Scientists’ Perceptions of Community and Citizen Science in the Arctic and Sub-Arctic: Benefits, Challenges, and Motivations

Understanding scientists’ perceptions of community and citizen science

The participatory sciences research community is increasingly interested in research on the processes and outcomes of CCS (Jordan et al. 2015). While there has been emphasis on documenting participants’ motivations and learning outcomes in the participatory sciences literature (West and Pateman 2016; Phillips et al. 2018), less attention has been paid to scientists’ perceptions of CCS research. The studies that do exist highlight that scientists mention gaining access to more data through CCS research (Collins, Sullivan, and Bray 2022; Carson and Rock 2023; L’Astorina et al. 2023), though they are concerned about the quality of data gathered by non-scientists (Riesch and Potter 2014; Golumbic et al. 2017; Bína et al. 2021). Scientists are more likely to consider engaging in forms of CCS research when they see the benefit to themselves and others, and perceive institutional support (Lenart-Gansiniec, Czakon, and Meyer 2024). Some scientists who have reported engaging in CCS work have experienced positive personal and career impacts (L’Astorina et al. 2023).

Understanding scientists’ perceptions of the benefits and challenges of CCS and their motivations for using this approach can inform how CCS is introduced to graduate students, early career researchers, or scientists who are new to the field (Golumbic et al. 2017; Rios and Neas 2024). Many forms of CCS in the North are cross-cultural across several different tribes, language groups, and races, and involve different types of partnerships between universities and Indigenous communities (David-Chavez and Gavin 2018). Understanding scientists’ motivations can inform the design and support structures for maximizing the benefits of CCS to Arctic Indigenous communities and for reducing the potential for extractive colonial science. Examples of design and support structures could include implementing co-produced research programs (Spellman et al. 2018; Clement et al. 2023) or data governance structures (Jennings et al. 2025).

In this study, we examined scientists’ perceptions of CCS research in the North, focusing on those who actively use CCS approaches in their work. We focused on the Arctic because the shift in Arctic research toward prioritizing both large-scale research and local collaboration means that collaboration, including co-production of knowledge with Indigenous communities, is becoming a standard practice for conducting research (Druckenmiller 2022). Understanding scientists’ perceptions of CCS in the North will help gauge the extent to which this intended transformation of Arctic research culture may be occurring. The research questions that guided this study were:

1. What do scientists perceive to be the benefits and challenges of CCS in the North?

2. What motivates scientists to engage in CCS in the North?

Study participant recruitment

We invited scientists who self-identified as CCS practitioners in the North to participate in an online survey about their experiences with using CCS research methods, followed by an optional interview. We used a purposive sampling design (Creswell 2014), recruiting from two professional groups known to include our target population. The survey was distributed using Qualtrics software via email lists for each group (the Association for Advancing Participatory Sciences and the Arctic Research Consortium of the United States) in November 2022. We distributed postcards with the survey link at two professional meetings in 2022 (the Navigating the New Arctic Community Office Annual Meeting and the American Geophysical Union Fall Meeting). Finally, we contacted colleagues conducting CCS research in the North to encourage participation in the survey. The survey was open from November 2022 to April 2023, and 47 surveys were returned. Survey respondents were given the option to record their contact information for a follow-up interview. In total, 25 respondents participated in interviews, which took place between January and September 2023. The interview data are the subject of this study, and survey data will be used in future manuscripts and reports. Survey respondents gave consent to participate in the survey and to be contacted for an interview if they recorded their contact information. Consent for the interview was obtained verbally before each interview began. The research protocol was approved by the University of Alaska Fairbanks Institutional Review Board (#1962261).

Data collection

In the semi-structured interview protocol (Supplemental File 1), respondents were asked to reflect on their definitions of CCS research and the benefits and challenges of conducting CCS research. They were asked what motivated them to conduct CCS research, and what goals they had for engaging participants in the work. Two transcripts were excluded from the final dataset because the discussion deviated substantially from the research questions. The demographics of the 23 scientists whose transcripts were included in the final dataset and their projects are described in Tables 1 and 2.

