Four key challenges in the open‐data revolution

Abstract

In Focus: Culina, A., Adriaensen, F., Bailey, L. D., et al. (2021) Connecting the data landscape of long‐term ecological studies: The SPI‐Birds data hub. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13388. Long‐term, individual‐based datasets have been at the core of many key discoveries in ecology, and calls for the collection, curation and release of these kinds of ecological data are contributing to a flourishing open‐data revolution in ecology. Birds, in particular, have been the focus of international research for decades, resulting in a number of uniquely long‐term studies, but accessing these datasets has been historically challenging. Culina et al. (2021) introduce an online repository of individual‐level, long‐term bird records with ancillary data (e.g. genetics), which will enable key ecological questions to be answered on a global scale. As well as these opportunities, however, we argue that the ongoing open‐data revolution comes with four key challenges relating to the (1) harmonisation of, (2) biases in, (3) expertise in and (4) communication of, open ecological data. Here, we discuss these challenges and how key efforts such as those by Culina et al. are using FAIR (Findable, Accessible, Interoperable and Reproducible) principles to overcome them. The open‐data revolution will undoubtedly reshape our understanding of ecology, but with it the ecological community has a responsibility to ensure this revolution is ethical and effective.

A standard protocol to report discrete stage-structured demographic information

“…Open data in ecology is a growing movement (Nadrowski et al, 2013;Gallagher et al, 2020;Salguero-Gómez et al, 2021). As we move ever further into the data-driven era of quantitative biology, data standards become increasingly important (Gallagher et al, 2020) Other fields have jumped on this standardisation bandwagon (e.g., meta-analyses (PRISMA method: Moher et al, 2009Moher et al, , 2015, genome/amino acid sequences (FASTA format: e.g., Pearson, 2016) and qPCR detection limits (Forootan et al, 2017)).…”

“…Data standardisation improves reproducibility and promotes data sharing across research disciplines (Reichman, Jones, & Schildhauer, 2011;Augusiak, van den Brink, & Grimm, 2014). Data standardization is therefore key for research to be replicated, validated, openly discussed, and ultimately for science to advance (Gilliland, 2008;Baker & Millerand, 2010;Reichman et al, 2011;Qin, Ball, & Greenberg, 2012;Peres-Neto, 2016;Powers & Hampton, 2019;Salguero-Gómez, Jackson, & Gascoigne, 2021). Examples of these standards include reporting sample size and variance of estimates and detailing the full list of original sources of data (Gerstner et al, 2017).…”

Avoiding growing pains in reproductive trait databases: the curse of dimensionality

“…The compilation of reproductive traits has been a remarkable effort that has allowed researchers to access broad‐scale questions (Fritz et al, 2009; Newbold et al, 2015; Tucker et al, 2018). Fortunately, reproductive trait databases do not appear to be limited by the inaccessibility issues that others have highlighted for databases of other traits (e.g., Hipsley & Sherratt, 2019; Salguero‐Gómez et al, 2021; Savage & Vickers, 2009; Wicherts et al, 2006). We want to emphasize that our critique regarding the under‐reporting of various reproductive measures is not a criticism of the original data collectors, nor those who assembled these databases.…”

“…Likewise, a 30-year-old fish database, FishBase (Froese & Pauly, 2021), has provided access to a host of fundamental and applied questions that were previously inaccessible. We are eager advocates for, and frequent users of, biological databases (Barneche et al, 2018;White et al, 2019White et al, , 2021, but as these databases grow, so do the potential challenges and risks of using them (Mills et al, 2015;Salguero-Gómez et al, 2021;Wilkinson et al, 2016).…”

Ten (mostly) simple rules to future-proof trait data in ecological and evolutionary sciences

“…S. Madin et al, 2016). However, these datasets bear a variety of new challenges linked to harmonisation, biases, expertise and communication (Salguero-Gómez et al, 2021). These challenges result in a major trade-off between investing in collection of new trait data or reusing open trait data (Westoby et al, 2021).…”