Using mobile phones to engage citizen scientists in research

Graham, Eric; Henderson, Sandra; Schloss, Annette L.

doi:10.1029/2011eo380002

Cited by 90 publications

(65 citation statements)

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“…Recent advances in digital information and communication technologies have enabled much wider participation in citizen science, to include people that may not possess any, or only limited, background knowledge of the studied subjects, but are nevertheless willing to contribute by collecting data (Adriaens et al 2015;Graham et al 2011;August et al 2015). Furthermore, the development of mobile smartphone technologies in particular offers several advantages, including the functionality to take photographs, use microphones and…”

Section: Introductionmentioning

confidence: 99%

Exploring the role of smartphone technology for citizen science in agriculture

Dehnen‐Schmutz

Foster

Owen

et al. 2016

Agron. Sustain. Dev.

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Citizen science is the involvement of citizens, such as farmers, in the research process. Citizen science has become increasingly popular recently, supported by the proliferation of mobile communication technologies such as smartphones. However, citizen science methodologies have not yet been widely adopted in agricultural research. Here, we conducted an online survey with 57 British and French farmers in 2014. We investigated (1) farmer ownership and use of smartphone technologies, (2) farmer use of farmspecific management apps, and (3) farmer interest and willingness to participate in agricultural citizen science projects. Our results show that 89 % respondents owned a smartphone, 84 % used it for farm management, and 72 % used it on a daily basis. Fifty-nine percent engaged with farm-specific apps, using on average four apps. Ninety-three percent respondents agreed that citizen science was a useful methodology for data collection, 93 % for real-time monitoring, 83 % for identification of research questions, 72 % for experimental work, and 72 % for wildlife recording. Farmers also showed strong interest to participate in citizen science projects, often willing to commit substantial amounts of time. For example, 54 % of British respondents were willing to participate in farmland wildlife recording once a week or monthly. Although financial support was not always regarded as necessary, experimental work was the most likely activity for which respondents thought financial support would be essential. Overall, this is the first study to quantify and explore farmers' use of smartphones for farm management, and document strong support for farm-based citizen science projects.

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Section: Introductionmentioning

confidence: 99%

Exploring the role of smartphone technology for citizen science in agriculture

Dehnen‐Schmutz

Foster

Owen

et al. 2016

Agron. Sustain. Dev.

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“…It is, therefore, useful to also consider the use of other approaches to obtain water level data. Citizen science is now more frequently used to obtain environmental data over large areas (Savan et al, 2003;Bonney et al, 2009;Graham et al, 2011;Fohringer et al, 2015;Huddart et al, 2016;Wiseman and Bardsley, 2016). Little et al (2016) gave citizen scientists water level sounders to measure groundwater levels in private wells and found that these measurements provided valuable data on groundwater levels across a large area in Alberta, Canada, and that the measurements were relatively accurate; the root mean square error between citizen scientist observed water levels and pressure transducer based water levels ranged between 3 and 11 cm.…”

Section: Introductionmentioning

confidence: 99%

Information content of stream level class data for hydrological model calibration

Meerveld

Vis

Seibert

2017

Hydrol. Earth Syst. Sci.

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Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

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“…New technologies such as smartphone applications st (apps) are increasingly being made available and are used to involve citizens in the reporting of species in Europe (Graham et al 2011, Teacher et al 2013. Apps can support the generation of georeferenced records of IAS which, once validated, can complement professional monitoring schemes and return useful data for early warning, rapid response programs and management schemes .…”

Section: Introductionmentioning

confidence: 99%