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

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Version 2 2024-06-06, 04:26
Version 1 2020-01-01, 00:00
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posted on 2024-06-06, 04:26 authored by RV Gallagher, DS Falster, BS Maitner, R Salguero-Gómez, V Vandvik, WD Pearse, FD Schneider, J Kattge, JH Poelen, JS Madin, MJ Ankenbrand, C Penone, X Feng, VM Adams, J Alroy, SC Andrew, MA Balk, LM Bland, BL Boyle, CH Bravo-Avila, I Brennan, AJR Carthey, R Catullo, BR Cavazos, DA Conde, SL Chown, B Fadrique, H Gibb, AH Halbritter, J Hammock, JA Hogan, H Holewa, M Hope, CM Iversen, M Jochum, M Kearney, A Keller, P Mabee, P Manning, L McCormack, ST Michaletz, DS Park, TM Perez, S Pineda-Munoz, CA Ray, M Rossetto, H Sauquet, B Sparrow, MJ Spasojevic, RJ Telford, JA Tobias, C Violle, R Walls, KCB Weiss, M Westoby, IJ Wright, BJ Enquist
© 2020, Springer Nature Limited. 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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Location

London, Eng.

Open access

  • Yes

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Journal

Nature Ecology and Evolution

Volume

4

Pagination

294-303

eISSN

2397-334X

Publisher

Springer Nature

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