Open science progress: A literature assessment of open access articles

The open science movement has been widely adopted in multiple scientific fields across nations. Its benefit has been proven in many cases, most notably when the practice accelerated the search for solutions to the Covid-19 pandemic both in medical and socio-economic contexts. Still, the movement has faced multiple challenges, including an imbalance in the adoption of its numerous aspects. For example, the open access aspect which indicates the starting point of the movement has been widely practiced. Unfortunately, while open access is essential, an open access practice alone is not enough to pursue open science. In this work, we would like to assess the imbalance of the adoption, especially to measure how open access practice contributes to other practices, namely open data and open source as a sub-aspect of the open reproducibility research. Our assessment is based on descriptive statistic analysis of 300 open access articles from three domains, that is engineering, social and life science. Our findings indicated that the free and open source computer codes were dominantly adopted by the three scientific fields. However, social science has the lowest involvement in public data. © 2022 ACM.

RoCoFake – A Romanian Covid-19 Fake News Dataset

The Covid-19 pandemic, managed to shed light onto a neglected problem – that of fake news. Even though lockdowns were imposed in most parts of the world, collaboration between researchers across the globe wasn't impeded. Moreover, the lockdown has deprived people of face-to-face interactions and so they shifted towards online communication. This translated into a massive chatting data, which part was true, but fake information also had its share. Therefore, it is of great interest to develop a dataset to try to spot the fake information. RoCoFake comes to address the lack of resources in this domain, by providing a Romanian Covid-19 Fake News dataset, by aggregating various resources available online, like tweets, news titles and fact-checking news sites like factual.ro. This data provides researchers from the medical domain particularly, but not only, with a valuable, open-access data source useful for various research projects. A benchmark for fake news detection is also provided, so that future investigations can compare against our research. Results suggest that even though the dataset is relatively large, improvements can be made by incorporating retweets and comments. © 2022 IEEE.

RoCoFake – A Romanian Covid-19 Fake News Dataset

The Covid-19 pandemic, managed to shed light onto a neglected problem – that of fake news. Even though lockdowns were imposed in most parts of the world, collaboration between researchers across the globe wasn't impeded. Moreover, the lockdown has deprived people of face-to-face interactions and so they shifted towards online communication. This translated into a massive chatting data, which part was true, but fake information also had its share. Therefore, it is of great interest to develop a dataset to try to spot the fake information. RoCoFake comes to address the lack of resources in this domain, by providing a Romanian Covid-19 Fake News dataset, by aggregating various resources available online, like tweets, news titles and fact-checking news sites like factual.ro. This data provides researchers from the medical domain particularly, but not only, with a valuable, open-access data source useful for various research projects. A benchmark for fake news detection is also provided, so that future investigations can compare against our research. Results suggest that even though the dataset is relatively large, improvements can be made by incorporating retweets and comments. © 2022 IEEE.

The role of the information community in ensuring that information is authoritative: Strategies from NISO Plus 2022

This article reports on a NISO Plus 2022 session that addressed what can be done to safeguard the integrity of the scholarly content being created, disseminated, and used. How much responsibility does the information community have in ensuring that the content we provide is authoritative? Preprints are a great way to make early research results available, but it is not always clear that those results are not yet thoroughly vetted. Peer review - a key element of scholarly publication - can help, but is far from foolproof. Retractions are another important tool, but most retracted research is still all too readily available. What can and should we be doing to safeguard the integrity of the content being created, disseminated, and used? © 2022 - The authors. Published by IOS Press.

Knowledge Construction in Engineering Education Research - Assessing the Role of Journals, Books, Conferences, and Other Products of Research

This panel will discuss the role of different knowledge artifacts in creating, maintaining, and circulating knowledge within the engineering education community. The past decade has seen a significant increase in the venues available for sharing engineering education research and as the field grows and builds more knowledge, it is equally important to also take stock of prior work and of strategies to create novelty. Within this context, what is the role of different knowledge encapsulating artifacts and why do those who engage with creating these artifacts do so? In this panel we touch upon these issues while taking stock of the knowledge base in the field. We will also discuss what the future of knowledge creation in the field might look like given the move towards open access online publications as the primary form of knowledge circulation. Finally, in the post-COVID context, what will and should be the role of in-person events in this process. In terms of equity of participation, what potential avenues are available?. © 2022 IEEE.

