Inequality persists in a large citizen science programme despite increased participation through ICT innovations
Mari JönssonDick KasperowskiStephen J. CoulsonJohan NilssonPavel BínaChristopher KullenbergNiclas HagenRené van der WalJesse Peterson
6
Citation
58
Reference
10
Related Paper
Citation Trend
Abstract:
Abstract Biological recording is a prominent and widely practised form of citizen science, but few studies explore long-term demographic trends in participation and knowledge production. We studied long-term demographic trends of age and gender of participants reporting to a large online citizen science multi-taxon biodiversity platform ( www.artportalen.se ). Adoption by user communities and continually developing Information and Communications Technologies (ICTs) greatly increased the number of participants reporting data, but profound long-term imbalances in gender contribution across species groups persisted over time. Reporters identifying as male dominated in numbers, spent more days in the field reporting and reported more species on each field day. Moreover, an age imbalance towards older participants amplified over time. As the first long-term study of citizen participation by age and gender, our results show that it is important for citizen science project developers to account for cultural and social developments that might exclude participants, and to engage with underrepresented and younger participants. This could facilitate the breadth of engagement and learning across a larger societal landscape, ensure project longevity and biodiversity data representation (e.g. mitigate gender bias influence on the number of reports of different species groups).Keywords:
Citizen Science
Representation
Citizen science has potential to provide multiple benefits to participants and the professional scientific community, and those benefits can be realized if citizen science projects are intentionally designed to achieve research objectives, and if participants have the skills, knowledge, and training to collect high-quality data. Using three years of data from a citizen science bird monitoring project in Salt Lake City, Utah, we assessed bird songs and calls learned by volunteers, and compared species detections, number of birds, and distance measurements between point counts by citizen scientists and professional biologists. We found significant increases in correct species identification for citizen scientists after going through the training program; the average percentage of bird songs and calls identified rose from 42.5% before training to 72.7% after training (p < 0.00001). For two data quality metrics, citizen scientists and professional biologists collected similar quality data: the average number of birds and average detection distances were not significantly different for point counts conducted by citizen scientists and professional biologists in the same locations. However, professional biologists identified an average of 1.48 more species than citizen scientists (p < 0.00001). Our findings emphasize the importance of evaluating training programs and data accuracy for citizen science projects. In instances in which citizen scientists may not be performing at the same level as professional biologists, identifying these patterns ensures that they can be fully explained and accounted for during data analysis.
Citizen Science
Identification
Salt lake
Cite
Citations (5)
Citizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that have unstructured protocols and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the iNaturalist platform with that from a semi-structured citizen science platform — eBird — for the continental United States. We tested whether four traits of species (color, flock size, body size, and commonness) predicted if a species was under- or over-represented in the unstructured dataset compared with the semi-structured dataset. We found strong evidence that large-bodied birds were over-represented in the unstructured citizen science dataset; moderate evidence that common species were over-represented in the unstructured dataset; moderate evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in unstructured citizen science data. Our results suggest that biases exist in unstructured citizen science data when compared with semi-structured data, likely as a result of the detectability of a species and the inherent recording process. Importantly, in programs like iNaturalist the detectability process is two-fold — first, an individual needs to be detected, and second, it needs to be photographed, which is likely easier for many large-bodied species. Our results indicate that caution is warranted when using unstructured citizen science data in ecological modelling, and highlight body size as a fundamental trait that can be used as a covariate for modelling opportunistic species occurrence records, representing the detectability or identifiability in unstructured citizen science datasets. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and expand our research by including structured citizen science data to understand how biases differ among unstructured, semi-structured, and structured citizen science platforms.
Citizen Science
Unstructured data
Taxonomic rank
Cite
Citations (7)
Biodiversity citizen science projects investigate, for example, which species of plants or animals exist in an area and how many individuals of each species live in that area. Such projects involve citizen scientists in identifying and monitoring biological diversity and collecting biodiversity data. With the help of citizen scientists, researchers can collect large amounts of such data that they would not be able to collect on their own. We wanted to know how citizen scientists benefit from their participation in biodiversity citizen science projects.
