Responsible practices for data sharing.

Alter, George; Gonzalez, Richard

doi:10.1037/amp0000258

Cited by 75 publications

(78 citation statements)

References 23 publications

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“…We understand that many researchers feel protective of their data, whether because the data are proprietary, confidential, involve regulated threatened or endangered species, or because researchers have invested so much effort and time in collecting the data. For the former 3 reasons, there are ways to share aggregated, summary, or de‐identified data that would at least begin to improve reproducibility (Alter and Gonzalez 2018, Pérignon et al 2019). For authors concerned about receiving credit for their hard‐earned data, we note that the availability of data has been associated with higher citation rates (Vandewalle 2012, Piwowar and Vision 2013) and may lead to new collaborations.…”

Section: Discussionmentioning

confidence: 99%

Computational Reproducibility in The Wildlife Society's Flagship Journals

ArchMiller¹,

Johnson²,

Nolan³

et al. 2020

J Wildl Manag

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Scientific progress depends upon the accumulation of empirical knowledge via reproducible methodology. Although reproducibility is a main tenet of the scientific method, recent studies have highlighted widespread failures in adherence to this ideal. The goal of this study was to gauge the level of computational reproducibility, or the ability to obtain the same results using the same data and analytic methods as in the original publication, in the field of wildlife science. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those that were suitable for reproducibility review (n = 74), we attempted to obtain study data from online repositories or directly from authors. Forty‐two authors did not respond to our requests, and we were further unable to obtain data from authors of 13 other studies. Of the 19 studies for which we were able to obtain data and complete our analysis, we judged that 13 were mostly or fully reproducible. We conclude that the studies with publicly available data or data shared upon request were largely reproducible, but we remain concerned about the difficulty in obtaining data from recently published papers. We recommend increased data‐sharing, data organization and documentation, communication, and training to advance computational reproducibility in the wildlife sciences. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

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Section: Discussionmentioning

confidence: 99%

Computational Reproducibility in The Wildlife Society's Flagship Journals

ArchMiller¹,

Johnson²,

Nolan³

et al. 2020

J Wildl Manag

View full text Add to dashboard Cite

show abstract

“…Ensuring data protection and mitigating data misuse can be viewed as a broader societal function. This is particularly relevant in areas such as the variability of consent and in data use policies [39][40][41]. Areas related to levels of data control and tracking of permissions, cybersecurity, and risk of re-identification will be critical to address.…”

Section: Broader Policy-related Issuesmentioning

confidence: 99%

Translation of Digital Health Technologies to Advance Precision Medicine: Informing Regulatory Science

et al. 2020

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The proliferation of digital technologies and the application of sophisticated data analysis techniques are increasingly viewed as having the potential to transform translational research and precision medicine. While digital technologies are rapidly applied in innovative ways to develop new diagnostics and therapies, the ultimate approval and adoption of these emerging methods presents several scientific and regulatory challenges. To better understand and address these regulatory science gaps, a working group of the Clinical and Translational Science Awards Program convened the Regulatory Science to Advance Precision Medicine Forum focused on digital health, particularly examining gaps in the use, validation, and interpretation of data from sensors that collect and tools that analyze digital biomarkers. The key findings and recommendations provided here emerged from the Forum and include the need to enhance areas related to data standards, data quality and validity, knowledge management, and building trust between all stakeholders.

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“…An important element in overcoming barriers is crediting and rewarding investigators for sharing data, along with other artefacts such as materials or code. Citation is one basic form of awarding credit (Alter & Gonzalez, 2018) and datasets can become citable by receiving a persistent identifier, such as a digital object identifier (DOI), through platforms like DataCite (https://datacite.org/). Looking forward, owing to its conspicuous societal value, sharing of data (but also of protocols, materials, code etc) could be rewarded and incentivized, by incorporating it in assessments of scientists for hiring, promotion and tenure (Moher, et al, 2018).…”

Section: Fully Accessible Research Information?mentioning

confidence: 99%

Increase value and reduce waste in research on psychological therapies

Cristea

Naudet

2019

Behaviour Research and Therapy

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A seminal Lancet series focussed on increasing value and reducing waste in biomedical research, providing a transferrable template to diagnose problems in research. Our goal was to document how some of these sources of waste apply to mental health and particularly psychological treatments research. We synthesize and critically evaluate empirical findings in relation to four major sources: i) defining research priorities; ii) research design, methods and analysis; iii) accessibility of research information; iv) accuracy and usability of research reports. We demonstrate that each source of waste considered is well-represented and amply documented within this field. We describe hype and insufficient consideration of what is known in defining research priorities, persistent risk of bias, particularly due to selective outcome reporting, for psychotherapy trials across mental disorders, intellectual and financial biases, direct and indirect evidence of publication bias, largely inexistent adoption of data sharing, issues of multiplicity and fragmentation of data and findings, and insufficient adoption of reporting guidelines. We expand on a few general solutions, including supporting meta-research, properly testing interventions to increase research quality, placing open science at the center of psychological treatment research and remaining vigilant particularly regarding the strains of research currently prioritized, such as experimental psychopathology.

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Responsible practices for data sharing.

Cited by 75 publications

References 23 publications

Computational Reproducibility in The Wildlife Society's Flagship Journals

Computational Reproducibility in The Wildlife Society's Flagship Journals

Translation of Digital Health Technologies to Advance Precision Medicine: Informing Regulatory Science

Increase value and reduce waste in research on psychological therapies

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