Building Bridges into Engineering and Computer Science: Outcomes, Impacts and Lessons Learned

Wright College, an urban open-access community college, independently accredited within a larger community college system, is a federally recognized Hispanic-Serving Institution (HSI) with the largest community college enrollment of Hispanic students in its state. In 2018, Wright College received an inaugural National Science Foundation-Hispanic Serving Institution (NSF:HSI) research project grant “Building Capacity: Building Bridges into Engineering and Computer Science”. The project's overall goals are to increase underrepresented students pursuing an associate degree (AES) in engineering and computer science and streamline two transitions: high school to community college and 2-year to 4-year institutions. Through the grant, Wright College created a holistic and programmatic framework that examines and correlates engineering students' self-efficacy (the belief that students will succeed as engineers) and a sense of belonging with student success. The project focuses on Near-STEM ready students (students who need up to four semesters of math remediation before moving into Calculus 1). The project assesses qualitative and quantitative outcomes through surveys and case study interviews supplemented with retention, persistence, transfer, associate and bachelor's degree completion rates, and time for degree completion. The key research approach is to correlate student success data with self-efficacy and belonging measures. Outcomes and Impacts Three years into the project, Wright College Engineering and Computer Science Program was able to: • Develop and implement the Contextualized Summer Bridge with a total of 132 Near-STEM participants. One hundred twenty-seven (127) completed;100% who completed the Bridge eliminated up to two years of math remediation, and 54% were directly placed in Calculus 1. All successful participants were placed in different engineering pathways, and 11 students completed Associate in Engineering Science (AES) and transferred after two years from the Bridge. • Increase enrollment by 940% (25 to 235 students) • Retain 93% of first-year students (Fall to fall retention). Seventy-five percent (75%) transferred after two years from initial enrollment. • Develop a holistic and programmatic approach for transfer model, thus increasing partnerships with 4-year transfer institutions resulting in the expansion of guaranteed/dual admissions programs with scholarships, paid research experience, dual advising, and students transferring as juniors. • Increase diversity at Wright College by bridging the academic gap for Near-STEM ready students. • Increase self-efficacy and belonging among all Program participants. • Increase institutionalized collaborations responsible for Wright College's new designation as the Center of Excellence for Engineering and Computer Science. • Increase enrollment, retention, and transfer of Hispanic students instrumental for Wright College Seal of Excelencia recognition. Lessons Learned The framework established during the first year of the grant overwhelmingly increased belonging and self-efficacy correlated with robust outcomes. However, the COVID-19 pandemic provided new challenges and opportunities in the second and third years of the grant. While adaptations were made to compensate for the negative impact of the pandemic, the face-to-face interactions were critical to support students' entry into pathways and persistence within the Program. © American Society for Engineering Education, 2022.

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome play a key role in controlling the pandemic. Recent studies have highlighted the role of point-of-care ultrasound imaging for COVID-19 screening and prognosis, particularly given that it is non-invasive, globally available, and easy-to-sanitize. COVIDx-US Dataset: Motivated by these attributes and the promise of artificial intelligence tools to aid clinicians, we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related ultrasound imaging data. The COVIDx-US dataset was curated from multiple data sources and its current version, i.e., v1.5., consists of 173 ultrasound videos and 21,570 processed images across 147 patients with COVID-19 infection, non-COVID-19 infection, other lung diseases/conditions, as well as normal control cases. CONCLUSIONS: The COVIDx-US dataset was released as part of a large open-source initiative, the COVID-Net initiative, and will be continuously growing, as more data sources become available. To the best of the authors' knowledge, COVIDx-US is the first and largest open-access fully-curated benchmark lung ultrasound imaging dataset that contains a standardized and unified lung ultrasound score per video file, providing better interpretation while enabling other research avenues such as severity assessment. In addition, the dataset is reproducible, easy-to-use, and easy-to-scale thanks to the well-documented modular design.

