Increasing the transparency, openness and replicability of psychological research: Mandatory data sharing for empirical studies in the Journal of Health Psychology – David F Marks,

Abstract:  This editorial announces this journal’s policy on transparency, openness and replication. From 1 July 2020, authors of manuscripts submitted to Journal of Health Psychology (JHP) are required to make the raw data fully accessible to all readers. JHP will only consider manuscripts which follow an open publication model defined as follows: M = Mandatory, I = Inclusion (of), R = Raw, D = Data (MIRD). All data and analytical procedures must be sufficiently well described to enable a third party with the appropriate expertise to replicate the data analyses. It is expected that findings and analyses in the JHP will be fully capable of being accurately reproduced.

 

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

“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.

 

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

“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.

 

The citation advantage of linking publications to research data

Abstract:  Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository—rather than being available on request or included as supporting information files—are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results.

 

Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

Abstract:  Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results. This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors’ ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility. This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements. To do this, we conducted a mixed-methods study that examined individual data policies alongside editors’ and authors’ interpretation of policy requirements to answer the following research questions. Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors’ assertion of data policy requirements, authors’ understanding of policy requirements, and the requirements stated in the policy language as written. We offer explanations for these discrepancies and offer recommendations for improving authors’ understanding of policies and increasing the likelihood of policy compliance.

 

Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

Abstract:  Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results. This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors’ ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility. This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements. To do this, we conducted a mixed-methods study that examined individual data policies alongside editors’ and authors’ interpretation of policy requirements to answer the following research questions. Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors’ assertion of data policy requirements, authors’ understanding of policy requirements, and the requirements stated in the policy language as written. We offer explanations for these discrepancies and offer recommendations for improving authors’ understanding of policies and increasing the likelihood of policy compliance.

 

Journal transparency index will be ‘alternative’ to impact scores | Times Higher Education (THE)

“A new ranking system for academic journals measuring their commitment to research transparency will be launched next month – providing what many believe will be a useful alternative to journal impact scores.

Under a new initiative from the Center for Open Science, based in Charlottesville, Virginia, more than 300 scholarly titles in psychology, education and biomedical science will be assessed on 10 measures related to transparency, with their overall result for each category published in a publicly available league table.

The centre aims to provide scores for about 1,000 journals within six to eight months of their site’s launch in early February….”

A study of the impact of data sharing on article citations using journal policies as a natural experiment

Abstract:  This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.