Best practices for building and curating databases for comparative analyses

Abstract

Comparative analyses have a long history of macro-ecological and -evolutionary approaches to understand structure, function, mechanism and constraint. As the pace of science accelerates, there is ever-increasing access to diverse types of data and open access databases that are enabling and inspiring new research. Whether conducting a species-level trait-based analysis or a formal meta-analysis of study effect sizes, comparative approaches share a common reliance on reliable, carefully curated databases. Unlike many scientific endeavors, building a database is a process that many researchers undertake infrequently and in which we are not formally trained. This Commentary provides an introduction to building databases for comparative analyses and highlights challenges and solutions that the authors of this Commentary have faced in their own experiences. We focus on four major tips: (1) carefully strategizing the literature search; (2) structuring databases for multiple use; (3) establishing version control within (and beyond) your study; and (4) the importance of making databases accessible. We highlight how one's approach to these tasks often depends on the goal of the study and the nature of the data. Finally, we assert that the curation of single-question databases has several disadvantages: it limits the possibility of using databases for multiple purposes and decreases efficiency due to independent researchers repeatedly sifting through large volumes of raw information. We argue that curating databases that are broader than one research question can provide a large return on investment, and that research fields could increase efficiency if community curation of databases was established.

A comprehensive database of amphibian heat tolerance

“…Data were organised in a ‘stacked’ format (following the recommendations of 267 ), to facilitate their use in comparative studies. Note that these data can be converted to an ‘effect size’ format for meta-analysis.…”

“…For a meta-analysis, one may convert the database to an ‘effect size’ format ( sensu Schwanz et al . 267 ) and use stepwise comparisons between acclimation temperatures as effect sizes 14 , 271 . Other variables affecting upper thermal limits include geographical origin 9 , 272 , ontogeny 11 , 14 , 273 , assay duration 274 , 275 , upper thermal limit metrics 10 , body size 16 , 26 , 35 , sex 271 , 276 , and photoperiod 111 .…”

Size scaling of sea turtle reproduction may reconcile fundamental ecology and conservation strategies at the global scale

“…Data curation was formatted following Schwanz et al. (2022), and a detailed description of the data extracted for the analysis is available in the Supporting Information (Table S2).…”

“…Air and/or nest temperatures during the study period were also obtained where applicable. Data curation was formatted following Schwanz et al (2022), and a detailed description of the data extracted for the analysis is available in the Supporting Information (Table S2).…”

Looking to the future: Building New Paradigms in Comparative Physiology and Biomechanics

“…Handling and analysing large datasets collected and compiled from multiple published studies is complex and requires a structured and rigorous approach. The Commentary by Lisa Schwanz and colleagues provides a valuable guide for building and curating databases for comparative analyses (Schwanz et al, 2022). The authors provide practical approaches to maximising the efficiency and accessibility of comparative databases, which in turn helps to promote and ensure data transparency and integrity.…”