Featuring the experiences of an open science advocate in pharma, an analysis of open science and diversity, an undergraduate peer-reviewed journal, the lessons learnt from COVID-19, alternatives to journal impact factors and a machine-learning approach to big data.
Open Pharma – an insider’s view via The Publication Plan
This week, Slávka Baróniková (Scientific Publications Lead at Galápagos) has shared her personal experiences with open access publishing from her time at Shire Pharmaceuticals (now part of Takeda) and at Galápagos. Slávka outlines the importance of stakeholder education and negotiation when developing mandatory open access policies and discusses how fine-tuning the definition of open access can be key to a successful proposal. Beyond internal strategies for open access, Slávka also considers the roles that authors, publishers and medical publications professionals can play in increasing open access publishing.
Accessibility and reproducibility should be two inseparable sides of the open science coin: if research aims to be reproducible, it should be accessible and vice versa. In addition, for science to truly be open, the barriers that make it difficult for women to be at the forefront of research need to be removed. Through network modelling and semantic analysis, this article explores open science and reproducibility literature to assess author diversity. Murphy and colleagues found that open science and reproducibility literature are emerging independently, with women being published more frequently in high-status author positions within open science literature than within reproducibility literature. The article features a list of suggested actions to create a diverse and forward-thinking scientific culture.
Publication, publication, publication – a new education via Journal of Microbiology and Biology Education
Researchers at the University of British Columbia have developed a peer-reviewed, open access journal dedicated to the publication of undergraduate research projects: The Undergraduate Journal of Experimental Microbiology and Immunology. Not only does this initiative improve students’ research competency and help them to develop their scientific identity, it also provides the opportunity to impress upon ‘tomorrow’s researchers’ the importance of open science. In addition, the journal offers graduate students a chance to be employed as journal editors.
Besançon et al. 2020: lessons from COVID-19 via bioRxiv
In a recent preprint, Lonni Besançon (Postdoctoral Fellow at Monash University) and colleagues explore the effect that COVID-19 has had on the status of preprints and open science as a whole. While the authors acknowledge the necessity of rapid and transparent research, they also consider how, in some cases, preprints have been misused either by overzealous researchers or by misinformed journalists. They highlight that these issues would not have arisen if preprint servers fully adopted open science principles such as open peer review.
The beginning of the end for journal impact factors via Scholastica
A blog post published last month on Scholastica explores alternative metrics to journal impact factors (JIFs) – including Altmetric for measuring article-level impact indicators, and journal rankings based on the Transparency and Openness Promotion (TOP) guidelines. JIFs are known to have limitations, as outlined in an article published in June 2020 in Quantitative Science Studies, which analysed the effect that individual articles can have on journal impact factors (JIFs). The analysis revealed that single publications can alter a JIF by more than 50%. Changes in a JIF due to outlier skew could cloud the judgement of budding researchers, who often select journals based on impact factors rather than other merits, such as open access options.
CORD-19 versus COVID-19 via Medium
This week, Alex Wade (Group Technical Program Manager at Meta) has explored how researchers have been harnessing the rich datasets made available from the vast quantity of COVID-19 research. Alex describes the design of the COVID-19 Open Research Dataset (CORD-19), which provides reliable data (free of charge) and thereby allows the machine-learning community to analyse COVID-19 literature. Since its launch, CORD-19 has grown to become an open dataset of more than 240,000 articles on the coronavirus family of diseases.
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