Report by the AEA Data Editor

doi:10.1257/pandp.109.718

“…Further, the amount of time needed to engage in open science should decrease with practice and ultimately lead to faster and more efficient development of psychological theories (Alter & Gonzalez, 2018; Sandve et al, 2013). As various scientific fields continue to move toward open data science practices, resources and guidelines on how to practice open science on individual and structural levels are becoming increasingly available (e.g., Christian et al, 2018; Eglen et al, 2017; Klein et al, 2018; Nosek et al, 2015; Sandve et al, 2013; Vilhuber, 2019). Importantly, resources will be needed to create the individual and structural level changes necessary to maintain these open science practices including the time and monetary costs of operating data repositories as well as providing open science training for professionals in psychology (e.g., how to share data and code in a reproducible way; e.g., Tenopir et al, 2011).…”

Section: Recommendationsmentioning

confidence: 99%

“…With these things in mind, we also recommend that journals employ professionals to ensure data are received and to evaluate the quality and reproducibility of any data and code that are shared, as this important work should not fall on the shoulders of already overburdened, volunteer reviewers 2 . Although these additional prepublication steps may initially slow down the review process (e.g., Christian et al, 2018), with dedicated professionals and adequate allocation of resources, this additional time cost should decrease over time (Vilhuber, 2019).…”

Section: Recommendationsmentioning

confidence: 99%

Sharing is caring: Ethical implications of transparent research in psychology.

Bosma¹,

Granger²

2022

American Psychologist

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The call for greater openness in research data is quickly growing in many scientific fields. Psychology as a field, however, still falls short in this regard. Research is vulnerable to human error, inaccurate interpretation, and reporting of study results, and decisions during the research process being biased toward favorable results. Despite the obligation to share data for verification and the importance of this practice for protecting against human error, many psychologists do not fulfill their ethical responsibility of sharing their research data. This has implications for the accurate and ethical dissemination of specific research findings and the scientific development of the field more broadly. Open science practices provide promising approaches to address the ethical issues of inaccurate reporting and false-positive results in psychological research literature that hinder scientific growth and ultimately violate several relevant ethical principles and standards from the American Psychological Association's (APA's) Ethical Principles of Psychologists Code of Conduct (APA, 2017). Still, current incentive structures in the field for publishing and professional advancement appear to induce hesitancy in applying these practices. With each of these considerations in mind, recommendations on how psychologists can ethically proceed through open science practices and incentive restructuring-in particular, data management, data and code sharing, study preregistration, and registered reports-are provided.

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“…A recommendation of the recent National Academies' report on Reproducibility and Replicability in Science is that journals "consider ways to ensure computational reproducibility for publications that make claims based on computations" (Committee on Reproducibility and Replicability in Science, 2019). In fields such as political science and economics, journals are increasingly adopting policies that require authors to publish the code and data required to reproduce computational findings reported in published manuscripts, subject to independent verification (Jacoby et al, 2017;Vilhuber, 2019;Alvarez et al, 2018;Christian et al, 2018;Eubank, 2016;King, 1995). Problems with the computational environment, installation and availability of software dependencies are common.…”

Section: Packaging Research Reproduciblymentioning

confidence: 99%

The Rockerverse: Packages and Applications for Containerisation with R

Nüst¹,

Eddelbuettel²,

Bennett³

et al. 2020

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The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software.

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“…A recommendation of the recent National Academies' report on Reproducibility and Replicability in Science is that journals "consider ways to ensure computational reproducibility for publications that make claims based on computations" (Committee on Reproducibility and Replicability in Science, 2019). In fields such as political science and economics, journals are increasingly adopting policies that require authors to publish the code and data required to reproduce computational findings reported in published manuscripts, subject to independent verification (Jacoby et al, 2017;Vilhuber, 2019;Alvarez et al, 2018;Christian et al, 2018;Eubank, 2016;King, 1995). Problems with the computational environment, installation and availability of software dependencies are common.…”

Section: Packaging Research Reproduciblymentioning

confidence: 99%

The Rockerverse: Packages and Applications for Containerization with R

Nüst,

Eddelbuettel,

Bennett

et al. 2020

Preprint

2

0

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The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software.

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Report by the AEA Data Editor

Cited by 23 publications

References 30 publications

Sharing is caring: Ethical implications of transparent research in psychology.

Sharing is caring: Ethical implications of transparent research in psychology.

The Rockerverse: Packages and Applications for Containerisation with R

The Rockerverse: Packages and Applications for Containerization with R

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