Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29–2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48–3.78; P < 0.001), delta (OR, 3.03; CI, 2.94–3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90–3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35–9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

Abstract Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Materials and Methods The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Results Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Conclusions The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

People from a diverse array of backgrounds play important roles in research, often in ways that cannot be quantified through traditional metrics of scholarly impact. This demands better approaches to evaluate and ultimately communicate scholarly outcomes beyond the narrow criteria of publications and grants. It is imperative to develop a structure to track a much wider diversity of contributor roles and research objects and do so in a manner that is easily populated with real data. This presentation will share details about the project, our team and approach, upcoming opportunities to collaborate, metrics for success, and integration and application of this work to date.

As Renaissance conceptions of otherness have become a locus of critical study, no work has been more central for making the case about dominant ideologies of race than Shakespeare's Othello. Ania Loomba has suggested that "more than any other play of the time, Othello allows us to see that skin colour, religion, and location were often contradictorily yoked together within ideologies of 'race,' and that all these attributes were animated by notions of sexual and gender difference." While some critics would moderate that view—pointing more or less toward religion, or gender, or geography—few would disagree about the basic ideology of race that Loomba identifies in the play and the early modern period. Daniel J. Vitkus, for example, focuses more on religious conversion than race, but still argues that "cultural anxieties about 'turning Turk'" were part of "the fear of a black planet that gripped Europeans in the early modern era as they faced the expansion of Ottoman power." Many others have agreed that the play "repeats and reinforces the already established perception of Turks as the 'religious threat' and racial 'other.'" A few, like Eric Griffin, have brought the focus on religion closer to home, reading the play as an expression of English nationalism during a time of tension with Catholic Spain, when anti-Catholic, anti-Spanish expression was its own form of othering "ethnocentric discourse." But even this broadened critical discussion is based on examination of a limited number of the texts published in early modern England, and it tells only a small part of the story of race in Othello. Digital approaches, however, open up a vast new body of texts for analysis, shifting the ground on which such criticism can take place.

Research profiling systems provide programmatic support for discovery and use of research and scholarly information regarding people and resourcesessentially serving as special

OpenVIVO is a free and open hosted semantic web platform open to anyone that gathers and shares open data about scholarship around the world. OpenVIVO, based on the VIVO open source platform, provides transparent access to data about the scholarly work of its participants. OpenVIVO demonstrates the use of persistent identifiers, automatic real-time ingest of scholarly ecosystem metadata, use of VIVO-ISF and related ontologies, attribution of work, and publication and reuse of data – all critical components of presenting, preserving, and tracking scholarship. The system was created by a cross-institutional team over the course of three months. The team created and used RDF models for research organizations in the world based on Digital Science GRID data, for academic journals based on data from CrossRef and the US National Library of Medicine, and created a new model for attribution of scholarly work. All models, data, and software are available in open repositories.

The past 2 decades have witnessed the emergence of information as a scientific discipline and the growth of information schools around the world. We analyzed the current state of the iSchool community in the U.S. with a special focus on the evolution of the community. We conducted our study from the perspectives of acquiring talents and producing research, including the analysis on iSchool faculty members' educational backgrounds, research topics, and the hiring network among iSchools. Applying text mining techniques and social network analysis to data from various sources, our research revealed how the iSchool community gradually built its own identity over time, including the growing number of faculty members who received their doctorates from the field that studies information, the deviation from computer science and library science, the rising emphasis on the intersection of information, technology, and people, and the increasing educational and research homogeneity as a community. These findings suggest that iSchools in the U.S. are evolving into a mature and independent discipline with a more established identity.

The past 2 decades have witnessed the emergence of information as a scientific discipline and the growth of information schools around the world. We analyzed the current state of the iSchool community in the U.S. with a special focus on the evolution of the community. We conducted our study from the perspectives of acquiring talents and producing research, including the analysis on iSchool faculty members' educational backgrounds, research topics, and the hiring network among iSchools. Applying text mining techniques and social network analysis to data from various sources, our research revealed how the iSchool community gradually built its own identity over time, including the growing number of faculty members who received their doctorates from the field that studies information, the deviation from computer science and library science, the rising emphasis on the intersection of information, technology, and people, and the increasing educational and research homogeneity as a community. These findings suggest that iSchools in the U.S. are evolving into a mature and independent discipline with a more established identity.

This presentation was given as part of the OpenRIF workshop on April 17, 2016 at the FORCE2016 conference. It provides background on the SciENcv system, the goals of integrating it with OpenRIF, and some details on how the integration is being accomplished.

