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Hot Topics in Citizen Science: Difference between revisions

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=== Data quality and reliability ===
=== Data quality and reliability ===
The reliability of data generated through citizen science activity is a hot topic as well as an active area of research. These investigations play an important role in validating citizen science initiatives by helping to address potential data quality concerns <ref>Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen science as an ecological research tool: Challenges and benefits. ''Annual Review of Ecology, Evolution, and Systematics, 41''(1), 149-172. doi:10.1146/annurev-ecolsys-102209-144636</ref> <ref>Lewandowski, E., & Specht, H. (2015). Influence of volunteer and project characteristics on data quality of biological surveys. ''Conservation Biology, 29''(3), 713-723. doi:10.1111/cobi.12481</ref> <ref>Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2016). Emerging problems of data quality in citizen science. ''Conservation Biology, 30''(3), 447-449. doi:10.1111/cobi.12706</ref>. This is important for the interpretation of results as well as providing guidance for the design of projects in aspects such as data collection procedures and training needs. Although there are an increasing number of international examples investigating citizen science data quality, there have been few such studies in New Zealand. Notable exceptions include in revival of the Stream Health Monitoring and Assessment Kit and the long-running Auckland Council supported Wai Care programme.  In these projects supporting research compared data collected by professionals to data collected by volunteers <ref>Moffett, E. R., & Neale, M. W. (2015). Volunteer and professional macroinvertebrate monitoring provide concordant assessments of stream health. ''New Zealand Journal of Marine and Freshwater Research, 49''(3), 366-375. doi:10.1080/00288330.2015.1018913</ref><ref>Storey, R. G., & Wright-Stow, A. (2017). Community-based monitoring of New Zealand stream macroinvertebrates: agreement between volunteer and professional assessments and performance of volunteer indices. ''New Zealand Journal of Marine and Freshwater Research, 51''(1), 60-18. doi:10.1080/00288330.2016.1266674</ref>. In general, the results of such studies show that the quality of volunteer data is often comparable to professional data, and may be used to augment professional monitoring programmes, or in some cases, as a standalone data source . The detection of differences may indicate that citizen science data are not ‘fit for purpose’ for some potential uses. Alternatively, these findings may provide a focus for further training initiatives, or other innovative approaches to control for sources of bias such as combining information on observer expertise with the data that is collected <ref name=":0">Johnston, A., Fink, D., Hochachka, W. M., & Kelling, S. (2018). Estimates of observer expertise improve species distributions from citizen science data. ''Methods in ecology and evolution, 9''(1), 88-97. doi:10.1111/2041-210x.12838</ref>.
The reliability of data generated through citizen science activity is a hot topic as well as an active area of research. These investigations play an important role in validating citizen science initiatives by helping to address potential data quality concerns <ref>Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen science as an ecological research tool: Challenges and benefits. ''Annual Review of Ecology, Evolution, and Systematics, 41''(1), 149-172. doi:10.1146/annurev-ecolsys-102209-144636</ref> <ref>Lewandowski, E., & Specht, H. (2015). Influence of volunteer and project characteristics on data quality of biological surveys. ''Conservation Biology, 29''(3), 713-723. doi:10.1111/cobi.12481</ref> <ref>Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2016). Emerging problems of data quality in citizen science. ''Conservation Biology, 30''(3), 447-449. doi:10.1111/cobi.12706</ref>. This is important for the interpretation of results as well as providing guidance for the design of projects in aspects such as data collection procedures and training needs. Although there are an increasing number of international examples investigating citizen science data quality, there have been few such studies in New Zealand. Notable exceptions include in revival of the Stream Health Monitoring and Assessment Kit and the long-running Auckland Council supported Wai Care programme.  In these projects supporting research compared data collected by professionals to data collected by volunteers <ref>Moffett, E. R., & Neale, M. W. (2015). Volunteer and professional macroinvertebrate monitoring provide concordant assessments of stream health. ''New Zealand Journal of Marine and Freshwater Research, 49''(3), 366-375. doi:10.1080/00288330.2015.1018913</ref> <ref>Storey, R. G., & Wright-Stow, A. (2017). Community-based monitoring of New Zealand stream macroinvertebrates: agreement between volunteer and professional assessments and performance of volunteer indices. ''New Zealand Journal of Marine and Freshwater Research, 51''(1), 60-18. doi:10.1080/00288330.2016.1266674</ref>. In general, the results of such studies show that the quality of volunteer data is often comparable to professional data, and may be used to augment professional monitoring programmes, or in some cases, as a standalone data source . The detection of differences may indicate that citizen science data are not ‘fit for purpose’ for some potential uses. Alternatively, these findings may provide a focus for further training initiatives, or other innovative approaches to control for sources of bias such as combining information on observer expertise with the data that is collected <ref name=":0">Johnston, A., Fink, D., Hochachka, W. M., & Kelling, S. (2018). Estimates of observer expertise improve species distributions from citizen science data. ''Methods in ecology and evolution, 9''(1), 88-97. doi:10.1111/2041-210x.12838</ref>.