Data analysis

The first and second authors coded interview data using Dedoose software (https://dedoose.org). We used a deductive approach (Saldaña 2021) to code a subset of four interviews together with three a priori codes: benefits, challenges, and motivations (Supplemental File 2). Benefits referred to the general advantages of using CCS methods. These benefits were not necessarily advantages that the scientists personally experienced. Challenges described issues that scientists perceived themselves or others might encounter in the work. Motivations covered scientists’ personal reasons for using CCS research methods in their work. We coded each excerpt as a benefit, challenge, or motivation based on the question to which the excerpt was responding (e.g., “What do you see as the general benefits of CCS research?”). We coded the excerpts using in vivo (codes using scientists’ exact words) and descriptive (codes that briefly summarize the excerpt’s topic) approaches (Saldaña 2021). The first author created memos about the initial codebook to identify themes across the benefits, challenges, and motivations codes.

Five themes characterizing the scientists’ experiences with CCS emerged across the benefits, challenges, and motivations codes. The two authors discussed the scope of each theme until they reached agreement. The first author added theme codes to the existing excerpts coded as benefits, challenges, or motivations. Both authors separately recoded a subset of five interviews in a subsequent reliability check. We applied pooled Cohen’s kappa to check our consistency in applying theme codes, reaching an interrater reliability agreement of .72, which is considered “good” agreement (Cicchetti 1994). The first author recoded the remainder of the interviews. In some cases, excerpts were coded as multiple themes or as two of the benefit, challenge, or motivation codes, such as when an aspect of CCS methods was described as a benefit and a challenge, or a challenge that was a source of motivation.

We used non-parametric tests of association to determine if there were any patterns between the number of CCS projects scientists worked on or the number of years they lived in the North and their perceptions of CCS research. We created contingency tables crossing whether each of the five themes were coded as benefits, challenges, or motivations with the number of projects in which scientists engaged CCS methods and years living in the North (30 contingency tables; five themes x three codes [benefits/challenges/motivations x two demographic groups [number of CCS projects/years living in the Arctic]) and applied the Fisher-Freeman-Halton Exact test using SPSS v. 31. Though our sample was small, we hoped to identify patterns worth examining in future research.

Benefits

Respondents most often associated the benefits of CCS research with the theme of scientist-participant relationships, followed by actionable science. Within scientist-participant relationships, the benefits scientists described for participants included access to science research, opportunities to learn, and growing their enthusiasm for science. One respondent said, “I think it’s beneficial to the people who are engaging. They’re hopefully learning something, they’re having fun, and they’re interacting, and they feel like they can have that connection with the [scientist]” (P6).

Multiple scientists described the benefit of participants contributing their knowledge and experience to the research. One scientist said, “They just really understand their system. They really understand their problems. I was just really blown away by the scope and quality of the research questions” (P2). Another said,

Within the theme of actionable science, scientists described the ability of CCS to enable decision-making and planning in Northern communities. One participant said,

Providing communities other resources such as compensation, employment, or technology was also mentioned as a benefit of CCS. One scientist said,

Scientists talked about the benefits of cross-cultural knowledge co-production contributing to science research through different perspectives. One scientist described improving the research process for communities, saying,

The most common data benefit scientists mentioned was the ability to access more of it: “[CCS] helps us develop a more dispersed and perhaps robust network that we can interact with. We don’t necessarily have to be in all these places to get data” (P9). Several scientists also described a positive culture shift in CCS away from extractive models of data collection, and toward data sovereignty in Indigenous communities. One said,

Approximately one-quarter of scientists described the interdisciplinary nature of CCS research as a benefit, primarily because the topics their research sought to address were multi-faceted. One scientist said,

Challenges

Scientist-participant relationships and cross-cultural knowledge co-production were the two themes most often associated with challenges in using CCS methods. The common challenge between the two themes was maintaining long-term relationships with Indigenous communities. Some scientists described issues with competing priorities affecting the degree to which people could participate. One scientist acknowledged, “their priority might be something happening, a festival, or hunting, or something happening at home” (P23). Two scientists cited challenges with maintaining relationships during times of staff turnover in communities. One said,

Other challenges within the scientist-participant relationships theme included recruiting and retaining participants and coordinating large and geographically dispersed teams. To retain participants, one scientist said,

