Leveraging Containers for Reproducible Psychological Research

Wiebels, Kristina; Moreau, David

doi:10.31234/osf.io/h7tkg

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…Researchers can make use of tools that promote backwards compatibility and portability of software and packages within different operating systems. These tools include software containers, which store all packages used alongside the code [103] (e.g. Docker, originally designed for app developers, ‘renv’ for R [104] and ‘conda’ for Python ()).…”

Section: Reuse Concernsmentioning

confidence: 99%

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

et al. 2022

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The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

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Section: Reuse Concernsmentioning

confidence: 99%

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

et al. 2022

Self Cite

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“…These libraries essentially package software versions along with the relevant code so that the original analysis can be reproduced even after software is updated. 44,45 Such workflows also facilitate returning to one's own code in the future.…”

Section: Sharing Data and Codementioning

confidence: 99%

Toward Open and Reproducible Epidemiology

Mathur

Fox

2023

American Journal of Epidemiology

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Starting in the 2010s, researchers in the experimental social sciences rapidly began to adopt increasingly open and reproducible scientific practices. These practices include publicly sharing deidentified data when possible, sharing analysis code, and preregistering study protocols. Empirical evidence from the social sciences suggests such practices are feasible, can improve analytic reproducibility, and can reduce selective reporting. In academic epidemiology, adoption of open-science practices has been slower than in the social sciences (with some notable exceptions, such as registering clinical trials). Epidemiologic studies are often large, complex, conceived after data have already been collected, and difficult to directly replicate by collecting new data. These characteristics makes it especially important to ensure their integrity and analytic reproducibility. Open-science practices can also pay immediate dividends to researchers’ own work by clarifying scientific reasoning and encouraging well-documented, organized workflows. We consider how established epidemiologists and early-career researchers alike can help midwife a culture of open science in epidemiology through their research practices, mentorship, and editorial activities.

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“…Researchers can make use of tools that promote backwards compatibility and portability of software and packages within different operating systems. These tools include software containers, which store all packages used alongside the code [81] (e.g., Docker, originally designed for app developers, 'renv' for R [82], and 'conda' for Python [https://docs.conda.io/en/latest/]). Bindr [https://mybinder.org] allows users to interactively run code (e.g.…”

Section: ) Transient Storagementioning

confidence: 99%

Why don’t we share data and code? Perceived barriers and benefits to public archiving practices

Gomes¹,

Pottier²,

Crystal‐Ornelas³

et al. 2022

Preprint

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The biological sciences community is increasingly recognizing the value of open, reproducible, and transparent research practices for science and society at large. Despite this recognition, many researchers remain reluctant to share their data and code publicly. This hesitation may arise from knowledge barriers about how to archive data and code, concerns about its re-use, and misaligned career incentives. Here, we define, categorise, and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

show abstract

Leveraging Containers for Reproducible Psychological Research

Cited by 6 publications

References 13 publications

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

Toward Open and Reproducible Epidemiology

Why don’t we share data and code? Perceived barriers and benefits to public archiving practices

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