What's in a Badge? A Computational Reproducibility Investigation of the Open Data Badge Policy in One Issue of Psychological Science

Tactics of just, equitable, diverse, and inclusive scientific research

The principles of social justice, equity, diversity, inclusion (JEDI) have received increasing attention in behavior analysis circles, but the conversation has largely centered on implications for applied behavior analysis practice and research. It may be less clear to researchers who conduct basic and translational research how JEDI principles can inform and inspire their work. This article synthesizes publications from behavior analysis and other scientific fields about tactics of JEDI-informed research. We organized this scholarship across five stages of research from developing the research question to sharing findings and curated sources for an audience of behavioral science researchers. We discuss reflexive practice, representation, belongingness, participatory research, quantitative critical theory, and open science, among other topics. Some researchers may have already adopted some of the practices outlined, some may begin new practices, and some may choose to conduct experimental analyses of JEDI problems. Our hope is that those actions will be reinforced by the behavior analysis scientific community. We conclude by encouraging the leadership of this journal to continue to work toward the structural changes necessary to make the experimental analysis of behavior just, equitable, diverse, and inclusive.

A guide for social science journal editors on easing into open science

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org ) has collated several resources on embedding open science in journal editing ( www.dpjedi.org/resources ). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx ).

Systematic review search strategies are poorly reported and not reproducible: a cross-sectional metaresearch study

OBJECTIVES

To determine the reproducibility of biomedical systematic review search strategies.

STUDY DESIGN AND SETTING

A cross-sectional reproducibility study was conducted on a random sample of 100 systematic reviews indexed in MEDLINE in November 2021. The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced, operationalized as fulfilling six key Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) reporting guideline items and having all database searches reproduced within 10% of the number of original results. Key reporting guideline items included database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records.

RESULTS

The 100 systematic review articles contained 453 database searches. Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; six searches differed by more than 1,000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary search details to be fully reproducible.

CONCLUSION

Systematic review search reporting is poor. To correct this will require a multifaceted response from authors, peer reviewers, journal editors, and database providers.

Computational reproducibility of Jupyter notebooks from biomedical publications

BACKGROUND

Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows, including for research publications. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications.

APPROACH

We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article's full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion.

RESULTS

Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions.

CONCLUSIONS

We zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.

Open Science 2.0: Towards a truly collaborative research ecosystem

Conversations about open science have reached the mainstream, yet many open science practices such as data sharing remain uncommon. Our efforts towards openness therefore need to increase in scale and aim for a more ambitious target. We need an ecosystem not only where research outputs are openly shared but also in which transparency permeates the research process from the start and lends itself to more rigorous and collaborative research. To support this vision, this Essay provides an overview of a selection of open science initiatives from the past 2 decades, focusing on methods transparency, scholarly communication, team science, and research culture, and speculates about what the future of open science could look like. It then draws on these examples to provide recommendations for how funders, institutions, journals, regulators, and other stakeholders can create an environment that is ripe for improvement.

Perspectives on scientific error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings.