iNaturalist citizen science community during City Nature Challenge: new computational approach for analysis of user activity

Tupikina, Liubov; Schlosser, Frank; Воскресенский, В. А.; Kloppenborg, Katharina; Lopez, Florence; Mariz, Albrecht; Mogilevskaja, Anna; Haklay, Muki; Tzovaras, Bastian Greshake

doi:10.48550/arxiv.2112.02693

Cited by 1 publication

(1 citation statement)

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“…Scientists and large-scale platforms, such as eBird and iNaturalist, can make efforts to design projects that promote and support local leadership and allow for regional customization (e.g. The City Nature Challenge, which takes on a 'train the trainers' model, utilizing iNaturalist; Tupikina et al, 2021). For existing large-scale projects, lead scientists can work to understand participants' identities, priorities and interests while weaving in their knowledge, which can lead to better long-term participation (Rotman et al, 2014).…”

Section: Prioritize Individual and Large-scale Inclusivitymentioning

confidence: 99%

A framework for contextualizing social‐ecological biases in contributory science data

Carlen,

Estien,

Caspi

et al. 2024

People and Nature

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Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

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Section: Prioritize Individual and Large-scale Inclusivitymentioning

confidence: 99%