=== Capacity building for CitSci initiatives ===
=== Capacity building for CitSci initiatives ===
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Latest revision as of 16:10, 22 August 2020

Benefits of citizen science

There is now a wealth of evidence showing the considerable benefits of citizen and community science. These extend far beyond a focus on gathering ‘data’ to include benefits associated with awareness-raising, science literacy, improved understanding of issues, and others.

Researchers

Citizen science can provide researchers with an increased ability to collect novel data over greater temporal and spatial scales (including private property). The shift towards a more collaborative and reciprocal approach to research, means that studies can be designed that respond more effectively to societal needs.

Communities

Everyone in society across a wide variety of groups can build new knowledge and skills; develop a greater awareness of issues that may encourage behaviour change; enhance wellbeing through increased social contact, physical activity and connection to the environment.

Decision-makers

New information to support evidence-based policy and planning by government and land management agencies e.g., through notifications of pest, pathogen and disease outbreaks, pollution events or the discovery of new species. Regionally, citizen science can support natural resource management and biodiversity strategies while national databases e.g., in New Zealand, the Land and Water Aotearoa platform may benefit from the input of community generated data. More broadly, citizen science is philosophically aligned to New Zealand’s Open Government and Open Data policies.  

Data quality and reliability

The reliability of data generated through citizen science activity is a hot topic as well as an active area of research. These investigations play an important role in validating citizen science initiatives by helping to address potential data quality concerns [1] [2] [3]. This is important for the interpretation of results as well as providing guidance for the design of projects in aspects such as data collection procedures and training needs. Although there are an increasing number of international examples investigating citizen science data quality, there have been few such studies in New Zealand. Notable exceptions include in revival of the Stream Health Monitoring and Assessment Kit and the long-running Auckland Council supported Wai Care programme.  In these projects supporting research compared data collected by professionals to data collected by volunteers [4] [5]. In general, the results of such studies show that the quality of volunteer data is often comparable to professional data, and may be used to augment professional monitoring programmes, or in some cases, as a standalone data source . The detection of differences may indicate that citizen science data are not ‘fit for purpose’ for some potential uses. Alternatively, these findings may provide a focus for further training initiatives, or other innovative approaches to control for sources of bias such as combining information on observer expertise with the data that is collected [6].

Capacity building for CitSci initiatives

Capacity building and support needs of citizen science projects may vary widely depending on their particular objectives [7]. In research projects looking to crowd-source data from volunteers, training in standardised data collection protocols is often desired as a means to improve data quality [8] [9]. However, there is also an increasing potential for citizen science to move away from standardised protocols towards the mining of available datasets using verification methodologies to support desirable analyses [10]. New Zealand studies have found that the top two challenges for maintaining CBEM projects in New Zealand are a lack of human resources (i.e. volunteers) and funding, followed by training in technical skills, and that many of the standardised monitoring ‘kits’ have been receiving low usage levels by community groups despite being specifically designed to support CBEM [11]. These examples suggest that the introduction of more onerous protocols may be problematic for volunteers due to factors such as limited time, and may be counterproductive for projects oriented toward local engagement where maintaining  participation is a core goal [12]. In many cases, participant-led forms of monitoring are more suited to local needs and the motivations of those involved [13] [14]. This can be supported by tailoring data collection methods to the citizens rather than training citizens in the existing field methods of professionals [15], and by adopting other means to control for observer bias and other data quality concerns [6].

