental and irreplaceable for documenting long-term trends in environmental conditions and making for policy decisions at the local, regional and global levels (Hughes et al., 2017; Mirtl et al., 2018). In particular, ILTER may play a major role in global monitoring and evaluation frameworks, such as the Aichi targets, the UN SDGs, and the Sendai Framework. Given this, the involvement of citizen scientists into the scientific processes has the potential to increase policy impacts of ILTER studies. Overall, CS is emerging as a practice that effectively contributes to all the aspects of the policy process (Turbè et al., 2019); this includes carrying out research at scales that would not have been possible otherwise and early detection of a range of environmental issues, as well as highlighting new issues for decision makers and promoting policy implementation through CS monitoring programmes. However, it can be arduous to evaluate the uses of findings for policy and decision-making or to attribute this use to a specific policy area (e.g., Hyder et al., 2015; Turbè et al., 2019). ILTER could be in the position to reinforce the effectiveness of CS for policy, mostly by fostering CS projects that collect data over broad spatial and temporal scales, so that policymakers would have evidence-based knowledge that can be used to serve multiple objectives and meet current and future policy objectives. Most ILTER CS initiatives reported here do not contain these characteristics; that is, they are primarily implemented at the local scale (at one LTER site only) or, more rarely, at the regional scale (at several LTER sites in the same region) and are short-term with a mean duration of 4 years. Thus, a key value of ILTER (i.e., its broad-scale) does not emerge from our study. This is very likely the consequence of the lack of harmonized views, guidelines and dedicated funding for CS at ILTER. This leaves to each site or network own responsibility for running the project, bringing the constraints typical of short-term projects, failing the target to create and sustain a stable long-term citizen engagement.

At the same time, ILTER sites are in the position to powerfully address local scale, community-based initiatives to support not only science, but also policy and community actions, through co-designed or bottom-up projects. However, as described in the next paragraph, less than 10% of volunteers are engaged in the stage of CS project design, definition of hypothesis and data analysis: there is still much to be done to include citizen participation in all steps of the scientific process and to realize the full social potential of CS in terms of citizen empowerment and transformative capacity.

The patterns of volunteer participating in CS activities may give important clues about the level of representativeness of the wider society in science and about possible bias concerning age, gender, education, socio-economic status and other factors in CS overall (National Academies of Sciences, 2018; Pateman et al., 2021). CS initiatives often engage volunteers who are prevalently men, people identifying as from white ethnic groups, have high socio-economic status, and are in education at school, college, or university (Pateman et al., 2021). Surprisingly, participating ILTER scientists report that more than 50% of their volunteers are members of underserved communities, in particular those living in rural areas and having limited financial resources. A likely explanation is that most LTER sites are located in natural/rural areas (Wohner et al., 2021), where the local communities may fall mainly into the “underserved” category. The involvement of this kind of volunteer suggests that ILTER CS studies are potentially able to incorporate place-based knowledge provided by volunteers into the scientific process.

Almost two-thirds of the respondents stated that the volunteers are involved mainly in “helping collect samples or record data.” Less than 10% of volunteers are engaged in the project design phase, definition of hypothesis and data analysis. This matches other studies (e.g., Phillips et al., 2018; Turrini et al., 2018; Stylinski et al., 2020); it limits opportunities for a meaningful dialogue between science and society, which could foster a greater sense of ownership among volunteers and benefit the research by incorporating local knowledge and expertise (Corburn, 2007). There are likely a number of reasons for this result. For example, scientists may maintain more traditional views of public engagement outcomes (e.g., focused on knowledge gains rather than mutual exchanges), or they may perceive data collection support aligns best with the research needs and ways in which volunteers can realistically contribute (Gray et al., 2017). That said, a large percentage of the ILTER scientists (30%–40%) listed several other types of involvement of volunteers, including dissemination of the project conclusions, discussion and translation of the results into action and applying them to new questions. This indicates that some ILTER scientists have a broader view of the volunteers’ contribution and greater commitment to more collaborative aspects of CS; this was also apparent in findings reported in L’Astorina et al., 2023.

Almost the totality of the volunteers in ILTER initiatives were trained using a variety of methods, such as face-to-face workshops, written instructions, and online tutorials. This is noteworthy since an appropriate degree of structured training is considered one of the essential characteristics of CS (Haklay et al., 2021) and one of the ways to ensure high data quality (Kosmala et al., 2016), while also meeting education goals of CS efforts. Training of citizens should lay the essential foundations for the good quality of the data gathered, even though the leaders of the CS initiatives will always have the responsibility to control, measure and report data quality and quality assurance procedures to demonstrate the validity and reliability of the data (Williams et al., 2018). This will also help to overcome one of the barriers that hampers the integration of CS into policy, that is the concern about data quality, interoperability, and access, and mistrust of non-traditional data sources (Kosmala et al., 2016; Balázs et al., 2021).

Data reliability and data quality in CS projects has been and continues to be discussed within the scientific community (e.g., Galloway et al., 2006; Silvertown, 2009; Hunter et al., 2013; Kosmala et al., 2016; Balázs et al., 2021) and is considered to be the main barrier to engaging scientists in CS projects (e.g., Riesch et al., 2013; Burgess et al., 2017; Golumbic et al., 2017).

For the ILTER CS initiatives, there is room for improvement in terms of the data accessibility, as less than half of the CS initiatives make the data produced fully accessible through online repositories after being validated by experts. Considering the growing tendency of the ILTER community to embrace an open science approach, following the expectations of a global research infrastructure (Mirtl et al., 2018), further steps are required to increase the dissemination of best practices for the promotion of open access and the set-up of adequate data infrastructure. ILTER could foster centralized access to the CS resources, creating a data and knowledge platform, which could also allow sharing tools and best practices all over the network with harmonized and interoperable metadata and data standards.

Questionnaire respondents most often reported sharing research findings with CS volunteers and other stakeholders via in-person meeting or informal online media only (e.g., reports, newsletters, email), which is congruent with outcomes from other studies (Theobald et al., 2015; Kullenberg and Kasperowski, 2016; Burgess et al., 2017; Turrini et al., 2018). Results published through these channels are relevant, since they may reach important local stakeholders; however, the rarer use of scientific publication, could preclude CS from really fulfilling the goal of advancing science as well as gaining grounds in more traditionally oriented research organizations.

When publishing peer-reviewed publications from their CS projects, almost all of the ILTER scientists cited their volunteers directly in the acknowledgments or by describing their contribution in the material and methods; a few even listed them as coauthors. Overall, this is an encouraging result, since it is proved that giving good feedbacks may provide many benefits (Robinson et al., 2018 and reference therein); in particular, it is a way to show volunteers that their contribution is meaningful, providing motivations for participation in other projects and reinforcing connection and trust between citizen scientists and professional scientists. It would be particularly useful to help improve the practice of CS scientists to describe how they recruited, trained, supported and provided benefits to volunteers in the methods section of their scientific papers (Davis et al., 2022).