Cited by 43Human Rights and DevelopmentGeographic Information Systems StudiesResearch Data Management Practices

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Using citizen science data to monitor the Sustainable Development Goals: a bottom-up analysis

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Ballerini, Laura, Bergh, Sylvia I.·2021·Sustainability Science

Abstract

Official data are not sufficient for monitoring the United Nations Sustainable Development Goals (SDGs): they do not reach remote locations or marginalized populations and can be manipulated by governments. Citizen science data (CSD), defined as data that citizens voluntarily gather by employing a wide range of technologies and methodologies, could help to tackle these problems and ultimately improve SDG monitoring. However, the link between CSD and the SDGs is still understudied. This article aims to develop an empirical understanding of the CSD-SDG link by focusing on the perspective of projects which employ CSD. Specifically, the article presents primary and secondary qualitative data collected on 30 of these projects and an explorative comparative case study analysis. It finds that projects which use CSD recognize that the SDGs can provide a valuable framework and legitimacy, as well as attract funding, visibility, and partnerships. But, at the same time, the article reveals that these projects also encounter several barriers with respect to the SDGs: a widespread lack of knowledge of the goals, combined with frustration and political resistance towards the UN, may deter these projects from contributing their data to the SDG monitoring apparatus.

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Research works printed but not linked: 51