Within the cross-cultural knowledge co-production theme, challenges included addressing histories of extractive research practices in communities, developing trusting relationships, navigating time constraints, and facing pressure to engage communities in research. One respondent said,

Scientists also described the challenges of not having enough time to engage in knowledge co-production appropriately, both at the beginning and end of projects. Several talked about the long spin-up time required to develop relationships with communities before beginning the co-production process. One scientist talked about how lack of time at the end of a project can negatively impact communities, saying,

Several respondents described pressure from the larger scientific community to do community-engaged research, which they worried would lead to further harm. One said, “There’s a lot of scientists who mean well, but don’t fully understand how to meaningfully conduct a co-produced project” (P3). Another said,

Scientists described fewer challenges within the data theme compared with most other themes. Challenges included anticipating and addressing data quality issues, disappointment that CCS data is not well respected by the larger scientific community, and overcoming design issues with data submission portals. One scientist shared:

Motivations

Scientists’ personal motivations to engage in CCS research were most frequently associated with actionable science and scientist-participant relationships. Scientists’ motivations related to actionable science focused on seeing the difference their CCS research made for the people and communities they worked with. One scientist said they liked using CCS methods because:

Others focused on using their science to help with resource management. One scientist said,

Multiple scientists described being motivated to do CCS research because they liked meeting and working with new people. One scientist said, “That’s one of the reasons I wanted to do this project, because I knew that I would learn a lot of things just from talking to people who are out and seeing things that have a lot of experience” (P1).

Within the cross-cultural knowledge co-production theme, several scientists described their motivation to braid Indigenous knowledge and Western science. One scientist said, “I was curious about [the program] and how did that work to bridge knowledge systems, like, how does someone actually do that?” (P20). Some scientists also expressed a motivation to travel to engage communities in CCS research. One said, “I don’t know if this is my prime motivator, but one of the great things I get to do is travel to [the community], which I love” (P10).

Motivations related to data focused on accessing more data and managing data efficiently. As with scientists who described expanded datasets as a benefit, one scientist said:

“Making a difference” is both a benefit and a motivator

The theme of actionable science, primarily the idea of “making a difference,” was described both as a benefit of CCS research and a motivator for scientists. Several scientists noted in their interviews that “science for science’s sake” was not as appealing to them as research that produced usable data, products, and resources for their partners. Coordinators engaging in CCS with marginalized and Indigenous communities similarly reported that their programs emphasized local community benefits over advancing generalizable scientific research (Benyei et al. 2023). The scientists we interviewed discussed tangible benefits of CCS like sharing financial, technological, or human resources to address communities’ environmental issues. Using funding from flexible sources that allow resource-sharing and participant compensation to extend the impact of CCS research beyond scientific findings has been recommended elsewhere in the literature (Doering et al. 2022; Benyei et al. 2023).

No scientist in our sample identified challenges within the actionable science theme. They identified actionable science benefits for their partners, such as using CCS data to make planning or management decisions, but did not discuss challenges associated with ensuring those benefits were realized. One study evaluating scientists’ needed competencies for conducting actionable science suggested that scientists are more concerned about challenges in the process of actionable science than challenges related to outcomes or products, which are often better defined (Goolsby et al. 2023). It is also possible that scientists in our sample had an overly optimistic vision of the impacts their research could achieve (Owen 2021). The desire to use CCS to make a meaningful difference is closely tied to the concept of response efficacy, or the belief in one’s ability to effect change. Response efficacy has been identified as a key factor influencing scientists’ willingness to engage with the public and produce actionable science (Besley et al. 2018). Further research examining CCS researchers’ response efficacy or their theories of change (their ideas about how their research contributes to societal impacts) could help identify how they perceive their own roles in ensuring that actionable science benefits are realized in their work.

Scientists focused on the benefits of CCS data

Expanded datasets are a main feature of CCS research, though issues with data quality have been frequently identified as pressing problems to address (Riesch and Potter 2014; Kosmala et al. 2016). When asked to identify the benefits of CCS research methods, more than half of the scientists we interviewed (57%) described data. Challenges with data, including quality and infrastructure management, were described by 26% of respondents.

