Our path to better science in less time using open data science tools

Lowndes, Julia S. Stewart; Best, Benjamin; Scarborough, Courtney; Afflerbach, Jamie C.; Frazier, Melanie; O’Hara, Casey C.; Jiang, Ning; Halpern, Benjamin S.

doi:10.1038/s41559-017-0160

Cited by 227 publications

(246 citation statements)

References 49 publications

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“…, Lortie , Lowndes et al. ) and in part due to challenges in replicating the methods used to handle diverse ecological data. R is an ideal mechanism to break through these limitations because it can be used to describe workflows and decisions in treating and modeling data in ecology.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the popularity of R in ecology

et al. 2019

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The programming language R is widely used in many fields. We explored the extent of reported R use in the field of ecology using the Web of Science and text mining. We analyzed the frequencies of R packages reported in more than 60,000 peer-reviewed articles published in 30 ecology journals during a 10-yr period ending in 2017. The number of studies reported using R as their primary tool in data analysis increased linearly from 11.4% in 2008 to 58.0% in 2017. The top 10 packages reported were lme4, vegan, nlme, ape, MuMIn, MASS, mgcv, ade4, multcomp, and car. The increasing popularity of R has most likely furthered open science in ecological research because it can improve reproducibility of analyses and captures workflows when scripts and codes are included and shared. These findings may not be entirely unique to R because there are other programming languages used by ecologists, but they do strongly suggest that given the relatively high frequency of reported use of R, it is a significant component of contemporary analytics in the field of ecology.

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Section: Discussionmentioning

confidence: 99%

Evaluating the popularity of R in ecology

et al. 2019

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“…Simultaneously, computational reproducibility can aid in transparency (Lowndes et al 2017). In ecological applications such as natural resource management and policy issues, high transparency may be essential for scientists and scientific institutions to maintain public trust (Reichman et al 2011, Michener and Jones 2012, Michener 2015, Schimel and Keller 2015.…”

Section: Transparencymentioning

confidence: 99%

Open science, reproducibility, and transparency in ecology

Powers

Hampton

2018

Ecological Applications

150

151

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Reproducibility is a key tenet of the scientific process that dictates the reliability and generality of results and methods. The complexities of ecological observations and data present novel challenges in satisfying needs for reproducibility and also transparency. Ecological systems are dynamic and heterogeneous, interacting with numerous factors that sculpt natural history and that investigators cannot completely control. Observations may be highly dependent on spatial and temporal context, making them very difficult to reproduce, but computational reproducibility can still be achieved. Computational reproducibility often refers to the ability to produce equivalent analytical outcomes from the same data set using the same code and software as the original study. When coded workflows are shared, authors and editors provide transparency for readers and allow other researchers to build directly and efficiently on primary work. These qualities may be especially important in ecological applications that have important or controversial implications for science, management, and policy. Expectations for computational reproducibility and transparency are shifting rapidly in the sciences. In this work, we highlight many of the unique challenges for ecology along with practical guidelines for reproducibility and transparency, as ecologists continue to participate in the stewardship of critical environmental information and ensure that research methods demonstrate integrity.

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“…There is significant motivation from within the ecological community to move toward providing detailed information about computational workflows for both repeatability and reproducibility, which includes repetition by the original investigator (Lowndes et al. ). It is also important in network ecology for data sources and methods for model construction be standardized and transparent, and that models be curated and shared (McNutt et al.…”

Section: Reproducibility: Open‐data Open‐source and Provenancementioning

confidence: 99%

Ecological network metrics: opportunities for synthesis

et al. 2017

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Abstract. Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g., stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics and their terminology across sub-disciplines; benchmarking new network algorithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular "model" data provenance, to increase the reproducibility of network models and analyses. We propose that applying these solutions will aid in synthesizing ecological sub-disciplines and allied fields by improving the accessibility of network methods and models.

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Our path to better science in less time using open data science tools

Cited by 227 publications

References 49 publications

Evaluating the popularity of R in ecology

Evaluating the popularity of R in ecology

Open science, reproducibility, and transparency in ecology

Ecological network metrics: opportunities for synthesis

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