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Area of Science:

  • Biodiversity informatics
  • Data science
  • Systematic biology

Background:

  • Growing concerns about aggregated biodiversity data quality erode trust in large-scale data networks.
  • Current strategies focus on correcting errors at the source, which are insufficient for full diagnosis.

Purpose of the Study:

  • To investigate the root causes of distrust in biodiversity data aggregators.
  • To analyze the role of social design and power dynamics in data aggregation.
  • To evaluate the impact of data standards on taxonomic coherence and accountability.

Main Methods:

  • Qualitative analysis of trust dynamics in data aggregation.
  • Examination of the social design of aggregation processes and power balances.
  • Assessment of the Darwin Core standard's role in data coherence.

Main Results:

  • Distrust stems not only from data quality but also from the social design of aggregation and power imbalances.
  • Aggregators create an accountability gap by downplaying the significance of taxonomic hierarchies ('backbones').
  • The Darwin Core standard lacks structure to preserve taxonomic coherence, leading to unsupported inferences.

Conclusions:

  • Effective data aggregation requires accommodating taxonomic plurality and addressing unsettled systematic research.
  • New technical and social mechanisms are needed for expert validation of taxonomically coherent data packages.
  • Enhancing trust necessitates a more accountable and inclusive biodiversity data aggregation process.