The finding that most scientists in our sample identified data as a general benefit of CCS research aligns with several other studies and seminal papers defining the practice of CCS (Silvertown 2009; Bína et al. 2021; Collins, Sullivan, and Bray 2022; Finger et al. 2023; Carson and Rock 2023). The scientists we interviewed most commonly identified having access to more data as a benefit, though several scientists also described the benefit of using CCS methods to collect data designed to meet their partners’ needs. While previous studies have demonstrated scientists’ perceptions of CCS data quality, value, and utility (e.g., de Sherbinin et al. 2021; Collins, Sullivan, and Bray 2022; Carson and Rock 2023), challenges with data, including acquiring high-quality data, were not prominent in scientists’ interviews. This may be because the North is geographically vast and data-sparse, and therefore the quality of CCS data may be better than available data, if any exist at all (Spellman and Mulder 2016; Schwoerer et al. 2021). Additionally, 91% of scientists in our sample reported using collaborative or co-created CCS research methods, including hybrid models that incorporated these approaches; this group included every respondent who described working with Indigenous communities. It is possible the scientists did not comment on data quality issues because it was their partners, not them, who were defining the standards the data needed to meet. Future research could investigate how scientists in the North address data challenges they may encounter when using CCS research methods in their work.

Responsible cross-cultural knowledge co-production and relationships are challenges scientists want to meet

We found that while scientists identified cross-cultural knowledge co-production and scientist-participant relationships as challenges in their work, they framed those challenges as important and fulfilling to address, rather than as reasons to disengage from using CCS research methods. Indigenous researchers and community members have provided context for these challenges, including long histories of colonial institutions like universities conducting research on Indigenous communities without free, prior, and informed consent (Bahnke et al. 2020; Smith 2021; David-Chavez et al. 2024). In the Arctic, scientists engaging in extractive research practices like withholding free, prior, and informed consent have contributed to a modern “structure of decision making that does not fully account for Indigenous Peoples’ knowledge, perspectives, or needs” (Yua et al. 2022, p.3). This approach to research, in which Indigenous communities are framed through a deficit lens, does not foster trust in scientists or research institutions within Indigenous communities (David-Chavez et al. 2024). Pathways to building trust through reciprocal relationships with Indigenous partners over sustained periods of time have been described throughout the contemporary guidance for Arctic research (Yua et al. 2022) and other parts of the world (Stevens et al. 2014), though scientists and institutions need to continue efforts to improve (Gardner-Vandy et al. 2021).

Challenges to building reciprocal research relationships with Indigenous communities that scientists in our sample described included aligning research activities with community priorities and feeling external pressure to co-produce research with communities. Many resources exist for individual scientists to improve their relationship-building efforts with Indigenous communities, including step-by-step guides and specific action recommendations (Hird et al. 2023; O’Brien et al. 2024). Efforts to support relationship-building at the institutional level could include requiring graduate students or scientists who are new to Arctic CCS research to participate in formal training like the “Arctic Research is Relationship” course at Alaska Pacific University (NNA-CO 2023) or mandating the use of FAIR and CARE data governance principles (Jennings et al. 2025) to be eligible for funding that encourages or requires CCS methods.

Changes to research funding structures could also better support scientists’ relationship-building with Indigenous communities. Scientists in our sample mentioned creating opportunities to extend the research at the beginning and end of projects as a needed innovation in funding structures, which is supported elsewhere in the literature (Doering et al. 2022; Rudolf et al. 2025). Creating flexibility within the terms of grants to use funds for relationship-building work prior to research was also described as a needed change in funding structures by scientists we interviewed. Grant flexibility is critical to support shared decision-making between scientists and partners and to support community needs and scientific outcomes (Ford et al. 2015; Rudolf et al. 2025). There are examples of funding agencies adapting to provide more support for early phases of developing research relationships, such as when the National Science Foundation in the United States altered the proposal solicitation timeline for their Navigating the New Arctic program to provide more time for relationship building and collaboration (Easterling 2021). It is important to note, however, that funding agency priorities are subject to the political climate, and scientists can work to foster reciprocal research relationships with Indigenous communities even within rigid grant structures as funding mechanisms continue to evolve (Herrmann et al. 2023).