The URI Big Data Initiative and HPC Core Facility (What is it, and what\u27s in it for you?)

The Big Data Collaborative at URI was formed in 2012 by three dozen scholar educators to compete for a cluster hire. The hire was awarded by the provost’s office in February 2015. Searches are now underway for eight tenure-track faculty members across five colleges, including Engineering. To support these hires and the work of others already at URI a HPC (High Performance Computing) sub-group has proposed a HPC and Research Computing Core Facility. The goal is to serve many partners and to achieve savings in time and money through centralization of hardware, software and human resources. Also related to the Big Data Initiative is an interdisciplinary committee that will soon propose a Data Science major for undergraduates. In the future the collaborative will propose graduate tracks through existing graduate programs and a research Institute for Data-Intensive Discovery – ID2. In support of the interdisciplinary engagement that the Big Data initiative is expected to foster, the Northeast EPSCoR institutions have secured EAGER funding to formulate no-boundary thinking in research and education. This talk will describe these inter-related initiatives and projects, explain how researchers and students at URI might benefit from the presence of the Big Data initiative, and connect these efforts to national and local movements in research funding, general education, and new developments in K-20 education and workforce development

NBT (No-Boundary Thinking): Needed To Attend To Ethical Implications Of Data And AI

In this era of Big Data and AI, expertise in multiple aspects of data, computing, and the domains of application is needed. This calls for teams of experts with different training and perspectives. Because data analysis can have serious ethical implications, it is important that these teams are well and deeply integrated. No-Boundary Thinking (NBT) teams can provide support for team formation and maintenance, thereby attending to the many dimensions of the ethics of data and analysis. In this NBT workshop session, we discuss the ethical concerns that arise from the use of data and AI, and the implications for team building; and provide and brainstorm suggestions for ethical data enabled science and AI

What Information Technology Asks of Business Higher Education Institutions: The Case of Rhode Island

Using data from Rhode Island, this research examines the relationships among certifications, skills, educational levels and the level of compensation across the IT field and attempts to discriminate between ten major job functions. It assesses the significance of skills and certifications to specific IT worker categories. The level of education was found to predict success. Certification and skills vary across job functions, and did not necessarily differentiate across IT job functions. Suggestions on the development of curricula are proposed

Team Building Without Boundaries

Team building can be challenging when participants are from the same discipline or sub-discipline, but needs special attention when participants use a different vocabulary and have different cultural views on what constitutes viable problems and solutions. Essential to No Boundary Thinking (NBT) teams is proper formulation of the problem to be solved, and a basic tenant is that the NBT team must come together with diverse perspectives to decide the problem before solutions can be considered. Given that participants come with different views on problem formulation and solution, it is important to consider a robust process for team formation and maintenance. This takes extra effort and time, but scholars studying teams of experts with diverse training have found that they are better positioned to be successful in solving even deep and difficult problems especially if they have learned to work well with each other. At this workshop we will discuss principles that scholars who have worked in NBT teams have discovered as effective. We will then engage with the workshop participants to consider discuss these principles and brainstorm to consider other approaches

CONSTRAINT BASED ANALYSIS OF DATABASE UPDATE PROPAGATION

Semantic and object-oriented data models provide convenient constructs for the specification of objects, relationships, and operations. The vehicle of representation is a collection of abstractions which parallel the means by which humans prefer to organize complex enterprises. These constructs inherently permit focus on one object, relationship, or operation at a time. Propagation, as a semantic construct, provides the extension of existing modeling capabilities by providing a mechanism for the specification of the update semantics between database objects. Through the analysis of constraints and the propagated actions necessary to maintain them, we attempt to do the following: 1) incorporate additional semantics into the database schema in the form of database propagation rules, 2) in the context of constraints and propagation rules, provide a model independent paradigm for determining if schemata are correct, and 3) provide a vehicle fur the explicit specification of update actions during database schema design

No-Boundary Thinking in Bioinformatics Research

Currently there are definitions from many agencies and research societies defining bioinformatics as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification