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Taxonomy of Data Quality Metrics in Digital Citizen Science

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Intelligent Sustainable Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 578))

Abstract

Data quality is key in the success of a citizen science project. Valid datasets serve as evidence for scientific research. Numerous projects have highlighted the ways in which participatory data collection can cause data quality issues due to human day-to-day practices and biases. Also, these projects have used and reported a myriad of techniques to improve data quality in different contexts. Yet, there is a lack of systematic analyses of these experiences to guide the design and of digital citizen science projects. We mapped 35 data quality issues of 16 digital citizen science projects and proposed a taxonomy with 64 mechanisms to address data quality issues before, during and after the data collection in digital citizen science projects. This taxonomy is built upon the analysis of literature reports (N = 144), two urban experiments (participants = 280), and expert interviews (N = 11). Thus, we contribute to advance the development of systematic methods to improve the data quality in digital citizen science projects.

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References

  1. Rotman, D., Hammock, J., Preece, J., Hansen, D., Boston, C., et al.: Motivations affecting initial and long-term participation in citizen science projects in three countries. iConference (2014)

    Google Scholar 

  2. Metso, L., Happonen, A., Rissanen, M., et al.: Data openness based data sharing concept for future electric car maintenance services. In: Smart Innovation, Systems and Technologies, pp. 429–436 (2020). https://doi.org/10.1007/978-3-030-57745-2_36

  3. Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., et al.: Citizen science: a developing tool for expanding science knowledge and scientific literacy. Bioscience 59(11), 977–984 (2009)

    Article  Google Scholar 

  4. Hand, E.: People power: networks of human minds are taking citizen science to a new level. Nature 466(7307), 685–688 (2010)

    Article  Google Scholar 

  5. Dickinson, J.L., Shirk, J., Bonter, D., Bonney, R., Crain, R.L., Martin, J., Phillips, T., Purcell, K.: The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10(6), 291–297 (2012)

    Article  Google Scholar 

  6. Dickinson, J.L., Zuckerberg, B., Bonter, D.N.: Citizen science as an ecological research tool: challenges and challenges and benefits. Ann. Rev. Ecol. Syst. 41(1), 149–172 (2010). https://doi.org/10.1146/annurev-ecolsys-102209-144636

  7. Bonney, R., Shirk, J.L., Phillips, T.B., Wiggins, A., Ballard, H.L., Miller-Rushing, A.J., Parrish, J.K.: Next steps for citizen science. Science 343(6178), 1436–1437 (2014)

    Article  Google Scholar 

  8. Palacin-Silva, M.V., Knutas, A., Ferrario, M.A., Porras, J., Ikonen, J., Chea, C.: The role of gamification in participatory environmental sensing: a study in the wild. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2018)

    Google Scholar 

  9. Balestrini, M., Diez, T., Kresin, F.: From participatory sensing to making sense. ECSA GA’2015 Conference (2015)

    Google Scholar 

  10. Newman, G., Wiggins, A., Crall, A., Graham, E., Newman, S., Crowston, K.: The future of citizen science: emerging technologies and shifting paradigms. Front. Ecol. Environ. 10(6), 298–304 (2012)

    Article  Google Scholar 

  11. Guo, B., Yu, Z., Zhou, X., Zhang, D.: From participatory sensing to mobile crowd sensing. In: 2014 IEEE PerCom Conference Workshop, 2014, pp. 593–598. IEEE (2014)

    Google Scholar 

  12. Christin, D., Reinhardt, A., Kanhere, S.S., Hollick, M.: A survey on privacy in mobile participatory sensing applications. J. Syst. Softw. 84(11), 1928–1946 (2011)

    Article  Google Scholar 

  13. Krontiris, I., Langheinrich, M., Shilton, K.: Trust and privacy in mobile experience sharing: future challenges and avenues for research. IEEE Commun. Mag. 7;52(8), 50–5 (2014)

    Google Scholar 

  14. Jahkola, O., Happonen, A., Knutas, A., Ikonen, J.: What should application developers understand about mobile phone position data. In: CompSysTech’17, ACM, pp. 171–178 (2017)

    Google Scholar 

  15. Foody, G., Fritz, S., Fonte, C.C., Bastin, L., Olteanu-Raimond, A.M., et al.: Mapping and the citizen sensor. Mapping Citizen Sens., 1–2 (2017)

    Google Scholar 

  16. Loss, S.R., Loss, S.S., Will, T., Marra, P.P.: Linking place-based citizen science with large-scale conservation research: a case study of bird-building collisions and the role of professional scientists. Biol. Conserv. 184, 439–445 (2015)

    Google Scholar 

  17. Heggen, S.: Participatory sensing: repurposing a scientific tool for stem education. Interactions 20(1), 18–21 (2013)

    Google Scholar 

  18. Zaman, J., De Meuter, W.: DisCoPar: distributed components for participatory campaigning. In: 2015 IEEE PerCom Conference Workshop, pp. 160–165. IEEE (2015)

    Google Scholar 

  19. Palacin, V., Gilbert, S., Orchard, S., Eaton, A., Ferrario, M.A., Happonen, A.: Drivers of Participation in Digital Citizen Science: Case Studies on Järviwiki and Safecast. Citizen Sci. Theory Prac. 5(1, 22), pp. 1–20 (2020). https://doi.org/10.5334/cstp.290

  20. Jennett, C., Cox, A.L.: Digital citizen science and the motivations of volunteers. In: The Wiley Handbook of Human Computer Interaction, vol.2, pp. 831-841 (2018)

    Google Scholar 

  21. Orchard, S.: Growing citizen science for conservation to support diverse project objectives and the motivations of volunteers. Pac. Conserv. Biol. 25(4), 342–344 (2019)

    Article  Google Scholar 

  22. Kortelainen, H., Happonen, A., Hanski, J.: From Asset Provider to Knowledge Company-Transformation in the Digital Era, In Lecture Notes in Mechanical Engineering (2019). ISSN: 2195-4356, pp. 333-341. https://doi.org/10.1007/978-3-319-95711-1_33

  23. Kinnunen, S.-K., Happonen, A., Marttonen-Arola, S., Kärri, T.: Traditional and extended fleets in literature and practice: definition and untapped potential. Int. J. Strat. Eng. Asset Manage. 3(3), 239–261 (2019)

    Article  Google Scholar 

  24. Kosmala, M., Wiggins, A., Swanson, A., Simmons, B.: Assessing data quality in citizen science. Front. Ecol. Environ. 14(10), 551–560 (2016)

    Article  Google Scholar 

  25. Hunter, J., Alabri, A., Ingen, C.: Assessing the quality and trustworthiness of citizen science data. Concurr. Comput. Prac. Exper. 25(4), 454–466 (2013)

    Article  Google Scholar 

  26. Budde, M., Schankin, A., Hoffmann, J., Danz, M., Riedel, T., Beigl, M.: Participatory sensing or participatory nonsense? Mitigating the effect of human error on data quality in citizen science. ACM 1(39), 1–23 (2017)

    Google Scholar 

  27. Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K.V., Shirk, J.: Citizen science: a developing tool for expanding science knowledge and scientific literacy. Bioscience 59(11), 977–984 (2009)

    Article  Google Scholar 

  28. Even, A., Shankaranarayanan, G.: Utility-driven assessment of data quality. ACM SIGMIS Database 38(2), 75–93 (2007)

    Article  Google Scholar 

  29. Kahn, B.K., Strong, D.M., Wang, R.Y.: Information quality benchmarks: product and service performance. Commun. ACM 45(4), 184–192 (2002)

    Article  Google Scholar 

  30. Metso, L., Happonen, A., Ojanen, V., Rissanen, M., Kärri, T.: Business model design elements for electric car service based on digital data enabled sharing platform. In: Cambridge International Manufacturing Symposium, 26–27 September 2019, Cambridge, UK, p. 6 (2019)

