Open science

Michael G Bertram; Josefin Sundin; Dominique G Roche; Alfredo Sánchez-Tójar; Eli S J Thoré; Tomas Brodin

doi:10.1016/j.cub.2023.05.036

Open science

Curr Biol. 2023 Aug 7;33(15):R792-R797. doi: 10.1016/j.cub.2023.05.036.

Authors

Michael G Bertram¹, Josefin Sundin², Dominique G Roche³, Alfredo Sánchez-Tójar⁴, Eli S J Thoré⁵, Tomas Brodin⁶

Affiliations

¹ Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden; Department of Zoology, Stockholm University, Stockholm, Sweden; School of Biological Sciences, Monash University, Melbourne, Australia. Electronic address: [email protected].
² Department of Aquatic Resources, Swedish University of Agricultural Sciences, Drottningholm, Sweden.
³ Department of Biology, Carleton University, Ottawa, Canada; Institut de Biologie, Université de Neuchâtel, Neuchâtel, Switzerland.
⁴ Department of Evolutionary Biology, Bielefeld University, Germany.
⁵ Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden; Laboratory of Animal Ecology, Global Change and Sustainable Development, KU Leuven, Leuven, Belgium; TRANSfarm - Science, Engineering & Technology Group, KU Leuven, Lovenjoel, Belgium.
⁶ Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden.

PMID: 37552940
DOI: 10.1016/j.cub.2023.05.036

Abstract

The term 'open science' refers to a range of methods, tools, platforms and practices that aim to make scientific research more accessible, transparent, reproducible and reliable. This includes, for example, sharing code, data and research materials, embracing new publishing formats such as registered reports and preprints, pursuing replication studies and reanalyses, optimising statistical approaches to improve evidence assessment and re-evaluating institutional incentives. The ongoing shift towards open science practices is partly due to mounting evidence that studies across disciplines suffer from biases, underpowered designs and irreproducible or non-replicable results. It also stems from a general desire amongst many researchers to reduce hyper-competitivity in science and instead promote collaborative research that benefits science and society.

MeSH terms

Humans
Motivation*
Publishing*
Research Personnel