Citizen Science
Global biodiversity
Cite
Citations (0)
Citizen science is a powerful way to undertake monitoring of biodiversity, both for detecting rare events (e.g. invasive species, animal and plant health issues or presence of rare species) and assessing trends. However, in order to use citizen science effectively we need to understand better the patterns of people’s participation in projects considering variation in participation between citizen science approaches and individual variation in participation within a project. variation in participation between citizen science approaches and individual variation in participation within a project. Here, we particularly focus on the information content of the data collected through citizen science (although we recognise that citizen science has many other benefits, in addition to data collection). Firstly, we assessed participation in five projects for biodiversity monitoring in the UK, from mass participation to monitoring by volunteer experts, representing up to two thousand people per activity per year. We quantified the patterns of participation (in terms of retention of participants, spatial patterns of participation, and unevenness of contributions per participant - as in the 90:10 rule). We found that the data from mass participation projects were more strongly spatially correlated with human population density and retention of individuals was lower compared to projects targeted on those with existing interest in the subject. Secondly, we quantified the recording behaviour of recorders in a butterfly citizen science project. We developed this with four thousand users of a smartphone app designed for recording sightings of butterflies in the UK. The majority of these users were active for less than 10 days, a feature common to many citizen science projects. The users who engaged for longer produced most of the records for the project. We characterised their recording behaviour using 11 metrics that describe the variation in temporal and spatial recording behaviour as well as the data they recorded. Results showed that citizen scientists in this project fall on a continuum along 4 main axes describing their behaviour. We then used a 20-year butterfly dataset to assess the contribution of different types of recorders to the overall estimate of biodiversity trends and their precision. Overall, variation in participation, both between projects and between individuals within projects, contributes to variation in the information content (and hence the usefulness) of citizen science datasets. We show how different approaches can provide data to meet different needs for data users and how this understanding can be used to improve analyses of these data, allowing us to better design citizen science activities in the future.
Citizen Science
Variation (astronomy)
Cite
Citations (0)
Citizen Science
Environmental research
Cite
Citations (0)
Citizen scientists have the potential to play a crucial role in the study of rapidly changing lady beetle (Coccinellidae) populations. We used data derived from three coccinellid‐focused citizen‐science programs to examine the costs and benefits of data collection from direct citizen‐science (data used without verification) and verified citizen‐science (observations verified by trained experts) programs. Data collated through direct citizen science overestimated species richness and diversity values in comparison to verified data, thereby influencing interpretation. The use of citizen scientists to collect data also influenced research costs; our analysis shows that verified citizen science was more cost effective than traditional science (in terms of data gathered per dollar). The ability to collect a greater number of samples through direct citizen science may compensate for reduced accuracy, depending on the type of data collected and the type(s) and extent of errors committed by volunteers.
Citizen Science
Liberian dollar
Cite
Citations (279)
Citizen Science
Identification
Scope (computer science)
Limiting
Plant identification
Cite
Citations (66)
Here we aim to provide guidance to support
people considering using a citizen science
approach, especially (but not necessarily
restricted to) monitoring biodiversity and
the environment in the UK. It will help you
decide whether citizen science is likely to be
useful, and it will help you decide which broad
approach to citizen science is most suitable for
your question or activity.
This guide does not cover the practical detail
of developing a citizen science project. That
information is provided in the ‘Guide to Citizen
Science’ (Tweddle et al., 2012).
Citizen Science
Cite
Citations (67)
Within conservation and ecology, volunteer participation has always been an important component of research. Within the past two decades, this use of volunteers in research has proliferated and evolved into “citizen science.” Technologies are evolving rapidly. Mobile phone technologies and the emergence and uptake of high-speed Web-capable smart phones with GPS and data upload capabilities can allow instant collection and transmission of data. This is frequently used within everyday life particularly on social networking sites. Embedded sensors allow researchers to validate GPS and image data and are now affordable and regularly used by citizens. With the “perfect storm” of technology, data upload, and social networks, citizen science represents a powerful tool. This paper establishes the current state of citizen science within scientific literature, examines underlying themes, explores further possibilities for utilising citizen science within ecology, biodiversity, and biology, and identifies possible directions for further research. The paper highlights (1) lack of trust in the scientific community about the reliability of citizen science data, (2) the move from standardised data collection methods to data mining available datasets, and (3) the blurring of the line between citizen science and citizen sensors and the need to further explore online social networks for data collection.
Citizen Science
Upload
Cite
Citations (128)
Citizen Science
Scope (computer science)
Cite
Citations (0)