  1. Biernacki P, Waldorf D (1981) Snowball sampling: problems and techniques of chain referral sampling. Sociol Methods Res 10:141–163. https://doi.org/10.1177/004912418101000205 · DOI
  2. Bishop IJ, Warner S, van Noordwijk TCGE, Nyoni FC, Loiselle S (2020) Citizen science monitoring for sustainable development indicator 6.3.2 in England and Zambia. Sustainability 12:1–15 · DOI
  3. Bonney R, Cooper CB, Dickinson J, Kelling S, Phillips T, Rosenberg KV, Shirk J (2009) Citizen science: a developing tool for expanding science knowledge and scientific literacy. Bioscience 59:977–984. https://doi.org/10.1525/bio.2009.59.11.9 · DOI
  4. Bonney R, Cooper C, Ballard H (2016) The theory and practice of citizen science: launching a new journal. Citiz Sci Theory Pract 1:1–4. https://doi.org/10.5334/cstp.65 · DOI
  5. Burgess HK, DeBey LB, Froehlich HE, Schmidt N, Theobald EJ, Ettinger AK, HilleRisLambers J, Tweksbury J, Parrish JK (2017) The science of citizen science: exploring barriers to use as a primary research tool. Biol Cons 208:113–120. https://doi.org/10.1016/j.biocon.2016.05.014 · DOI
  6. Carlson T, Cohen A (2018) Linking community-based monitoring to water policy: perceptions of citizen scientists. J Environ Manag 219:168–177. https://doi.org/10.1016/j.jenvman.2018.04.077 · DOI
  7. Cohn JP (2008) Citizen science: can volunteers do real research? Bioscience 58:192–197. https://doi.org/10.1641/B580303 · DOI
  8. Danielsen F, Burgess ND, Balmford A (2005) Monitoring matters: examining the potential of locally-based approaches. Biodivers Conserv 14:2507–2542. https://doi.org/10.1007/s10531-005-8375-0 · DOI
  9. Eisenhardt KM (1989) Building theories from case study research. Acad Manag Rev 14:523–550. https://doi.org/10.2307/258557 · DOI · Paper page
  10. Eitzel MV, Cappadonna JL, Santos-Lang C et al (2017) Citizen science terminology matters: exploring key terms. Citiz Sci Theory Pract 2:1–20. https://doi.org/10.5334/cstp.96 · DOI
  11. Fisher A, Fukuda-Parr S (2019) Introduction—data, knowledge, politics and localizing the SDGs. J Hum Dev Capab 20:375–385. https://doi.org/10.1080/19452829.2019.1669144 · DOI
  12. Flückiger Y, Seth N (2016) SDG indicators need crowdsourcing. Nature 531:448. https://doi.org/10.1038/531448c · DOI
  13. Fraisl D, Campbell J, See L et al (2020) Mapping citizen science contributions to the UN sustainable development goals. Sustain Sci. https://doi.org/10.1007/s11625-020-00833-7 · DOI
  14. Freitag A, Meyer R, Whiteman L (2016) Strategies employed by citizen science programs to increase the credibility of their data. Citiz Sci Theory Pract 1:1–11. https://doi.org/10.5334/cstp.6 · DOI
  15. Fritz S, See L, Carlson T et al (2019) Citizen science and the united nations sustainable development goals. Nat Sustain 2:922–930. https://doi.org/10.1038/s41893-019-0390-3 · DOI
  16. Fukuda-Parr S, McNeill D (2019) Knowledge and politics in setting and measuring the SDGs: introduction to special issue. Glob Policy 10:5–15. https://doi.org/10.1111/1758-5899.12604 · DOI
  17. Gabrys J, Pritchard H, Barratt B (2016) Just good enough data: figuring data citizenships through air pollution sensing and data stories. Big Data Soc 3:1–14. https://doi.org/10.1177/2053951716679677 · DOI
  18. Gray J, Lämmerhirt D, Bounegru L (2016) Changing what counts. How can citizen-generated and civil society data be used as an advocacy tool to change official data collection. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2742871 · DOI
  19. Jordan R, Crall A, Gray S, Phillips T, Mellor D (2015) Citizen science as a distinct field of inquiry. Bioscience 65:208–211. https://doi.org/10.1093/biosci/biu217 · DOI
  20. Kennedy H, Poell T, van Dijck J (2015) Data and agency. Big Data Soc 2(2):1–7. https://doi.org/10.1177/2053951715621569 · DOI
  21. Kieslinger B, Schaefer T, Heigl F, Dörler D, Richter A, Bonn A (2017) The challenge of evaluation: an open framework for evaluating citizen science activities. https://osf.io/preprints/socarxiv/enzc9 · DOI
  22. Kitchin R, McArdle G (2016) What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data Soc 3(1):1–10. https://doi.org/10.1177/2053951716631130 · DOI · Paper page
  23. Kling F, Pozdnoukhov A (2012) When a city tells a story: urban topic analysis. In: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. https://doi.org/10.1145/2424321.2424395 · DOI
  24. Kosmala M, Wiggins A, Swanson A, Simmons B (2016) Assessing data quality in citizen science. Front Ecol Environ 14:551–560. https://doi.org/10.1002/fee.1436 · DOI
  25. Langford M, Winkler I (2014) Muddying the water? Assessing target-based approaches in development cooperation for water and sanitation. J Hum Dev Capab 15:247–260. https://doi.org/10.1080/19452829.2014.896321 · DOI
  26. Li Z, Wang C, Emrich CT, Guo D (2016) A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods. Cartogr Geogr Inf Sci 45:97–110. https://doi.org/10.1080/15230406.2016.1271356 · DOI
  27. Lowry CS, Fienen MN (2013) CrowdHydrology: crowdsourcing hydrologic data and engaging citizen scientists. Ground Water 51(1):151–156. https://doi.org/10.1111/j.1745-6584.2012.00956.x · DOI
  28. Lukyanenko R, Parsons J, Wiersma Y (2011) Citizen science 2.0: data management principles to harness the power of the crowd. In: Jain H, Sinha AP, Vitharana P (eds) Service-oriented perspectives in design science research. DESRIST 2011. Lecture Notes in Computer Science 6629. Springer, Berlin Heidelberg, pp 465–473 · DOI
  29. Lukyanenko R, Parsons J, Wiersma Y (2016) Emerging problems of data quality in citizen science. Conserv Biol 30(3):447–449 · DOI
  30. MacFeely S, Nastav B (2019) “You say you want a [data] revolution”: a proposal to use unofficial statistics for the SDG global indicator framework. Stat J IOS 35:309–327. https://doi.org/10.3233/SJI-180486 · DOI
  31. Mahajan M (2019) The IHME in the shifting landscape of global health metrics. Global Pol 10(S1):110–120. https://doi.org/10.1111/1758-5899.12605 · DOI
  32. Meijer A, Potjer S (2018) Citizen-generated open data: an explorative analysis of 35 cases. Gov Inf Q 35:613–621. https://doi.org/10.1016/j.giq.2018.10.004 · DOI
  33. Merry SE (2019) The sustainable development goals confront the infrastructure of measurement. Global Pol 10:146–148. https://doi.org/10.1111/1758-5899.12606 · DOI
  34. Michael M, Lupton D (2016) Toward a manifesto for the public understanding of big data. Public Underst Sci 25(1):104–116. https://doi.org/10.1177/0963662515609005 · DOI
  35. Newman G, Chandler M, Clyde M, McGreavy B, Haklay M, Ballard H, Gray S, Scarpino R, Hauptfeld R, Mellor D, Gallo J (2017) Leveraging the power of place in citizen science for effective conservation decision making. Biol Cons 208:55–64. https://doi.org/10.1016/j.biocon.2016.07.019 · DOI
  36. Purdam K (2014) Citizen social science and citizen data? Methodological and ethical challenges for social research. Curr Sociol 62:374–392. https://doi.org/10.1177/0011392114527997 · DOI
  37. Rogers R (2013) Digital methods. MIT Press, Cambridge · DOI
  38. Saner R, Yiu L, Nguyen M (2019) Monitoring the SDGs: digital and social technologies to ensure citizen participation, inclusiveness and transparency. Dev Pol Rev 00:1–18. https://doi.org/10.1111/dpr.12433 · DOI
  39. Satterthwaite ML, Dhital S (2019) Measuring access to justice: transformation and technicality in SDG 16.3. Global Pol 10:96–109. https://doi.org/10.1111/1758-5899.12597 · DOI
  40. Shirk JL, Ballard HL, Wilderman CC et al (2012) Public participation in scientific research: a framework for deliberate design. Ecol Soc 17(2):29. https://doi.org/10.5751/ES-04705-170229 · DOI
  41. Silverton J (2009) A new dawn for citizen science. Trends Ecol Evol 24(9):467–471. https://doi.org/10.1016/j.tree.2009.03.017 · DOI
  42. Strasser BJ, Mahr D, Sanchez GA, Tancoigne E (2019) “Citizen Science”? Rethinking Science and Public Participation. Sci Technol Stud 32(2):52–76
  43. Strauss A, Corbin J (1997) Grounded theory in practice. SAGE Publications, Thousand Oaks
  44. Turbé A, Barba J, Pelacho M et al (2019) Understanding the citizen science landscape for European Environmental Policy: an assessment and recommendations. Citiz Sci Theory Pract 4:1–16. https://doi.org/10.5334/cstp.239 · DOI
  45. Turrini T, Dorler D, Richter A, Heigl F, Bonn A (2018) The threefold potential of environmental citizen science—generating knowledge, creating learning opportunities and enabling civic participation. Biol Conserv 225:176–186. https://doi.org/10.1016/j.biocon.2018.03.024 · DOI
  46. Tyson E, Bowser A, Palmer J et al (2018) Global mosquito alert: building citizen science capacity for surveillance and control of disease-vector mosquitoes. Workshop Report for the Wilson Center, Washington DC
  47. Wiggins A, Crowston K (2011) From conservation to crowdsourcing: a typology of citizen science. Paper presented at 44th Hawaii International Conference on System Sciences, Kauai. https://ieeexplore.ieee.org/document/5718708 · DOI
  48. Winkler IT, Satterthwaite ML (2017) Leaving no one behind? Persistent inequalities in the SDGs. Int J Hum Rights 21:1073–1097. https://doi.org/10.1080/13642987.2017.1348702 · DOI
  49. Yamin AE (2019) Power, politics and knowledge claims: sexual and reproductive health and rights in the SDG era. Global Pol 10:52–60. https://doi.org/10.1111/1758-5899.12598 · DOI
  50. Yap ML-M, Watene K (2019) The sustainable development goals (SDGs) and indigenous peoples: another missed opportunity? J Hum Dev Capab 20:451–467. https://doi.org/10.1080/19452829.2019.1574725 · DOI
  51. Yin R (2014) Case study research: design and methods, 5th edn. SAGE Publications, Thousand Oaks

Metadata

Authors
Ballerini, L., & Bergh, S. I.
Affiliations
  • University of Oxford (GB)
  • International Institute of Social Studies (NL)
Corresponding author
Bergh, Sylvia I.
Venue
Sustainability Science
Published
2021-07-23
Volume
16
Issue
6
Pages
1945-1962
ISSN
  • 1862-4057 (ISSN-L)
  • 1862-4065
OpenAlex
W3186995134
Cited by
43
References
1
Citations by year
2026: 8 · 2025: 9 · 2024: 3 · 2023: 10 · 2022: 12 · 2021: 1
FWCI
9.01
Type
journal article
Language
en
Open access
Locations
  • Sustainability Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
  • RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
Funders
John Stuart Mill College, Vrije Universiteit Amsterdam
Topics
  • Social Sciences › Sociology and Political Science › Human Rights and Development
  • Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
Keywords
citizen science data, Sustainable Development Goals, SDG monitoring, marginalized communities

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