Customization and testing of a mobile reader app for an open-access SARS-CoV-2 antigen lateral flow assay

Lateral flow assays (LFA’s) are a common diagnostic test form, particularly in low-to-middle income countries (LMIC’s). Visual interpretation of LFA’s can be subjective and inconsistent, especially with faint positive results, and commercial readers are expensive and challenging to implement in LMIC’s. We report a phone-agnostic Android app to acquire images and interpret results of a variety of LFA’s with no additional hardware. Starting from the open-source “rdt-scan” codebase, we integrated new features and revamped the peak detection method. This included improved perspective corrections, phone level check to eliminate shadows, high resolution still-image capture besides existing video frame capture, and new peak detection method. This peak detection incorporated smoothing and baseline removal from the one-dimensional profiles of a given color channel’s intensity averaged across the read window’s width, with location and relative size constraints to correctly report locations and peak heights of control and test lines. The app was tested in a real-world setting in conjunction with an open-access LFA for SARS-CoV-2 antigen developed by GH Labs. The app acquired 155 images of LFA cassettes, and results were compared against both visual interpretation by trained clinical staff and PCR results from the same patients. With an appropriate setting for test line intensity threshold, the app matched visual read for all cases but one missed visual positive. From ROC analyses against PCR, the app outperformed visual read by 1-3% across sensitivity, specificity, and AUC. The app thus demonstrated promise for accurate, consistent interpretation of LFA’s while generating digital records that could also be useful for health surveillance. © 2022 SPIE

Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm

The COVID-19 related shutdowns have made significant impacts on the electric grid operation worldwide. The global electrical demand plummeted around the planet in 2020 continuing into 2021. Moreover, demand shape has been profoundly altered as a result of industry shutdowns, business closures, and people working from home. In view of such massive electric demand changes, energy forecasting systems struggle to provide an accurate demand prediction, exposing operators to technical and financial risks, and further reinforcing the adverse economic impacts of the pandemic. In this context, the “IEEE DataPort Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm" was organized to support the development and dissemination state-of-the-art load forecasting techniques that can mitigate the adverse impact of pandemic-related demand uncertainties. This paper presents the findings of this competition from the technical and organizational perspectives. The competition structure and participation statistics are provided, and the winning methods are summarized. Furthermore, the competition dataset and problem formulation is discussed in detail. Finally, the dataset is published along with this paper for reproducibility and further research. Author

Interrogating COVID-19 vaccine intent in the Philippines with a nationwide open-access online survey.

To mitigate the unprecedented health, social, and economic damage of COVID-19, the Philippines is undertaking a nationwide vaccination program to mitigate the effects of the global pandemic. In this study, we interrogated COVID-19 vaccine intent in the country by deploying a nationwide open-access online survey, two months before the rollout of the national vaccination program. The Health Belief Model (HBM) posits that people are likely to adopt disease prevention behaviors and to accept medical interventions like vaccines if there is sufficient motivation and cues to action. A majority of our 7,193 respondents (62.5%) indicated that they were willing to be vaccinated against COVID-19. Moreover, multivariable analysis revealed that HBM constructs were associated with vaccination intention in the Philippines. Perceptions of high susceptibility, high severity, and significant benefits were all good predictors for vaccination intent. We also found that external cues to action were important. Large majorities of our respondents would only receive the COVID-19 vaccines after many others had received it (72.8%) or after politicians had received it (68.2%). Finally, our study revealed that most (21%) were willing to pay an amount of PHP 1,000 (USD20) for the COVID-19 vaccines with an average willing-to-pay amount of PHP1,892 (USD38).

Load-Altering Attacks Against Power Grids under COVID-19 Low-Inertia Conditions

The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant decrease in energy demands across the globe. Recent studies have shown that the low demand conditions caused by COVID-19 lockdowns combined with large renewable generation have resulted in extremely low-inertia grid conditions. In this work, we examine how an attacker could exploit these scenarios to cause unsafe grid operating conditions by executing load-altering attacks (LAAs) targeted at compromising hundreds of thousands of IoT-connected high-wattage loads in low-inertia power systems. Our study focuses on analyzing the impact of the COVID-19 mitigation measures on U.S. regional transmission operators (RTOs), formulating a plausible and realistic least-effort LAA targeted at transmission systems with low-inertia conditions, and evaluating the probability of these large-scale LAAs. Theoretical and simulation results are presented based on the WSCC 9-bus and IEEE 118-bus test systems. Results demonstrate how adversaries could provoke major frequency disturbances by targeting vulnerable load buses in low-inertia systems and offer insights into how the temporal fluctuations of renewable energy sources, considering generation scheduling, impact the grid’s vulnerability to LAAs. Author

Addressing disorder in scholarly communication: Strategies from NISO plus 2021

Open science and preprints have invited a larger audience of readers, especially during the pandemic. Consequently, communicating the limitations and uncertainties of research to a broader public has become important over the entire information lifecycle. This paper brings together reports from the NISO Plus 2021 conference session “Misinformation and truth: from fake news to retractions to preprints”. We discuss the validation and verification of scientific information at the preprint stage in order to support sound and open science standards, at the publication stage in order to limit the spread of retracted research, and after publication, to fight fake news about health-related research by mining open access content. © 2021 - The authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).

Opportunities and Challenges in Democratizing Immunology Datasets.

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.

The Online Cross-Country Tour: Evaluation of a Novel Online Series for Canadian Radiology Residency Programs and Future Implications for the Radiology CaRMS Process.

PURPOSE: The COVID-19 pandemic indefinitely cancelled visiting medical student radiology electives across Canada. In response, the Canadian Association of Radiologists Resident & Fellow Section (CAR RFS) and Medical Student Network (MSN) developed and evaluated an online series for medical students to learn about Canadian radiology residency programs. METHODS: Medical students from any year of training were recruited through the MSN, local radiology interest groups, and social media to attend a 2-week online series of interactive sessions via Zoom with program representatives from Canadian radiology residency programs. A survey evaluating the online series, in particular its impact on residency and career planning, was administered to program representatives and students. RESULTS: Fifteen of Canada's 16 radiology residency programs participated in the online series. A total of 212 students attended at least one session and nearly half were participating in the Canadian Resident Matching Service (CaRMS) this year. The postsurvey revealed that 77% of students agreed that the online series helped prepare them for CaRMS and ranking programs. The online series also benefited pre-CaRMS students as significantly more students were considering radiology as a specialty on the postsurvey compared to the presurvey. Students and program representatives agreed that this series should be held in future years, regardless of whether health and travel restrictions are lifted. CONCLUSION: The CAR RFS and MSN hosted an online series for medical students to learn about radiology residency programs outside their home institution. Feedback was highly positive with important implications for the future CaRMS iterations for any specialty.

Selecting Journal for Publication in the Era of "Haste Predatory Journals and COVID-19".

How to cite this article: Siddiqui S, Ahmed A, Azim A. Selecting Journal for Publication in the Era of "Haste Predatory Journals and COVID-19". Indian J Crit Care Med 2020;24(12):1284-1285.

Global academic response to COVID-19: Cross-sectional study.

This study explores the response to COVID-19 from investigators, editors, and publishers and seeks to define challenges during the early stages of the pandemic. A cross-sectional bibliometric review of COVID-19 literature was undertaken between 1 November 2019 and 24 March 2020, along with a comparative review of Middle East respiratory syndrome (MERS) literature. Investigator responsiveness was assessed by measuring the volume and type of research published. Editorial responsiveness was assessed by measuring the submission-to-acceptance time and availability of original data. Publisher-responsiveness was assessed by measuring the acceptance-to-publication time and the provision of open access. Three hundred and ninety-eight of 2,835 COVID-19 and 55 of 1,513 MERS search results were eligible. Most COVID-19 studies were clinical reports (n = 242; 60.8%). The submission-to-acceptance [median: 5 days (IQR: 3-11) versus 71.5 days (38-106); P < .001] and acceptance-to-publication [median: 5 days (IQR: 2-8) versus 22.5 days (4-48·5-; P < .001] times were strikingly shorter for COVID-19. Almost all COVID-19 (n = 396; 99.5%) and MERS (n = 55; 100%) studies were open-access. Data sharing was infrequent, with original data available for 104 (26.1%) COVID-19 and 10 (18.2%) MERS studies (P = .203). The early academic response was characterized by investigators aiming to define the disease. Studies were made rapidly and openly available. Only one-in-four were published alongside original data, which is a key target for improvement. Key points: COVID-19 publications show rapid response from investigators, specifically aiming to define the disease.Median time between submission and acceptance of COVID-19 articles is 5 days demonstrating rapid decision-making compared with the median of 71.5 days for MERS articles.Median time from acceptance to publication of COVID-19 articles is 5 days, confirming the ability to introduce rapid increases at times of crisis, such as during the SARS outbreak.The majority of both COVID-19 and MERS articles are available open-access.