Open Science principles for accelerating trait-based science across the Tree of Life

Rachael V. Gallagher(Macquarie University), Daniel S. Falster(UNSW Sydney), Brian Maitner(University of Arizona), Roberto Salguero‐Gómez(The University of Queensland), Vigdis Vandvik(Bjerknes Centre for Climate Research), William D. Pearse(Utah State University), Florian D. Schneider, Jens Kattge(German Centre for Integrative Biodiversity Research), Jorrit H. Poelen, Joshua S. Madin(University of Hawaiʻi at Mānoa), Markus J. Ankenbrand(University of Würzburg), Caterina Penone(University of Bern), Xiao Feng(University of Arizona), Vanessa M. Adams(University of Tasmania), John Alroy(Macquarie University), Samuel C. Andrew(Commonwealth Scientific and Industrial Research Organisation), Meghan A. Balk(University of Arizona), Lucie M. Bland(Deakin University), Brad Boyle(University of Arizona), Catherine H. Bravo‐Avila(University of Miami), Ian G. Brennan(Australian National University), Alexandra J. R. Carthey(Macquarie University), Renee A. Catullo(Australian National University), Brittany R. Cavazos(Iowa State University), Dalia A. Conde(University of Southern Denmark), Steven L. Chown(Monash University), Belén Fadrique(University of Miami), Heloise Gibb(La Trobe University), Aud H. Halbritter(Bjerknes Centre for Climate Research), Jennifer Hammock(Smithsonian Institution), J. Aaron Hogan(Florida International University), Hamish Holewa(Commonwealth Scientific and Industrial Research Organisation), Michael Hope(Commonwealth Scientific and Industrial Research Organisation), Colleen M. Iversen(Oak Ridge National Laboratory), Malte Jochum(University of Bern), Michael Kearney(The University of Melbourne), Alexander Keller(University of Würzburg), Paula Mabee(University of South Dakota), Peter Manning(Senckenberg Biodiversity and Climate Research Centre), Luke McCormack(Morton Arboretum), Sean T. Michaletz(University of British Columbia), Daniel Park(Harvard University), Timothy M. Perez(University of Miami), Silvia Pineda‐Munoz(Georgia Institute of Technology), Courtenay A. Ray(Arizona State University), Maurizio Rossetto(The University of Queensland), Hervé Sauquet(Université Paris-Sud), Benjamin Sparrow(The University of Adelaide), Marko J. Spasojevic(University of California, Riverside), Richard J. Telford(Bjerknes Centre for Climate Research), Joseph A. Tobias(Imperial College London), Cyrille Violle(Centre National de la Recherche Scientifique), Ramona Walls(University of Arizona), Katherine Weiss(Arizona State University), Mark Westoby(Macquarie University), Ian J. Wright(Macquarie University), Brian J. Enquist(Santa Fe Institute)
Nature Ecology & Evolution
February 17, 2020
Cited by 228Open Access
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Abstract

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.


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