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Decentralized but Globally Coordinated Biodiversity Data.

Beckett W Sterner1, Edward E Gilbert1, Nico M Franz1

  • 1School of Life Sciences, Arizona State University, Tempe, AZ, United States.

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|March 11, 2021
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Summary
This summary is machine-generated.

Decentralizing biodiversity data aggregation through loosely joined, self-governing communities enhances data quality and societal impact. This approach fosters expert engagement and allows for flexible data sharing across scientific languages.

Keywords:
biodiversity datacommunities of practicedata aggregationdata intelligencedecentralizationknowledge commonsontology alignmentsystematic biology

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Area of Science:

  • Biodiversity informatics
  • Data science
  • Ecology

Background:

  • Centralized biodiversity data aggregation faces systemic quality issues, failing to meet societal needs.
  • Current models struggle with data quality, limiting scientific progress and impact.

Purpose of the Study:

  • To propose a novel, decentralized approach for biodiversity data aggregation.
  • To leverage the principles of the World Wide Web for improved data quality and engagement.

Main Methods:

  • Implementing a decentralized coordination model for data across scientific languages and communities.
  • Empowering localized communities to control their data infrastructures and versioning.
  • Developing automatable data sharing agreements for cross-community propagation and translation.

Main Results:

  • Fostering emergence of self-identifying communities of practice (regional, taxonomic, institutional).
  • Creating loosely connected networks of mid-level data aggregators.
  • Addressing integration challenges, particularly conflicting classifications and phylogenies.

Conclusions:

  • Decentralization offers long-term benefits: sustained expert engagement, higher-quality data, and greater societal impact.
  • Global coordination should prioritize shared languages for data signal translation over data homogenization.
  • This model enables flexible, scalable biodiversity data management and utilization.