Although closely related, multidisciplinarity and interdisciplinarity are different. The former indicates the co-existence of multiple disciplines while the latter is more about the integration among various areas. As collaboration between researchers from different areas is one of the major approaches for interdisciplinarity, this research investigated whether higher levels of multidisciplinarity in academic institutions are related to more collaborations, especially more interdisciplinary collaborations, among its faculty members. Using U.S. iSchools as a case study, we applied social network analysis and text mining techniques to faculty members' educational background and publication data, and proposed metrics for multidisciplinarity and collaboration interdisciplinarity. Our analysis results revealed that the multidisciplinarity of an iSchool is actually negatively correlated with the frequency and interdisciplinarity of research collaborations among its faculty members. This finding suggests that having a multidisciplinary environment alone is not sufficient to promote collaborations, nor interdisciplinary collaborations.

This paper examines the effects of gender differences in collaboration on research outcomes. We analyzed network characteristics of seventeen medical research institutions that are Clinical and Translational Science Awardees (CTSA) to determine if network connectivity characteristics have the potential to help mitigate the performance gap between the sexes. We determined betweenness centrality to identify well-connected researchers. Then we used clustering coefficient to determine how tightly connected their collaborators were with each other. We correlate these scores with productivity (number of total publications for each author), and h-index (the number of papers h for which an author has h citations). We also provide data on how network characteristics vary by role for each gender studied. Our results indicate that being well connected is more highly correlated with success for women than men for most of the institutions we studied. We believe these results can be leveraged to improve success rates for women in the future

SciENcv [1], the US Federal Science Experts Network Curriculum Vitae, is an online system for simplifying the creation of researcher biographical sketches or biosketches, which are required when applying for federal funding. SciENcv profiles are curated and controlled by researchers themselves they own the data, they control what data are public, and they edit and maintain the information contained within the profiles, which includes expertise, employment history, educational background, and professional accomplishments. The system leverages data from myNCBI and eRA Commons, and includes links to ORCiD [2] researcher identifiers. The structure of SciENcv biosketches is defined by an XML Schema Definition and profiles can be downloaded in XML format. The system aims to eliminate the need for researchers to repeatedly enter biosketch information and reduce the administrative burden associated with federal grant submission and reporting requirements, as well as creating a repository of researcher profile data where researchers can describe their scientific contributions in their own language.

This paper presents collaboration trends for five Clinical and Translational Science Awardee (CTSA) institution to demonstrate the need for new evaluation metrics. Translational science, a methodology that bridges gaps between fundamental and applied science, has gained attention from both the medical research community and government funding agencies. To facilitate interdisciplinary research it is important to understand what aspects of the process act as a bottleneck and limit its effectiveness. Cultural norms within the scientific community make communication between disciplines difficult [1]. CTSAs are meant to help ease burdens and foster an environment where clinicians and basic scientists work together [2]. In 2013, NCATS director, Dr. Chris Austin, said the CTSAs operated "without particular encouragement or direction from the NIH, and this in a disjointed and uncoordinated fashion" [3]. Our intention is to highlight areas of concern and to demonstrate the need and potential for bibliometric translational indicators to help alleviate some of the difficulties with CTSA evaluation by determining the importance of basic scientists in research networks, discussing patterns of co-authorship by institution, and presenting two translational indicators, which can help institutions and funding agencies compare CTSA collaborative patterns.

Fostering collaborations across multiple disciplines within and across institutional boundaries is becoming increasingly important with the growing emphasis on translational research. As a result, Research Networking Systems that facilitate discovery of potential collaborators have received significant attention by institutions aiming to augment their research infrastructure. We have conducted a survey to assess the state of adoption of these new tools at the Clinical and Translational Science Award (CTSA) funded institutions. Survey results demonstrate that most CTSA funded institutions have either already adopted or were planning to adopt one of several available research networking systems. Moreover a good number of these institutions have exposed or plan to expose the data on research expertise using linked open data, an established approach to semantic web services. Preliminary exploration of these publically-available data shows promising utility in assessing cross-institutional collaborations. Further adoption of these technologies and analysis of the data are needed, however, before their impact on cross-institutional collaboration in research can be appreciated and measured.

Article retraction in research is rising, yet retracted articles continue to be cited at a disturbing rate. This paper presents an analysis of recent retraction patterns, with a unique emphasis on the role author self-cites play, to assist the scientific community in creating counter-strategies. This was accomplished by examining the following: (1) A categorization of retracted articles more complete than previously published work. (2) The relationship between citation counts and after-retraction self-cites from the authors of the work, and the distribution of self-cites across our retraction categories. (3) The distribution of retractions written by both the author and the editor across our retraction categories. (4) The trends for seven of our nine defined retraction categories over a 6-year period. (5) The average journal impact factor by category, and the relationship between impact factor, author self-cites, and overall citations. Our findings indicate new reasons for retractions have emerged in recent years, and more editors are penning retractions. The rates of increase for retraction varies by category, and there is statistically significant difference of average impact factor between many categories. 18 % of authors self-cite retracted work post retraction with only 10 % of those authors also citing the retraction notice. Further, there is a positive correlation between self-cites and after retraction citations.