Citizen science data reliability enhancing scientific research: insights from an 11-year study in the Mediterranean Sea

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The quality of data produced by citizen science (CS) programs has been called into question by academic scientists, governments, and corporations. Their doubts arise because they perceive CS groups as intruding on the rightful opportunities of standard science and industry organizations, because of a normal skepticism of novel approaches, and because of a lack of understanding of how CS produces data. I propose a three-pronged strategy to overcome these objections and improve trust in CS data. Develop methods for CS programs to advertise their efforts in data quality control and quality assurance (QCQA). As a first step the PPSR core could incorporate a field that would allow programs to point to webpages that document the QAQC practices of each program. It is my experience that many programs think carefully about data quality, but the CS community currently lacks an established protocol to share this information. Define and implement best practices for generating biodiversity data using different methods. Wiggins et al. 2011 published a list of approaches that can be used for QCQA in CS projects but how these approaches should be implemented has not been systematically investigated. Measure and report data quality. If one takes the point of view that citizen science is akin to a new category of scientific instruments, then the ideas of instrument measurement and calibration can be applied CS. Scientists are well aware that any instrument needs to be calibrated before its efficacy can be established. However, because CS is new approach, the specific procedures needed for different kinds of programs are just now being worked out for the first time. Develop methods for CS programs to advertise their efforts in data quality control and quality assurance (QCQA). As a first step the PPSR core could incorporate a field that would allow programs to point to webpages that document the QAQC practices of each program. It is my experience that many programs think carefully about data quality, but the CS community currently lacks an established protocol to share this information. Define and implement best practices for generating biodiversity data using different methods. Wiggins et al. 2011 published a list of approaches that can be used for QCQA in CS projects but how these approaches should be implemented has not been systematically investigated. Measure and report data quality. If one takes the point of view that citizen science is akin to a new category of scientific instruments, then the ideas of instrument measurement and calibration can be applied CS. Scientists are well aware that any instrument needs to be calibrated before its efficacy can be established. However, because CS is new approach, the specific procedures needed for different kinds of programs are just now being worked out for the first time. The strategy outlined above faces some specific challenges. Citizen science biodiversity programs must address two important problems that standard scientific entities encounter when sampling and monitoring biodiversity. The first is correctly identifying species. For citizens this can be a problem because they often do not have the training and background of scientist teams. Likewise, it may be difficult for CS projects to manage updating and maintaining the taxonomies of the species being investigated. A second set of challenges is the diverse kinds of biodiversity data collected by CS programs. For instances, Notes from Nature decodes that labels of museum specimens, Snapshot Serengeti identifies species of large mammals from camera trap photographs, iNaturalist collections images of species and then has a crowdsource identification processs, while eBird collects observations of birds that are immediately filtered with computer algorithms for review by the observer and if, subsequently flagged, reviewed by a local expert. Each of these programs likely requires a different set of best practices and methods to measure data quality.

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A recurrent complaint of members who attend ESA Annual Meetings is that the large number of concurrent sessions make it impossible to attend all the ones of interest. Symposium Reports from the ESA Annual Meeting is one response to this dilemma. They provide, for those who could not attend, an overview of the symposium presentations and the resulting discussion, as well as a convenient means to identify the presenters. And attendees can review the session! The Editor hopes these Reports are useful, and encourages future Symposium organizers to write Reports for the Bulletin when the presentations are given. For detailed instruction for contributions see: 〈http://esapubs.org/esapubs/journals/Bulletin.htm#Typ〉. Until recently if one were to ask "who is a scientist?" a common response would be, someone who works at a university or a government agency or in a laboratory. In other words, many of us tend to think of scientists as professionals who have been academically trained and conduct research through the auspices of a research institution, agency, nonprofit organization, or academic institution. Historically, though, individuals from outside this circle of professionals have been instrumental in shaping and contributing to science. In fact, some of the most renowned scientists and ecologists could be considered citizen scientists (e.g., Charles Darwin, Harold Mayfield, Alexander Skutch). But the view that the public could actively contribute to science faded greatly over the course of the 20th century (particularly in the United States) to such an extent that we were left with the view that only someone who was professionally trained could be a scientist. Recently, however, we have witnessed an increase in the extent and acceptability of public participation and engagement in science. In particular, over the past decade we have seen a marked increase in such "citizen science" (Fig. 1). Although explanations of citizen science vary slightly, they converge on this definition: the involvement of citizens from the nonscientific community in academic research (Trumbull et al. 2000, Lee et al. 2006). Historical trend of citizen science articles. Data represent peer-reviewed articles as identified in ISI Web of Science using the search term "citizen science" from 1980 through 2008. The number of unique articles (n = 55) published is indicated by "articles," and the number of unique citations (n = 353) is indicated by "citations." Because citizen science has seen a dramatic increase in recent years, both in terms of the number of participants and its spread into new disciplines, a symposium was held at the 2008 Ecological Society of America Annual Meeting to address the role of such activity in ecology. Eleven speakers from around the world converged in Milwaukee, Wisconsin, to share their work in "Citizen Science in Ecology: The Intersection of Research and Education," a symposium organized by Christopher Lepczyk, Owen Boyle, and Timothy