A dataset for benchmarking molecular identification tools based on genome skimming

Genome skimming is an emerging tool allowing for scalable DNA barcoding efforts for numerous biodiversity science applications. Despite its growing importance, there are few standardized datasets for benchmarking genome skimming tools, making it challenging to evaluate new methods (e.g., using machine learning), and comparing to existing ones (e.g., conventional barcoding loci derived from Sanger-based sequencing). To address this gap, we present four curated datasets designed for benchmarking molecular identification tools using low-coverage genomes. These datasets comprise vast phylogenetic and taxonomic diversity from closely related species to all taxa currently represented on NCBI SRA. One of them consists of novel sequences from taxonomically verified samples in the plant clade Malpighiales, while the other four datasets compile publicly available data. All include raw genome skim sequences and two-dimensional graphical representations of genomic data (chaos game representations and varKodes), enabling comprehensive testing and validation of molecular species identification methods. These datasets represent a reliable resource for researchers to assess the accuracy, efficiency, and robustness of their tools in a consistent and reproducible manner.