    Google Scholar 

  31. Kärri, T., Marttonen-Arola, S., Kinnunen, S-K., et al.: Fleet-based industrial data symbiosis. In: S4Fleet—Service Solutions for Fleet Management, DIMECC Publications Series No. 19, 2017. ISBN: 978-952-238-199-6, eISSN: 2342-2696, pp. 124–169 (2017)

    Google Scholar 

  32. Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45(4), 211–218 (2002)

    Article  Google Scholar 

  33. Sabrina, T., Murshed, M., Iqbal, A.: Anonymization Techniques for Preserving Data Quality, p. 11 (2016)

    Google Scholar 

  34. Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)

    Article  Google Scholar 

  35. Strong, D.M., Lee, Y.W., Wang, R.Y.: Data quality in context. Commun. ACM 40(5), 103–110 (1997)

    Article  Google Scholar 

  36. Cappiello, C., Francalanci, C., Pernici, B.: Time-related factors of data quality in multichannel information systems. J. Manag. Inf. Syst. 20(3), 71–92 (2003)

    Article  Google Scholar 

  37. Heinrich, B., Helfert, M.: Analyzing Data Quality Investments in CRM-A Model-Based Approach (2003).

    Google Scholar 

  38. Kaiser, M., Klier, M., Heinrich, B.: How to Measure Data Quality? A Metric-Based Approach (2007)

    Google Scholar 

  39. Wixom, B.H., Watson, H.J.: An empirical investigation of the factors affecting data warehousing success. In: MIS, vol. quarterly, pp. 17–41 (2001)

    Google Scholar 

  40. Bobrowski, M., Marré, M., Yankelevich, D.: A Software Engineering View of Data Quality (1998)

    Google Scholar 

  41. Wiggins, A., He, Y.: Community-Based Data Validation Practices in Citizen Science, vol. 2, pp. 1548–1559 (2016)

    Google Scholar 

  42. Welvaert, M., Caley, P.: Citizen surveillance for environmental monitoring: combining the efforts of citizen science and crowdsourcing in a quantitative data framework, vol. 5, no. 1. SprinerPlus (2016)

    Google Scholar 

  43. Lukyanenko, R., Parsons, J., Wiersma, Y.F.: Emerging problems of data quality in citizen science. Conserv. Biol. 30(3), 447–449 (2016)

    Article  Google Scholar 

  44. Vaddepalli, K.: Improving Data Quality in Citizen Science. Master’s thesis, LUT University, Lappeenranta (2019). [Online]. Available: http://urn.fi/URN:NBN:fi-fe2019090226408. Accessed from 13 May 2020

  45. Palacin, V., Ginnane, S., Ferrario, M.A., Happonen, A., Wolff, A., Piutunen, S., Kupiainen, N.: SENSEI: harnessing community wisdom for local environmental monitoring in Finland. CHI Conf. 1–8. https://doi.org/10.1145/3290607.3299047

  46. “Home | Doit Europe”, Doit-europe.net, 2020. [Online]. Available: https://www.doit-europe.net. Accessed from 15 May 2022

  47. Vaddepalli, K., Palacin, V., Porras, J., Happonen, A.: Connecting Digital Citizen Science Data Quality Issue to Solution Mechanism Table (2020). [Online]. https://doi.org/10.5281/zenodo.3829498

  48. Buytaert, W., Zulka?i, Z., Grainger, S., Acosta, L., Alemie, T.C., et al.: Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development. Front. Earth Sci. 2, 26 (2014)

    Google Scholar 

  49. Alabri, A., Hunter, J.: Enhancing the Quality and Trust of Citizen Science Data, vol. 12 (2010)

    Google Scholar 

  50. Wiggins, A., Newman, G., Stevenson, R.D., Crowston, K.: Mechanisms for Data Quality and Validation in Citizen Science, vol. 12 (2011)

