Making sense of journal research data policies

Naughton, Linda; Kernohan, David

doi:10.1629/uksg.284

Cited by 27 publications

(22 citation statements)

References 6 publications

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“…In recent years research data policies have been used by funding agencies, publishers and research institutions to drive research data sharing. 5 Requirements across these stakeholders vary, but tend to include common aspects, such as the preparation of data management plans, data archiving, citation of data sets and inclusion of data availability statements in published research papers; and in some cases, the peer review of research data. In October 2018 the Belmont Forum, which represents 26 funding agencies internationally, released a draft position paper 6 outlining requirements for data availability statements to be made available outside any paywalls applied to articles, and the minimum features they should include, such as persistent identifiers for the data set and licensing and access information.…”

Section: Introductionmentioning

confidence: 99%

Implementing publisher policies that inform, support and encourage authors to share data: two case studies

Jones¹,

Grant²,

Hrynaszkiewicz³

2019

Insights the UKSG Journal

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Section: Introductionmentioning

confidence: 99%

Implementing publisher policies that inform, support and encourage authors to share data: two case studies

Jones¹,

Grant²,

Hrynaszkiewicz³

2019

Insights the UKSG Journal

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“…Journal research data policies have previously been studied in the fields of environmental sciences (Weber et al 2010), political science (Gherghina and Katsanidou 2013), genetics (Moles 2014), social sciences (Herndon and O'Reilly 2016;Crosas et al 2018), biomedical sciences (Vasilevsky et al 2017) and through multidisciplinary approaches (Piwowar and Chapman 2008;Sturges et al 2015;Blahous et al 2016;Naughton and Kernohan 2016;Castro et al 2017;Resnik et al 2019). Within the findings of recent multidisciplinary studies on journal research data policies, circa 50-65% of journals had a research data policy and 20-30% of these policies were either classified as strong policies or mandated data sharing into a public repository (Sturges et al 2015;Blahous et al 2016;Naughton and Kernohan 2016). Furthermore, studies suggest that journals with high Impact Factors also have the strongest data sharing policies (Vasilevsky et al 2017;Resnik et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Journal research data sharing policies: a study of highly-cited journals in neuroscience, physics, and operations research

Rousi

null

2020

Scientometrics

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The practices for if and how scholarly journals instruct research data for published research to be shared is an area where a lot of changes have been happening as science policy moves towards facilitating open science, and subject-specific repositories and practices are established. This study provides an analysis of the research data sharing policies of highly-cited journals in the fields of neuroscience, physics, and operations research as of May 2019. For these 120 journals, 40 journals per subject category, a unified policy coding framework was developed to capture the most central elements of each policy, i.e. what, when, and where research data is instructed to be shared. The results affirm that considerable differences between research fields remain when it comes to policy existence, strength, and specificity. The findings revealed that one of the most important factors influencing the dimensions of what, where and when of research data policies was whether the journal's scope included specific data types related to life sciences which have established methods of sharing through community-endorsed public repositories. The findings surface the future research potential of approaching policy analysis on the publisher-level as well as on the journal-level. The collected data and coding framework is provided as open data to facilitate future research and journal policy monitoring.

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“…The vast majority of scientific journals require inclusion of processing and analysis methods in 'sufficient detail for reproduction' [e.g. 92,93-97], though journal requirements are diverse and complex [98], and the level of detail authors provide can vary greatly in practice [99,100]. More recently, many authors have highlighted that full reproducibility requires sharing data and resources at all stages of the scientific process, from raw data (including biological samples) to full methods and analysis workflows [1,9,72,100].…”

Section: Publishing Datamentioning

confidence: 99%

Best Practice Data Life Cycle Approaches for the Life Sciences

Griffin

Khadake

LeMay³

et al. 2017

Preprint

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Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

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Making sense of journal research data policies

Cited by 27 publications

References 6 publications

Implementing publisher policies that inform, support and encourage authors to share data: two case studies

Implementing publisher policies that inform, support and encourage authors to share data: two case studies

Journal research data sharing policies: a study of highly-cited journals in neuroscience, physics, and operations research

Best Practice Data Life Cycle Approaches for the Life Sciences

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