Vargo. The goals of the symposium were to explore the following questions: (1) Is citizen science a new discipline, sub-discipline, or tool, relative to ecology? (2) Are data collected by citizen scientists valid, and if so, comparable to data collected by professional ecologists or their assistants? (3) Can citizen science be an effective tool to help bridge the gap between ecological research, communities, and education, both for the public and students? (4) Is citizen science the same as or different from ecological monitoring, or is one a subset of the other? (5) Are citizen scientists actively participating in the scientific process as ecologists, thus increasing their ecological literacy? To address these questions, speakers discussed citizen science both in general conceptual terms and in case-specific contexts from around the world. Rick Bonney of Cornell University opened the symposium with an overview of the history and evolution of citizen science in academic research. He explained that citizen science began as a series of monitoring projects designed to put the findings of hobbyists, such as bird watchers and star gazers, to meaningful scientific use. Following these early monitoring projects were ones designed with educational goals and even some set up as experiments. Eventually citizen science started to become an accepted technique for data collection in several scientific disciplines. Today, new citizen science efforts are involving participants in data analysis as well as data collection, and some are even starting to collect data from online images such as nestcams (readouts from recorders aimed at birds' nests). Following on the heels of the history of citizen science was a series of four case studies describing ecological research and monitoring projects that rely upon volunteers for their success. These case studies were arranged along a continuum from large-scale national projects with thousands of volunteers to regional and local projects. In addition, each case study represented varying degrees of interaction between researchers and volunteers. Leading off the case studies was David Ziolkowski of Patuxent Wildlife Refuge, who discussed how citizens drive the North American Breeding Bird Survey (BBS). Specifically, the BBS protocol conducts annual bird surveys along >4000 routes (Fig. 2) across the United States, Canada, and Mexico, using a highly skilled volunteer workforce. Part of the success of the BBS program has been its relatively straightforward field protocol and standardized design. Moreover, the BBS program has resulted in over 400 bird species being surveyed annually at a cost of less than $900 per species per year. Without citizen scientists, such accomplishments could not be achieved. Similarly, in the United Kingdom, a long-running insect monitoring project has been led by citizen scientists in conjunction with Rothamsted Research, the oldest agricultural research station in the world. Philip Gould highlighted how the Rothamsted Insect Survey has used light traps (Fig. 3) to capture insects across 460 sites in the UK for the past 50 years. This survey takes about five minutes each day to collect insects, which are then sent to Rothamsted Research for sorting and identification of the macro-moth fraction of the catch. To ensure a robust monitoring project, volunteers are reimbursed for any trap maintenance and provided with annual summaries of the moths collected from their trap. The success of the program has been built upon: (1) keeping the monitoring system simple; (2) ensuring that the volunteers are trained; (3) knowing when to discontinue sites; and (4) providing all volunteers with feedback on their work. The value of the insect survey was demonstrated in recent findings that two-thirds of common moth species across the UK have declined over the last 35 years, with 20% declining so fast that they should be considered threatened. As a result, several more species have now been added to Biodiversity Action Plans in the UK. Both the BBS and the Rothamsted Insect Survey demonstrate how large-scale monitoring can be used to denote changes in diversity and abundance over time. Furthermore, they both use protocols to filter data, thereby allowing for robust data set production. Locations of breeding bird survey routes. Figure credit: Curtis Flahter and Mike Knowles. Examples of (A) a light trap station in use, and (B) placement in a back yard. Photo credits: (A) Syd Wright MBE, and (B) Philip Gould. At the regional scale, Susanne Masi, manager of Chicago Botanic Garden's Plants of Concern Program, presented an overview and findings from the garden's rare plant monitoring project. The Chicago Botanic Garden established this program to monitor listed and rare plants in the greater Chicago metropolitan area. Initiated in 2001, the program involves ~250 trained volunteers each year in collecting plant data (Fig. 4), and has now accumulated 8+ years of standardized data on 205 plant species at 245 sites. Aside from simply monitoring rare plants, the program has demonstrated several key findings related to using citizen scientists. First, a two-year volunteer data validation study comparing randomly selected volunteer data to professional data showed a high degree of correlation between the two groups. For example, there was >80% correspondence between the two groups in critical data fields such as population numbers and presence of threats. Second, the results of a Plants of Concern citizen science focus group showed that volunteers participated actively in, and understood critical elements of, the scientific process. Furthermore, participants unanimously experienced an increase of their involvement in stewardship and conservation activities as a result of the program, and reported sharing this scientific understanding and enhanced conservation commitment with the broader public. Volunteers (A) determining plot locations and conducting rare and listed plant inventories (B–D) as part of the Chicago Botanic Garden's Plants of Concern Program. Photo credits: (A) Peter Jacobs, (B) Robin Carlson, (C) Emily Kapler, and (D) Dani Drekich. Capping off the case studies was a presentation by Bill Mueller, who introduced the Milwaukee County Avian Migration Monitoring Partnership (MCAMMP), an avian monitoring study focused on migratory bird stopover ecology in the urban parks of Milwaukee County, Wisconsin. To date the project has utilized the assistance of >140 citizen scientists over six migrations (three years) to help address the major goals of assessing habitat use and quality in both riparian and upland sites, and quantifying habitat use by migratory birds. Citizen science volunteers involvement includes training for transect counts, assistance with bird-banding operations, vegetation sampling and analysis, and recording of data. One major aim of training the citizen scientists is that they will be able to establish a long-term, urban avian monitoring project that can expand in the future. The second main portion of the symposium was devoted to a set of talks on the issues of the philosophy, policy, and technology of citizen science. Rebecca Jordan began this second portion with a discussion of a framework for promoting ecological literacy within the context of citizen science programs. She stressed that program design must balance both the scientific goals, which include ensuring data accuracy, and educational goals. Together these goals promote conceptual knowledge about the system of study, epistemological knowledge about science processes, and behavioral change with respect to environmental and civic action. While there is much evidence to support the promotion of conceptual knowledge, the latter two areas warrant further investigation. Integrating cognitive and environmental action theory will likely prove useful as practitioners seek to broaden program impact. David Bonter of Cornell's Laboratory of Ornithology next discussed the issue of data validation processes for large citizen science databases, such as Project FeederWatch. Currently, Project FeederWatch receives >100,000 checklists from >14,000 citizen scientists annually, yielding over 5,000,000 bird observations of ~500 individual species. Thus, it is critical that such large volumes of data be inspected for any problems; this requirement has led to the development of a quality control and quality assurance protocol. This protocol uses a review system, whereby unusual observations or potential errors are flagged and sent to experts for follow-up with the citizen scientists. Unverified reports remain flagged and are excluded from data analyses and web-based data output. The system also allows researchers to identify volunteers who are in need of support and to focus educational efforts accordingly, ultimately improving data quality and integrity. Moving from data editing to data collecting, Louis Liebenberg, founder of CyberTracker Conservation, presented a talk on how technology can be used to get people back in touch with nature. Specifically, Louis has developed the free software program CyberTracker (available at 〈http://www.cybertracker.org/〉), which enables volunteers of all ages to collect biodiversity data on simple portable devices, such as smartphones and PDAs (Fig. 5). CyberTracker is already in active use for both citizen science projects and environmental education around the world. For instance, in the United States, NatureMapping, BioKIDS, and BioBlitz are using PDAs with CyberTracker software to enable volunteers of all ages to collect biodiversity data. Similarly, in South Africa, the NaturalWorld web site allows participants to share and view bird sightings, and in the Kalahari trackers from local communities are being employed to survey wildlife conservation corridors. Finally, the WhaleForce project involves yachtsmen around the world using CyberTracker to monitor whales. Ultimately, the software allows for easy data collection by citizen scientists and helps to promote people who engage the outdoors by collecting field data. Bushmen in Africa using CyberTracker. Photo credit: Louis Liebenberg. Michelle Prysby next discussed more efficient ways for interested citizens to find a project, and for projects to find interested volunteers. One partnership for scientists and educators interested in reaching trained citizen scientists consists of the Master Naturalist programs. These programs are volunteer training and service programs that involve the public in natural resource education, citizen science, and stewardship. Currently there are >25 Master Naturalist programs in the United States that represent a ready pool of volunteers who have been trained in core citizen science skills, such as recording field observations and using taxonomic keys to identify organisms. These volunteers are well connected to their local environments, and are part of an existing infrastructure that can support their citizen science volunteer activities. (For more information on natural resource education and stewardship programs such as Master Naturalists, Watershed Stewards, and Conservation Stewards, please see the Alliance of Natural Resource Outreach and Service Programs 〈http://www.anrosp.org〉). The final presentation of the morning was by Hague Vaughan, of Canada's Ecological Monitoring and Assessment Network (EMAN), who wove together the themes of the morning's talks. He described how citizen science fosters a desperately needed means to better link ecological monitoring to policy development and decision-making. His argument was that the emphasis on certainty in ecological monitoring leaves decision-makers lacking sentinel and feedback information where timeliness is a key factor. If focused on outcomes, complementary citizen science can be a means of enhancing effectiveness. To illustrate how to integrate citizen science into policy, Vaughan discussed a project that combined citizen data with targeted research and air quality monitoring stations in Hamilton, Ontario, Canada to identify pollution and lichen hot-spots that was used to deliver feedback on municipal and industrial choices (Fig. 6). A map of arboreal lichens in Hamilton, Ontario, Canada, based upon citizen science data. Darker green locations represent greater numbers of lichens, and points represent sampling locations. The symposium concluded with a round table discussion of the morning's talks. Following the symposium, an additional workshop on citizen science was held over the weekend at the Urban Ecology Center of Milwaukee. At this workshop many of the symposium speakers gave an additional talk during the morning portion, with an afternoon of hands-on activities designed to train and educate citizen scientists. Overall, the symposium sought to address five major goals related to citizen science. In reflecting upon these five goals it is clear that there was progress made on all, but not necessarily agreement. For instance, the general view was that citizen science has new elements to offer ecology, but there was no definitive agreement among the speakers that it was a new discipline or subdiscipline. Whether or not this will change remains to be seen; citizen science is still very much an area of new ideas and growth. On the other hand, several speakers presented data from their research illustrating that the quality of data collected by citizen scientists is of the same or better quality than that collected by professional ecologists. Such quality is enhanced further with the aid of both software (e.g., Project FeederWatch and CyberTracker) and expert assistance. Similarly, there was strong evidence that citizen science can be an effective tool to help bridge the gap between ecologists and the public. In terms of the overlap with monitoring, it is clear that they share a number of similarities and will likely continue to do so in the future. However, many of the citizen science projects were much broader than monitoring alone, because they engaged the public in the scientific process or served to enhance ecological literacy. Based upon the talks and concluding discussions, citizen science is an increasing part of ecology, and has great promise for contributing knowledge, improving ecological literacy, training scientists to work with the public, and providing information for policy-makers.

Citizen scientists have become integral participants in biodiversity monitoring, and the demand for effective surveying efforts has sparked interest in utilizing environmental DNA (eDNA) for species monitoring, drawing volunteers into survey planning and sample collection. However, there remains a gap in understanding the motivations behind volunteer engagement in surveys that don’t involve direct observations of species, including the need to validate collected data and recognize volunteer contributions to ensure their satisfaction. To address this gap, we used questionnaires to assess volunteer science capital, motivation to participate, and their experiences during eDNA sampling. We trained ten conservation volunteers, all of whom were members of the Essex Wildlife Trust, to collect eDNA samples from two rivers and a beaver enclosure to detect terrestrial and semi-aquatic mammals. Citizen scientists were highly motivated and demonstrated high science capital, enabling them to develop innovative eDNA sampling methods and take enjoyment in contributing to meaningful scientific knowledge and helping their local wildlife. Additionally, citizen scientists detected more species compared with eDNA researchers, underscoring the value of local knowledge, and the longer sampling periods associated with typical citizen scientist projects. This case study highlights the benefits of involving citizen scientists in eDNA surveys, as sharing results led to meaningful discussions about the species detected and further enriched knowledge exchange. The case study’s limitations include small sample size and limited participant diversity in terms of age, gender, and ethnicity. However, the findings can inform future research with larger and more diverse participants.

<p>Citizen science has contributed to biodiversity research and monitoring for hundreds of years. Still, the recent increase in scale, scope, diversity and number of citizen science projects highlights the challenge of designing and implementing good practices around data collection and data curation. The Committee on Data for Science and Technology of the International Council for Science (ICSU-CODATA) and the World Data System (WDS) recently founded a joint Task Group to understand and support good practices for citizen science data validation, data cleaning and curation, and, short- and long- term data management.</p> <p>Research projects conducted by the ICSU-CODATA-WDS Task Group include the development of an initial typology of citizen science data generating tasks, and an exploratory landscape analysis of the state of the data in citizen science. The landscape analysis found that citizen science projects use a wide range of strategies for data validation at numerous stages of the scientific research process. In comparison, practices for data documentation, curation, and long-term management are less advanced. This may limit data discovery and re-use.</p> <p>This work compliments the planned and ongoing efforts of the TDWG Citizen Science Interest Group to advance biodiversity informatics for citizen science. Presenting research on the state of the data in citizen science can promote cross-pollination between the ICSU-CODATA-WDS Task Group and the biodiversity community, and encourage researchers and practitioners to work together to advance citizen science data quality, standards, and interoperability.</p>

Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

The major concern of applying citizen science in water resources is the quality of the data. However, there are limited scientific studies addressing this concern and showing the data value. In this study, we established a citizen science program in the Akaki catchment which hosts Addis Ababa, Ethiopia. Citizen scientists monitored river stage at multiple gauging sites for multiple years. We evaluated the quality of citizen science data through a systematic quality control. Reference data was obtained from neighboring stations of the citizen science program and professionals while the evaluation involved graphical inspections and statistical methods. The quality-controlled data were applied to evaluate the spatial and temporal variation of rainfall-runoff relationships. Initially, large numbers of suspicious data were detected using single station data but that was significantly reduced when the data of multiple sites were compared. Further comparison against professional data revealed excellent agreement with high correlation coefficient (r >0.95), and low centered root mean square error (RMSE) <0.03–0.08 mm. The citizen science data indicated a large difference in rainfall-runoff relationship over the dominantly urban and rural sub-catchments. The citizen science data allowed comparison of runoff coefficient and base flow index for recent and historical periods where recent streamflow data is unavailable from a formal data source. This study illustrates the immense value of (i) multiple data quality assessment steps for building confidence on the quality of citizen science data, and (ii) citizen science for enhancing our understanding of rainfall-runoff relationships and change in a rapidly urbanizing catchment.

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers.We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo-based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short-term (single session) and longer-term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo-based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long-lived citizen science projects.

Citizen science can be understood as an approach to scientific research in which volunteer contributors undertake work in one or more phases of the research process. Citizen science projects can be initiated by volunteers or institutional actors (e.g., scientists in academia), and volunteers often work together with professional researchers. In citizen science, participants are not just objects of research (e.g., interviewee or survey respondent) but also research subjects—that is, taking an active role in collecting data, analyzing data sets, contributing to study design, or disseminating results (or combinations of these tasks). Participants may have little background knowledge on the topic under study, or they might be amateur enthusiasts with a great deal of existing expertise. Citizen science projects aim for genuine science outcomes, which can include scientific data sets and publications, new discoveries, or policy or management action. Although citizen science projects are currently being developed and carried out in a wide variety of scientific fields, including medical biology (e.g., self-monitoring of disease symptoms), environmental science (e.g., monitoring air or water quality), history (e.g., archive transcription), and “citizen social science,” the field of biology especially has a long history of amateur involvement in research. Citizen science in this field often takes the form of collecting data on the natural world and submitting these data to biodiversity databases (e.g., reporting bird observations). In addition to collecting data, citizen scientists take up a large part of taxonomy, describing new species and rearranging, merging, and splitting species groups. Furthermore, citizen scientists are heavily involved in the verification process, checking on observations done by other citizen scientists and giving feedback, acting not only as gatekeepers toward data quality but also as authorities, educating the community. Biodiversity citizen science projects may involve monitoring of the natural world initiated by communities of natural history enthusiasts, but research institutes in the field of biology and ecology also increasingly mobilize volunteers to collect data about the natural environment. Compared to many other domains in which citizen science is being applied, biodiversity monitoring especially stands out for its long history of amateur involvement in natural history. Because initiating biodiversity citizen science projects will thus often mean that research and policy actors engage with volunteer-driven networks, understanding these networks aids effective and just design of biodiversity citizen science. Although engaging with these long-standing networks of natural history offers many opportunities, perspectives of professional ecological research and communities of practice can differ markedly. In the current state of affairs, scientific literature shows tensions between volunteers operating in their communities of practice and scientists operating in theirs. Among others, these differences involve the meaning of observations: Whereas in research these are given meaning by gathering them up and statistically analyzing the resulting data sets, within a community of practice observations predominantly reflect human–nature relationships and are shared with expectations of respectful use for the protection of nature. Not only can the meaning of observations differ but also the act of validation can refer to very different activities as well as to different aspects of quality of information. In the community of practice of observers in the field, validation plays an important role in establishing relations of trust and authority within the network, with a strong emphasis on correct observations and volunteers’ motivation for learning and belonging. Conversely, validation in the scientific practice of research concerns the structure of the monitoring protocol and the statistical demands placed on the data. For scientists and policymakers, respectful cooperation with networks of amateur biodiversity recorders requires taking their perspectives seriously and respecting their way of working and the communities they have built. It also requires citizen science organizers to think carefully about whose questions are being answered. For citizen scientists, understanding the (statistical) needs of scientists and the relevance for policy allows their network to grow through funding and training.

Abstract. The traditional way of doing science has been changing with the recent movements on open science, open data and the world-wide spread of volunteer activities. With the help of technological advancements, in particular mobile technologies, do-it-yourself kits and free online education sources, ordinary citizens may contribute to scientific processes based on their interest and abilities. Citizen Science (CitSci) is an active research agenda which draws the interest of scientists from different disciplines. It investigates the various contributions that citizens could do to improve scientific process. These contributions are utilized in diverse areas ranging from biology to environmental monitoring to classification of galaxies, all of which have a spatiotemporal dimension. The emerging developments in spatial information science allow the public to understand their environment better and efficiently. In this way society would have the means to deal with complex modern problems including but not limited to water/air pollution monitoring and mapping a region after a natural disaster to aid relief and recovery efforts. In addition, there is a growing public demand for research projects involving CitSci; because such collaborative efforts can be critical to achieve sustainability goals. The aim of this paper is to provide an overview on the state-of-the art methods and applications where CitSci is a crucial component in different phases of disaster management, from environmental protection to risk analysis and aid provision. Use of CitSci methods in disaster situations can be vital for different reasons, such as acquisition of local and most current information, organization of help during emergency, development of instant rescue plans, accurate information dissemination, and monitoring of the post-disaster influences to the society and the environment. Open issues on the use of CitSci methods in disaster management, such as data quality, technical and management-related barriers, open-standards and interoperability, and volunteer engagement, will also be discussed.

Citizen science (CS) can foster transformative impact for science, citizen empowerment and socio-political processes. To unleash this impact, a clearer understanding of its current status and challenges for its development is needed. Using quantitative indicators developed in a collaborative stakeholder process, our study provides a comprehensive overview of the current status of CS in Germany, Austria and Switzerland. Our online survey with 340 responses focused on CS impact through (1) scientific practices, (2) participant learning and empowerment, and (3) socio-political processes. With regard to scientific impact, we found that data quality control is an established component of CS practice, while publication of CS data and results has not yet been achieved by all project coordinators (55%). Key benefits for citizen scientists were the experience of collective impact (“making a difference together with others”) as well as gaining new knowledge. For the citizen scientists’ learning outcomes, different forms of social learning, such as systematic feedback or personal mentoring, were essential. While the majority of respondents attributed an important value to CS for decision-making, only few were confident that CS data were indeed utilized as evidence by decision-makers. Based on these results, we recommend (1) that project coordinators and researchers strengthen scientific impact by fostering data management and publications, (2) that project coordinators and citizen scientists enhance participant impact by promoting social learning opportunities and (3) that project initiators and CS networks foster socio-political impact through early engagement with decision-makers and alignment with ongoing policy processes. In this way, CS can evolve its transformative impact.

A Place for Volunteers in Field Sciences

Recent advances in digital technology, coupled with rapidly increasing interest in the creation and dissemination of digitized specimen data for use in broad-scale research by botanists and other organismal scientists, have encouraged the development of a variety of new research opportunities in the botanical sciences (e.g., Page et al., 2015; Soltis, 2017). It is now increasingly possible to collect, use, re-use, and share data more easily and effectively. With the advent of the U.S. National Science Foundation's Advancing Digitization of Biodiversity Collections initiative and the establishment of iDigBio (Integrated Digitized Biocollections; www.idigbio.org) as the national resource for specimen digitization and digital data mobilization, researchers now have access to ever larger and varied digital data sets for visualization, analysis, and modeling and have new opportunities for adopting "big data" strategies for facilitating discovery. The iDigBio portal alone now includes nearly 20 million botanical specimen records, a figure that is growing rapidly as new institutions share their data. In this special issue of Applications in Plant Sciences, which is based on symposium presentations at Botany 2017 (the annual meeting of the Botanical Society of America and affiliated societies) and the XIX International Botanical Congress, authors present a broad array of examples of the latest developments in botanical biodiversity research using digitized specimen data, including in the fields of genomics, conservation assessment, ecology, phenology, and taxonomic revisions. The papers present current trends in the proactive digitization of specimen data that occurs during the collecting and vouchering of specimens and field data; the tools, skills, and strategies needed for linking and visualizing botanical data; and innovative methods for digital discovery. This collection also highlights how digital data are being used in research that expands our understanding and conservation of plant diversity and the environment. Although the source data for the papers in this collection are herbarium specimens, the topics extend well beyond systematics. Broadly integrative plant biologists will be interested in new approaches to using and re-using specimen data—whether locality information for modeling or images for analysis of morphology and/or functional traits. More importantly, digitized herbarium data become even more valuable when linked to other data sources, such as environmental or genetic data. In fact, emerging cyberinfrastructure and new data sources provide unparalleled opportunities for mobilizing and integrating massive amounts of information from organismal biology, ecology, genetics, climatology, and other disciplines. Particularly powerful is the integration of phylogenies with specimen data, enabling analyses of phylogenetic diversity in a spatio-temporal context, the evolution of niche space, and more. Such data-driven synthetic analyses may generate unexpected patterns, yielding new hypotheses for further study. However, a major challenge is the heterogeneous nature of complex data, and new methods are needed to link these divergent data types. Ongoing efforts to link and analyze diverse data are yielding new perspectives on a range of ecological problems. Integration of plant phylogeny, distributions, traits, and ultimately genetics is permitting new perspectives on landscape-level patterns of biodiversity, with implications for conservation and management of natural resources. Although many specific hypotheses may be addressed through integrated analyses of biodiversity and environmental data, perhaps the greatest value of such data-enabled science will lie in the unanticipated patterns that emerge. The papers in this collection capture some of the diversity of the emerging themes that can be addressed via use of digitized herbarium specimens. The authors address the broad range of research that can be facilitated by analysis of digitized herbarium specimens; limitations and bias of digitized specimens for certain avenues of research; future digitization and training needs; the role of globally unique identifiers (GUIDs) in integrative research involving herbarium specimens and other sources of data; digitization workflows that incorporate field, museum, and data mobilization components; the use of deep learning in specimen identification from images; the development of a standardized workflow for scoring plant phenology from herbarium specimens; the use of aggregated digitized data for fungi in generating a comprehensive mycological flora (or mycoflora) for North America; the role of digital images in education and public outreach; the effective contributions of citizen scientists of all ages to hypothesis-driven research; and the need for effective, comprehensive, and accurate tracking of data use for understanding the impact of digitized collections. Noting the centuries of exploration that have yielded the global span of the world's herbaria, James et al. (2018) provide an overview of how open, digitized, aggregated botanical data can be used to document global change, predict future impacts, and drive biological and environmental remediation. Herbarium data—from the information in labels to data that can be extracted from images—have an increasing role to play in analyses of temporal and spatial change in community composition and structure. Moreover, patterns identified via analyses of herbarium specimens can form the foundation for conservation, rehabilitation, and restoration efforts of not only single species but entire communities. However, collections data—whether plants, animals, or fungi—may not always be research-ready. James et al. address the fitness for use of herbarium data in basic and applied research, noting that taxonomic, spatial, and temporal limitations may hamper the usefulness of herbarium data for specific questions. Fortunately, research efforts addressing issues of data quality, uncertainty, and bias are providing guidance for assessing limitations for specific uses and for ameliorating the effects. Given the enormous potential of herbarium data for research in systematics, ecology, conservation, and global change, the authors cite the need for greater global advocacy for collections, from curation of physical specimens to digitization to online publishing of digitized data. Future work to enhance digital herbarium collections through digitization of other resources, such as field notes, libraries, etc., and to develop tools for discovery, visualization, analysis, and communication is needed. Key to innovative and effective use of digitized herbarium data will be skills training for the next generation of botanical researchers. The assignment of GUIDs to facilitate the tracking, linking, and discovery of biodiversity specimens across the internet has been a hotly debated subject. Although the majority of biodiversity informaticians agree that the use of unique identifiers is essential, controversy remains about which types of identifiers are best, the most appropriate Darwin Core field in which they should be published, strategies for resolving identifiers to physical specimens across the internet, and effective implementation strategies for the wide variation in biodiversity collections storage, management, and digitization. Nelson et al. (2018) narrow the scope of this debate to the implementation of GUID assignments to the digitization and mobilization of herbarium specimen data. They review the types of GUIDs in current use and strongly recommend that GUID values be associated with all specimens and included in all digital records of those specimens. They address the lack of a universal, community-supported resolver for GUID values and offer guidelines and recommended practices for minting, managing, and sharing GUIDs for herbarium specimens. Contreras (2018) brings a paleobotanical perspective to this special edition, highlighting the important role researchers can play in incorporating collection, digitization, analysis, curation, and data mobilization into an integrated research and digitization pipeline. Although she emphasizes that the workflow and pipeline presented may be especially useful in smaller institutions with limited staff or when images and other digital data are integral to the research project, the protocols she outlines may have broad applicability to researchers and other staff working in larger collections, as well as to those in non-paleontological collections, including herbaria. Her workflow incorporates three components—field, museum, and data mobilization—that are often temporally and spatially separated in current practice. As a result, the paper brings a clear museum perspective to the research process, with the museum phase serving as a transition during which specimens are organized, data are bridged from field to museum, and the preparation of a museum workspace designed to facilitate these steps. Contreras' paper offers an important viewpoint on the ways in which research, collections management, digitization, and curation can be linked to support the management of specimens in the museum. Given the rapid increase in the availability of high-quality specimen and field images of plants, the capacity to utilize computer vision and image mining techniques to make automated taxonomic identifications, extract traits, and produce phenological scorings provides the field of convolutional neural networks and deep learning tremendous opportunities for applications in botany. Botella et al. (2018) review previous work with these tools, pointing out that recent progress with deep learning techniques has shown impressive recognition performance and that, when combined with mobile applications such as Pl@ntNet (https://identify.plantnet-project.org/), these techniques may contribute significantly to species distribution modeling (SDM), biodiversity monitoring, and the inclusion of citizen science observations within each of these domains. Their paper explores the use of automated identification in the absence of human validation for SDM, particularly the impact of the degree of uncertainty when training the MAXENT niche modeling approach. They evaluated five invasive species against a training set of 332,000 human-validated plant images belonging to about 11,000 species. Their results suggest significant research challenges for using these types of data in SDMs, as well as for developing models for integrating citizen science observations into conservation management. Automated image mining is of continuing importance to botany and is a worthwhile avenue for further research. Plant phenology (seasonal events such as leaf out, flowering, and fruiting) has complex effects on multiple levels of biological organization from individuals to ecosystems, and Yost et al. (2018) discuss the potential of herbarium specimens for addressing basic and applied research on plant phenology. Phenological shifts are key indicators of global change, and temporal mismatches in phenology may have important, even catastrophic effects on natural communities and agricultural systems. For example, such mismatches between plants and pollinators can quickly cause local extinctions, drive rapid evolutionary shifts, and cause billions of dollars of agricultural losses. Herbarium specimens are an excellent source of data for documenting changes in plant phenology (see review by Willis et al., 2017), but despite millions of specimens that could contribute to an understanding of historical phenology, inter-year variation in phenology, and true shifts in phenology, the use of these data suffers from a lack of standardized scoring methods and definitions of phenological states. To date, phenological information has been captured in a herbarium specimen record in multiple ways, for example, in Darwin Core fields from 'reproductiveCondition' to 'occurrenceRemarks,' 'organismRemarks,' 'dynamicProperties,' or 'fieldNotes.' The lack of standardization in scoring and recording phenological data has limited large-scale use of specimens for phenological study. Yost et al. propose a standardized methodology for scoring phenological characters from herbarium specimens that can be applied by researchers across herbaria, research groups, and means of data collection, including via citizen science, satellite imagery, and stationary cameras. Herbaria for centuries have typically housed collections of not only plants, but also fungi. Despite current knowledge that fungi represent the sister group to animals and are not closely related to plants, many of the curatorial practices for fungi are similar to those for plants, and this similarity extends to digitization as well. Thiers and Halling (2018) describe the Macrofungi Collections Consortium (MaCC) and the development of MyCoPortal (http://mycoportal.org/portal/index.php) for serving digitized specimen information. MaCC digitized data from ~1.25 million specimens; including data contributed by the Microfungi Collections Consortium (http://www.microfungi.org/), the MyCoPortal database currently houses nearly 3.5 million specimens, as well as descriptions, illustrations, and observational records. The driving force behind development of MyCoPortal was production of a database to provide baseline data on the extent and distribution of macrofungal diversity, and the aggregated data have certainly accomplished this goal. Moreover, MyCoPortal has attracted the amateur mycological community from the United States, which comprises 80 clubs and 10,200 members. Together, professional and amateur mycologists, with the foundational data from MyCoPortal, are poised to produce a comprehensive mycoflora of North America, complete with DNA sequences, phenotypic descriptions, and images. Data from MyCoPortal have been used in taxonomic treatments, large-scale phylogenetic analyses, ecological studies, and analyses of native versus invasive species and set the stage for a broad range of uses into the future. Herbaria are reservoirs of both well-documented specimens and undescribed diversity. New species are described each year from specimens that have been housed in collections for decades, if not centuries. However, the pace of such discovery is slow, especially for non-angiosperms, and accelerating the process of discovery is expensive. von Konrat et al. (2018) explore