    Google Scholar 

  51. Sullivan, B.L., Wood, C.L., Iliff, M.J., Bonney, R.E., Fink, D., Kelling, S.: eBird: a citizen-based bird observation network in the biological sciences. Biol. Cons. 142(10), 2282–2292 (2009)

    Article  Google Scholar 

  52. Sutcliffe, A.: Designing for user engagement: aesthetic and attractive user interfaces. Synth. Lect. 2(1) (2009)

    Google Scholar 

  53. Sutcliffe, A., Namoune, A.: Getting the Message Across: Visual Attention, Aesthetic Design and What Users Remember, pp. 11–20 (2008)

    Google Scholar 

  54. Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N.Y., Huang, R., Zhou, X.: Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput. Surv. (CSUR) 48(1), 7 (2015)

    Article  Google Scholar 

  55. Maisonneuve, N., Stevens, M., Ochab, B.: Participatory noise pollution monitoring using mobile phones. Inf. Pol. 15(1), 2–51 (2010)

    Google Scholar 

  56. Santti, U., Happonen, A., Auvinen, H.: Digitalization boosted recycling: gamification as an inspiration for young adults to do enhanced waste sorting. In: AIP Conference Proceedings, vol. 2233, no. 1, p. 12 (2020)

    Google Scholar 

  57. Crowston, K., Prestopnik, v: Motivation and Data Quality in a Citizen Science Game: A Design Science Evaluation. In: 2013 46th HICSS, pp. 450–459 (2013)

    Google Scholar 

  58. Ren, J., Zhang, Y., Zhang, K., Shen, X.S.: SACRM: social aware crowdsourcing with reputation management in mobile sensing. Comput. Commun. 65, 55–65 (2015)

    Article  Google Scholar 

  59. Arazy. O., Nov, O., Anderson, D.: Scientists@ home: what drives the quantity and quality of online citizen science participation? PloS 9(4), p. 90375, 11 (2014)

    Google Scholar 

  60. Slaughter, S.A., Harter, D.E., Krishnan, M.S.: Evaluating the cost of software quality. Commun. ACM 41(8), 67–73 (1998)

    Article  Google Scholar 

  61. Kelling, S., Hochachka, W.M., Fink, D., Riedewald, M., Caruana, R., et al.: Data-intensive science: a new paradigm for biodiversity studies. Bioscience 59(7), 613–620 (2009)

    Article  Google Scholar 

  62. Roman, L.A., Scharenbroch, B.C., Östberg, J.P., Mueller, L.S., et al.: Data quality in citizen science urban tree inventories. Urban Forestry and Urban 22, 124–135 (2017)

    Article  Google Scholar 

  63. Bennin, K.E., Keung, J., Monden, A., Kamei, Y., et al.: Investigating the Effects of Balanced Training and Testing Datasets on Effort-Aware Fault Prediction Models, vol. 1, p. 6 (2016)

    Google Scholar 

  64. Mashhadi, A.J., Capra, L.: Quality Control for Real-Time Ubiquitous Crowdsourcing, vol. 9, pp. 5–8 (2011)

    Google Scholar 

  65. Graves, M., Constabaris, A., Brickley, D.: Foaf: connecting people on the Semantic web. Catalog. Classif. Quart. 43(3–4), 191–202 (2007)

    Google Scholar 

  66. Jaimes, L.G., Vergara-Laurens, I.J., Raij, A.: A survey of incentive techniques for mobile crowd sensing. IEEE Internet Things J. 2(5), 370–380 (2015)

    Article  Google Scholar 

  67. Kim, S., Mankoff, J., Paulos, E.: Sensr: Evaluating a Flexible Framework for Authoring Mobile Data-Collection Tools for Citizen Science, vol. 2, pp. 1453–1462 (2013)

    Google Scholar 

  68. Mitra, T., Hutto, C.J., Gilbert, E.: Comparing Person and Process Centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk, vol. 4, pp. 1345–1354 (2015)

    Google Scholar 

  69. Khoshgoftaar, T., Folleco, A., Van, J., Bullard, H.L.: Multiple Imputation of Missing Values in Software Measurement Data (2006)

    Google Scholar 

  70. Loss, S.R., Loss, S.S., Will, T., Marra, P.: Linkingplace-based citizen science with large-scale conservation research: a case study of bird building collisions and the role of professional scientists. Biol. Cons. 184, 439–445 (2015)

    Article  Google Scholar 

  71. Cheng, M., Huang, T., Peng, P.L.H.: Bias reduction for nonparametric and semiparametric regression models. Statistica Sinica 28(4) (2018)

    Google Scholar 

  72. Kananura, R.M., Ekirapa-Kiracho, E., Paina, L., Bumba, A., et al.: Participatory monitoring and evaluation approaches that influence decision-making: lessons from a maternal and newborn study in Eastern Uganda. Health Res. Policy Syst. 15(2), 107 (2017)

    Article  Google Scholar 

  73. Garbarino, J., Mason, C.E.: The power of engaging citizen scientists for scientific progress. J. Microbiol. Biol. Educ. 17(1), 7 (2016)

    Article  Google Scholar 

  74. Bonter, D.N., Cooper, C.B.: Data Validation in Citizen Science: A Case Study from Project FeederWatch, vol. 10, no. 6 (2012)

    Google Scholar 

  75. Irwin, A.: Citizen Science: A Study of People. Psychology Press, Expertise and Sustainable Development (1995)

    Google Scholar 

  76. Höller, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., Boyle, D.: From Machine to the Internet of Things: Introduction to a New Age of Intelligence (2014)

    Google Scholar 

  77. Askham, N., Cook, D., Doyle, M., Fereday, H., Gibson, M., Landbeck, U., et al.: The six primary dimensions for data quality assessment. DAMA UK Working. Group (2013)

    Google Scholar 

  78. Messenger, J.C., Ho, K.K., Young, C.H., Slattery, L.E., Draoui, J.C., et al.: The national cardiovascular data registry (NCDR) data quality brief: the NCDR data quality program in 2012. J. Am. Coll. Cardiol. 60(16), 1484–1488 (2012)

    Article  Google Scholar 

  79. Heinrich, B., Klier, M.: Metric-based data quality assessment-developing and evaluating a probability-based currency metric. Decis. Support Syst. 72, 82–96 (2015)

    Article  Google Scholar 

  80. Caldiera, V.R.B.G., Rombach, H.D.: The goal question metric approach. Encycl. Softw. Eng., 528–532 (1994)

    Google Scholar 

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Acknowledgements

Authors thank European Regional Development Funds and Regional Council of South Karelia for funding MINT project supporting experience collection and also AWARE and CroBoDITT CBC projects funded by the European Union, supporting manuscript finalization.

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Authors and Affiliations

  1. Paladin Cyber, 545 Sutter St Ste 405, San Francisco, CA, 94102-1150, USA

    Krishna Vaddepalli

  2. University of Helsinki, Unioninkatu 35, 00014, Helsinki, Finland

    Victoria Palacin

  3. Aalto University, Otakaari 1B, FI-00076, Espoo, Finland

    Jari Porras

  4. LUT University, Yliopistonkatu 36, 58350, Lappeenranta, Finland

    Jari Porras & Ari Happonen

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  1. Krishna Vaddepalli
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  2. Victoria Palacin
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  4. Ari Happonen
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Correspondence to Krishna Vaddepalli .

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Editors and Affiliations

  1. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Department of Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Vaddepalli, K., Palacin, V., Porras, J., Happonen, A. (2023). Taxonomy of Data Quality Metrics in Digital Citizen Science. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 578. Springer, Singapore. https://doi.org/10.1007/978-981-19-7660-5_34

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