References



  1. ↑ Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen science as an ecological research tool: Challenges and benefits. Annual Review of Ecology, Evolution, and Systematics, 41(1), 149-172. doi:10.1146/annurev-ecolsys-102209-144636
  2. ↑ Lewandowski, E., & Specht, H. (2015). Influence of volunteer and project characteristics on data quality of biological surveys. Conservation Biology, 29(3), 713-723. doi:10.1111/cobi.12481
  3. ↑ Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2016). Emerging problems of data quality in citizen science. Conservation Biology, 30(3), 447-449. doi:10.1111/cobi.12706
  4. ↑ Moffett, E. R., & Neale, M. W. (2015). Volunteer and professional macroinvertebrate monitoring provide concordant assessments of stream health. New Zealand Journal of Marine and Freshwater Research, 49(3), 366-375. doi:10.1080/00288330.2015.1018913
  5. ↑ Storey, R. G., & Wright-Stow, A. (2017). Community-based monitoring of New Zealand stream macroinvertebrates: agreement between volunteer and professional assessments and performance of volunteer indices. New Zealand Journal of Marine and Freshwater Research, 51(1), 60-18. doi:10.1080/00288330.2016.1266674
  6. ↑ 6.0 6.1 Johnston, A., Fink, D., Hochachka, W. M., & Kelling, S. (2018). Estimates of observer expertise improve species distributions from citizen science data. Methods in ecology and evolution, 9(1), 88-97. doi:10.1111/2041-210x.12838
  7. ↑ Conrad, C. C., & Hilchey, K. G. (2011). A review of citizen science and community-based environmental monitoring: issues and opportunities. Environmental Monitoring and Assessment, 176(1), 273-291. doi:10.1007/s10661-010-1582-5
  8. ↑ Gouveia, C., Fonseca, A., Câmara, A., & Ferreira, F. (2004). Promoting the use of environmental data collected by concerned citizens through information and communication technologies. Journal of Environmental Management, 71(2), 135-154. doi:10.1016/j.jenvman.2004.01.009
  9. ↑ Sullivan, J. J., & Molles, L. E. (2016). Biodiversity monitoring by community‐based restoration groups in New Zealand. Ecological Management & Restoration, 17(3), 210-217. doi:10.1111/emr.12225
  10. ↑ Catlin-Groves, C. L. (2012). The citizen science landscape: From volunteers to citizen sensors and beyond. International Journal of Zoology, 2012, 1-14. doi:10.1155/2012/349630
  11. ↑ Peters, M. A., Hamilton, D., Eames, C., Innes, J., & Mason, N. W. H. (2016). The current state of community-based environmental monitoring in New Zealand. New Zealand Journal of Ecology, 40(3), 279-288.
  12. ↑ Orchard, S. (2019). Growing citizen science for conservation to support diverse project objectives and the motivations of volunteers. Pacific conservation biology, 25(4), 342-344. doi:10.1071/PC18011
  13. ↑ McKay, A. J., & Johnson, C. J. (2017). Identifying Effective and Sustainable Measures for Community-Based Environmental Monitoring. Environmental Management, 60(3), 484-495. doi:10.1007/s00267-017-0887-3
  14. ↑ Wiseman, N. D., & Bardsley, D. K. (2016). Monitoring to learn, learning to monitor: A critical analysis of opportunities for indigenous community‐based monitoring of environmental change in Australian rangelands. Geographical Research, 54(1), 52-71. doi:10.1111/1745-5871.12150
  15. ↑ Palmer Fry, B. (2011). Community forest monitoring in REDD+: the ‘M’ in MRV? Environmental Science and Policy, 14(2), 181-187. doi:10.1016/j.envsci.2010.12.004
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