India Roadkill Monitoring Project

The dataset is standardized using Darwin Core terms to ensure interoperability and integration with global biodiversity platforms.Roadkill represents a significant but underreported source of biodiversity loss, affecting a wide range of taxa. This dataset includes records of mammals, birds, reptiles, amphibians, collected across diverse habitats such as forests, agricultural lands, wetlands, and urban environments. Each record typically contains species identification (to the lowest possible taxonomic level), geographic coordinates, date of observation, and supporting metadata, including photographs where available.The dataset enables the identification of spatial hotspots of road mortality, temporal patterns in wildlife-vehicle collisions, and species-specific vulnerability.

These insights are essential for mitigation strategies such as wildlife crossings, road planning, and traffic regulation measures.Although the dataset may exhibit spatial and temporal biases inherent to citizen science approaches, it represents one of the most comprehensive efforts to document roadkill in India. By contributing to global biodiversity databases, it supports comparative analyses in road ecology and provides a valuable resource for conservation planning, ecological research, and infrastructure management.

The India Roadkill Monitoring Project is an Asian partner of the RISKY project, which aims to develop a comprehensive global web platform on wildlife mortality from transport and energy infrastructure. The RISKY project seeks to contribute knowledge for the development and expansion of energy and transport infrastructures that respect and protect wildlife. Data collection framework The India Roadkill Monitoring Project dataset is compiled using a dual-source monitoring framework combining (i) citizen science observations and (ii) systematic non-consecutive road surveys.

While the citizen science observations are from 2018-2025 conducted across ten states of India, the records from systematic surveys are from 2021-2022 from Amravati district, Maharashtra State, India. All records were structured following the Darwin Core occurrence model, where each occurrence originates from either a road survey or an opportunistic observation, associated via a unique occurrenceID. Data standardization We conducted a clustering analysis across all text fields to identify similar entries with minor variations, such as typographical errors, which were subsequently corrected using OpenRefine.

All date values were also standardized using OpenRefine. Coordinate uncertainty values recorded as 0 m were adjusted to either 30 m or 100 m, depending on whether the records were collected after or before 2000, respectively, following the recommendations of the Darwin Core Reference Guide. Taxonomy All species names were cross-referenced against the GBIF Backbone Taxonomy using Java and the GBIF API.

This process aimed to correct taxonomic inconsistencies, retrieve additional information such as Kingdom, Phylum, and scientific authorship, and fill taxonomic gaps within the datasets. Species names that could not be matched automatically were manually reviewed, and synonyms were searched and assigned when available. Georeferencing quality control We used the OpenStreetMap API through Java to identify potential geographic inaccuracies and verify whether coordinates matched the reported country.

We calculated the distance from each occurrence to the nearest road using the GRIP global roads database, ensuring that all records fell within the defined coordinate uncertainty. We also verified whether the survey duration corresponded to the provided initial and final survey dates. In addition, we calculated the distance between the reported initial and final road coordinates and cross-checked it against the stated road length.

Duplicate records within the same dataset (i.e., identical location, species, and date) were identified and merged, with the number of roadkills aggregated into a single occurrence record. Intellectual Rights and Licensing Current version of the India Roadkill Monitoring Project dataset includes photographic evidence associated with occurrence records through the Darwin Core Multimedia Extension. Occurrence records are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Photographic media linked through the Multimedia Extension are published under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Contributors retain copyright ownership of submitted photographs while granting the India Roadkill Monitoring Project and its collaborators a worldwide, royalty-free, perpetual, irrevocable license to use, archive, distribute, and publish such media for scientific, educational, conservation, and policy-related purposes in accordance with the project Terms of Use.

Multimedia creator information is represented as “citizen science contributor”, while rights-holder information is represented as “Original contributor” unless otherwise specified. The dataset and associated media are intended to support biodiversity research, road ecology studies, conservation planning, environmental education, and evidence-based policy development.