the role of digitization in increasing accessibility to specimens, particularly in combination with citizen science efforts and online technology for uses beyond label transcription. The authors connect natural history collections to education and outreach through a citizen science tool based on the online Zooniverse platform. Their project, MicroPlants (http://microplants.fieldmuseum.org/), uses images of the liverwort genus Frullania and both a web-based platform and an interactive touchscreen version to capture large data sets for taxonomic analysis, engage a diverse participant group in research, and expose the public to novel analytical approaches and the scientific process. MicroPlants has been used in informal science settings at the Field Museum and in formal educational venues in middle schools, high schools, and colleges and universities. The project has provided valuable data on both morphological variation in Frullania and the educational effectiveness of this citizen science platform. Noteworthy is the fact that preliminary analyses indicate that data provided by non-experts were comparable to those generated by experts, supporting a role for citizen scientists in addressing authentic hypothesis-driven research. Data aggregators and publishers benefit significantly from knowing how their collections data are being used and attributed, the number of records and data sets being downloaded, the types of individuals who are finding these data useful, and the impact of projects for which the data are used. Usage metrics, in particular, help herbaria document value to institutional administrators as well as potential funders, and assist herbarium directors seek out and target underserved or expanding audiences. Cantrill (2018) summarizes tracked usage of nearly 900,000 records from the Royal Botanic Gardens Victoria, served through the Australasian Virtual Herbarium, and details trends in data usage since 2009. Queries were tracked in three broad categories, including general use, non-research use, and scientific research use, with histories of how these categories and their subcategories have become more refined over the past decade. Cantrill points out that even with more highly resolved classifications, about one third of all queries still remain unclassified. He further notes that although the data give a glimpse of data use as reported by users, they do not provide a metric for understanding the impact of the projects for which they were downloaded. Future research must assess this issue if we are to understand and report the full impact of our collections. This collection of papers provides a current snapshot of some of the issues surrounding the aggregation and use of digitized herbarium data and of some of the many possible uses of these data in research and education. However, the field is changing rapidly, with new tools for data mining, image analysis, and data tracking coming available at a rapid pace. The application of innovative analytic, algorithmic, and informatics approaches to centuries-old specimens is revolutionizing the role of herbaria and other museum collections in modern biology. Integrated Digitized Biocollections (iDigBio) is funded by grants from the U.S. National Science Foundation's Advancing Digitization of Biodiversity Collections program (Co-operative Agreements EF-1115210 and DBI-1547229). The authors thank the contributors to this special issue of Applications in Plant Sciences for their contributions to two symposia on Green Digitization (at Botany 2017 and the XIX International Botanical Congress), and we thank the Botanical Society of America and the International Botanical Congress Organizing Committee for supporting these symposia.

The current environmental urgency means that we need data on the ocean, to understand its changes and potential solutions. Although citizen science data have been identified as a potential contribution to meeting international requirements (Fraisl et al., 2020; Fraisl et al., 2023; Danielsen et al., 2024), there is still a long way to go. The reporting requirements of official monitoring programmes, e.g. under MSFD 2008/56 EC or WFD 2000/60 EC, are still reluctant to integrate citizen science data, while some successful experiences at national level are already in place (e.g. descriptor 10 MSFD). On the other hand, due to the establishment of data libraries for the digital twin of the ocean there is an urgent requirement for data, including citizen science. Furthermore, consolidated structures are gathering and validating citizen science data, such as specialised structures such as the MINKA citizen science observatory, or international aggregators such as EMODnet and GBIF. However, there are still some gaps in the data pipeline from data to policy mainly for the data citation and data providers acknowledgement. Despite significant data collection efforts and standardization initiatives, achieving consistent data quality remains challenging in citizen science due to diverse stakeholder requirements and varying accuracy levels across projects (Balázs et al, 2021; European Commission, 2021). However, several European-funded projects are currently working on data validation, accreditation and data provider recognition such as MINKE Project (Metrology for Integrated Marine Management and Knowledge-Transfer Network), ENFORCE (Empowering Citizen for Environmental Action), Guarden (safeGUARDing biodiversity and critical ecosystem services across sectors and scales) and Marine biodiversity monitoring harmonisation. Nevertheless, in order to achieve official recognition of citizen science, it is essential that international, European institutions and Member States facilitate its integration into monitoring programmes and environmental agreements within the regulatory framework. The research infrastructures could have the potential to facilitate data accreditation for end-users in terms of the data management processes. A showcase of best practices on how citizen science can shape environmental compliance supporting legislative requirements will be provided, focusing on both realised benefits and untapped potential. ReferenceB Balázs, P Mooney, E Nováková, et al, 2021, Data Quality in Citizen Science Chapter 8 in K. Vohland et al. (eds.), The Science of Citizen Science, https://doi.org/10.1007/978-3-030-58278-4_8Danielsen, F, Ali, N, Andrianandrasan, H.T., et al, 2024. Involving citizens in monitoring the Kunming-Montreal Global Biodiversity Framework. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01447-yEuropean Commission: Joint Research Centre, Mitton, I., Tricarico, E., Schade, S., Lopez Canizares, C. et al., Data-validation solutions for citizen science data on invasive alien species, Publications Office of the European Union, 2021, https://data.europa.eu/doi/10.2760/694386Fraisl, D, Campbell, J, See, L, et al., 2020. Mapping citizen science contributions to the UN sustainable development goals. Sustainability Science, 15(6): 1735–1751. DOI: https://doi.org/10.1007/s11625-020-00833-7Fraisl,D, See, L, Campbell, J, et al, 2023, The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks Citizen Science: Theory and Practice 8 (1): 27. DOI: 10.5334/cstp.643