Luz F Jiménez-Segura, Daniel Restrepo-Santamaria, Juan G Ospina-Pabón, María C Castellanos-Mejía, Daniel Valencia-Rodríguez, Andrés F Galeano-Moreno, José L Londoño-López, Juliana Herrera-Pérez, Víctor M Medina-Ríos, Jonathan Álvarez-Bustamante, Manuela Mejía-Estrada, Marcela Hernández-Zapata, Luis J García-Melo, Omer Campo-Nieto, Iván D Soto-Calderón, Carlos DoNascimiento
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引用次数: 0
Abstract
Progress in the acquisition of massive sets of molecular data and in the bioinformatic capabilities for their processing have revolutionised species identification, filling gaps in crucial areas such as taxonomy, phylogenetic inference, biogeography, and even biodiversity conservation. Advanced DNA sequencing and metabarcoding have uncovered previously hidden diversity, although their effectiveness is highly dependent on the accuracy of reference DNA databases at local and regional scales. The compilation of information on freshwater fishes from the Magdalena River basin is an important milestone in improving our knowledge of the genetic and taxonomic diversity of a highly endemic region in the Neotropical context. Here, we share DNA data from 1,270 specimens representing 183 species, cross-referenced with complete collecting and catalogue information, along with high resolution photographs of voucher specimens when alive. This collection of multiple sources of information based on fish specimen records not only contributes to future research on Neotropical fish systematics and ecology, but also to conservation decisions in one of the South American rivers with a highest level of endemism.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.