The UK House of Commons Science and Technology Committee has called for evidence on the roles that different stakeholders play in reproducibility and research integrity. Of central priority are proposals for improving research integrity and quality, as well as guidance and support for researchers. In response to this, we argue that there is one important component of research integrity that is often absent from discussion: the pedagogical consequences of how we teach, mentor, and supervise students through open scholarship. We justify the need to integrate open scholarship principles into research training within higher education and argue that pedagogical communities play a key role in fostering an inclusive culture of open scholarship. We illustrate these benefits by presenting the Framework for Open and Reproducible Research Training (FORRT), an international grassroots community whose goal is to provide support, resources, visibility, and advocacy for the adoption of principled, open teaching and mentoring practices, whilst generating conversations about the ethics and social impact of higher-education pedagogy. Representing a diverse group of early-career researchers and students across specialisms, we advocate for greater recognition of and support for pedagogical communities, and encourage all research stakeholders to engage with these communities to enable long-term, sustainable change.
“Coordination is needed with related policy domains, including open science, which enhances transparency into research processes and outputs….
Efforts to implement open science can make more of the process and outputs of scientific research freely and readily accessible to other scientists, engineers, policymakers, students and educators, and the general public, while maintaining needed protections of national security, personal privacy, and other sensitive information. By making research publications, study data, analytical software and code, and study protocols more readily available for inspection and reuse—as Federal science agencies are currently doing—open science affords new opportunities to detect instances of interference, mischaracterization, and other policy violations. As such, open science is an essential enabler of scientific integrity….
Open science policies and practices provide transparency to help ensure that publications, data, and other outputs of Federally funded research are readily available to other researchers, innovators, students, and the public (taking into consideration legal and ethical limitations on access, such as national security and privacy)….
Facilitate free flow of scientific and technological information, by availability online in open formats and, where appropriate, including data and models underlying regulatory proposals and policy decisions…. ”
“In 2020, the importance of open and rapid communication of academic research came to the fore, as possibly never before, in the global effort to address the COVID-19 pandemic. The pandemic arrived at a time when much of the infrastructure for sharing research openly and rapidly was already in place, and to a large extent, the global publishing enterprise was able to fulfill its function of dissemination of information.
However, we are already seeing signs that publishing may revert to a more closed model post pandemic. It is also clear that the pandemic has exacerbated some of the problems in scholarly communication, such as a worsening participation by women and unequal distribution of funding globally. Furthermore, it is not clear that some of the innovations developed in the pandemic for sharing of information—such as the CORD-19 dataset of publications—will endure in their current state. Finally, the sheer volume of publishing, especially through relatively novel mechanisms, such as preprints, has led to uncertainty about how to support trust in research publications, both in the academic community and in the wider public.”
Abstract: Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. Further analysis reveals that these graphical integrity issues are significantly more prevalent in some research fields, such as psychology and computer science, and some regions of the globe. Additionally, we find no temporal and seniority trends in violations. Finally, apart from openly releasing our large annotated dataset and method, we discuss how computational research integrity could be part of peer-review and the publication processes.