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  1. Home
  3. Best Practices For Building And Curating Databases For Comparative Analyses.
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  3. Best Practices For Building And Curating Databases For Comparative Analyses.

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Best practices for building and curating databases for comparative analyses.

Lisa E Schwanz1, Alex Gunderson2, Maider Iglesias-Carrasco3

  • 1Evolution and Ecology Research Centre, and the School of Biological, Earth, and Environmental Sciences, University of New South Wales, Sydney, NSW 2035, Australia.

The Journal of Experimental Biology
|March 8, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

Building robust databases is crucial for comparative ecological and evolutionary research. This commentary offers practical tips on literature searching, database structuring, version control, and accessibility to enhance research efficiency and data reuse.

Keywords:
BiomechanicsMorphologyOpen sciencePhylogeneticsPhysiologySystematic review

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Area of Science:

  • Macro-ecology and macro-evolutionary studies.

Background:

  • Comparative analyses rely heavily on curated databases for macro-ecological and evolutionary research.
  • Database construction is a critical but often undertrained aspect of scientific research.

Purpose of the Study:

  • To provide guidance on building effective databases for comparative analyses.
  • To highlight common challenges and offer practical solutions based on author experiences.

Main Methods:

  • Focus on four key strategies: literature search planning, multi-use database structuring, version control implementation, and data accessibility.
  • Discuss how study goals and data types influence database design.

Main Results:

  • Single-purpose databases limit reusability and create research inefficiencies.
  • Broader databases offer a higher return on investment and promote efficiency.

    • Conclusions:

    • Adopting best practices in database construction is essential for advancing comparative research.
    • Community curation of databases can significantly improve research efficiency and data sharing.