Biodiversity Information Science and Standards 9: e183350 doi: 10.3897/biss.9.183350 Conference Abstract Unpacking Data Quality in Citizen Science: An Analysis of City Nature Challenge India Ram Dayal Vaishnav , Vijay Barve , Lavanya Bhat , A. Ashwin , Anukriti Sharma , Chitra Shanker ‡ The Naturalist School, Bangalore, India § The University of Trans-Disciplinary Health Sciences and Technology (TDU), Bangalore, India | Naturals History Museum of Los Angeles County, Los Angeles, United States of America ¶ Nature Mates Nature Club, Kolkata, India # Indian Institute of Rice Research, Hyderabad, India Corresponding author: Ram Dayal Vaishnav (
[email protected]) Received: 23 Dec 2025 | Published: 24 Dec 2025 Citation: Vaishnav RD, Barve V, Bhat L, Ashwin A, Sharma A, Shanker C (2025) Unpacking Data Quality in Citizen Science: An Analysis of City Nature Challenge India. Biodiversity Information Science and Standards 9: e183350. https://doi.org/10.3897/biss.9.183350 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. ‡,§ |,¶,§ ‡ ‡ ‡ # © Vaishnav R et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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. Figure 1. City Nature Challenge India Spatial & Temporal Analysis: Observation Count and Research Grade Percentage from 12 cities over three years (Vaishnav, Barve, Bhat, A and Shanker CC -BY 4.0). Figure 2. City Nature Challenge India Taxonomic Analysis: Observation Count and Research Grade Percentage for different taxa (Vaishnav, Barve, Bhat, A and Shanker CC-BY 4.0). 2Vaishnav R et al
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 Figure 3. City Nature Challenge India Taxonomic Analysis: Observers and Identifiers Count for different taxa, along with Research Grade Percentage (Vaishnav, Barve, Bhat, A and Shanker CC-BY 4.0). Figure 4. City Nature Challenge India Taxonomic Analysis for 'Needs ID' Observations: Number of observations stuck at different ranks for different taxa (Vaishnav, Barve, Bhat, A and Shanker CC-BY 4.0). Unpacking Data Quality in Citizen Science: An Analysis of City Nature Challenge ... 3
sustaining and improving the living data ecosystems that support contemporary biodiversity science. Keywords iNaturalist, Research Grade Presenting author Ram Dayal Vaishnav Presented at Living Data 2025 Conflicts of interest The authors have declared that no competing interests exist. References • iNaturalist Community (2023) City Nature Challenge 2023: India Dataset. URL: https:// www.inaturalist.org/projects/city-nature-challenge-2023-india • iNaturalist Community (2024) City Nature Challenge 2024: India Dataset. URL: https:// www.inaturalist.org/projects/city-nature-challenge-2024-india • iNaturalist Community (2025) City Nature Challenge 2025: India Dataset. URL: https:// www.inaturalist.org/projects/city-nature-challenge-2025-india • Vattakaven T, Barve V, Ramaswami G, Singh P, Jagannathan S, Dhandapani B (2022) Best Practices for Data Management in Citizen Science - An Indian Outlook. Biodiversity Informatics 17 https://doi.org/10.17161/bi.v17i.16441 4Vaishnav R et al
