Year: 2025
Type: article
Abstract: The City Nature Challenge (CNC) has rapidly expanded across India from 2023 to 2025, leading to a surge in biodiversity observations on iNaturalist. While the volume of data is impressive, its long-term research and conservation value depends heavily on data quality (Vattakaven et al. 2022)—particularly the proportion of observations reaching "Research Grade" (RG). This study investigates patterns and barriers related to RG achievement in CNC India observations over three consecutive years using iNaturalist data (iNaturalist Community 2023, iNaturalist Community 2024, iNaturalist Community 2025). We analyze RG percentages at regional, state, and city scales, comparing the total number of observations year by year (Fig. 1). By tracking these metrics from 2023 to 2025, we quantify the "identification lag" (Fig. 2)—the gap between data quantity (uploads) and data quality (verified records). Our analysis identifies whether growth is driven solely by participation or if it is supported by a parallel increase in expert identification (Fig. 3). In addition to temporal (year-wise) comparisons, we also investigate spatial (geographical) variations in data quality across India. We also identify taxa and locations with disproportionately high numbers of "Needs ID" observations (which are not yet considered as RG; Fig. 4). This analysis is conducted both horizontally—i.e., comparing taxonomic groups (such as, Plantae, Fungi, Animalia, Insecta), and vertically—i.e., comparing taxonomic rank (such as, family, genus, species). This approach helps us to evaluate whether these bottlenecks stem from inherent identification challenges, observer behavior, or a lack of specialized taxonomic expertise within the local identifier community. Our results indicate a widening gap between upload volume and RG attainment (Fig. 2), particularly in high-performing regions. This disparity is most acute for taxa requiring specialized expertise and for observations at the species level. These findings offer practical insights for enhancing community engagement, directing expert identification efforts, and designing support mechanisms for new observers. By providing a nuanced perspective on the evolution of data quality within a massive, volunteer-driven dataset, this presentation contributes to broader discussions on sustaining and improving the living data ecosystems that support contemporary biodiversity science.
Language: English
Subfield: Ecological Modeling
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117112366