Applying Translational Principles to Data Science Curriculum Development

This paper reports on a curriculum mapping study that examined job descriptions and advertisements for three data curation focused positions: Data Librarian, Data Steward / Curator, and Data Archivist. We present a transferable methodological approach for curriculum development and the findings from our evaluation of employer requirements for these positions. This paper presents " model pathways " for these data curation roles and reflects on opportunities for iSchools to adopt translational data science principles to frame and extend their curriculum to prepare their students for data-driven career opportunities

Education for Real-World Data Science Roles (Part 2): A Translational Approach to Curriculum Development

This study reports on the findings from Part 2 of a small-scale analysis of requirements for real-world data science positions and examines three further data science roles: data analyst, data engineer and data journalist. The study examines recent job descriptions and maps their requirements to the current curriculum within the graduate MLIS and Information Science and Technology Masters Programs in the School of Information Sciences (iSchool) at the University of Pittsburgh. From this mapping exercise, model ‘course pathways’ and module ‘stepping stones’ have been identified, as well as course topic gaps and opportunities for collaboration with other Schools. Competency in four specific tools or technologies was required by all three roles (Microsoft Excel, R, Python and SQL), as well as collaborative skills (with both teams of colleagues and with clients). The ability to connect the educational curriculum with real-world positions is viewed as further validation of the translational approach being developed as a foundational principle of the current MLIS curriculum review process

Learning by Teaching about RDM: An Active Learning Model for Internal Library Education

This paper reports on the design, delivery and assessment of a model for internal library education around research data management (RDM). Conducted at the University of Pittsburgh Library System (ULS), the exercise and resultant instructional session employed an active learning approach, in which a group of librarians and archivists explored data issues and conventions in a discipline of their own selection and presented their findings to an audience of library colleagues. In this paper, we put forth an adaptable active learning model for internal RDM education and offer guidance for its implementation by peer libraries that are similarly building internal capacity for the design and delivery of RDM services that are responsive to disciplinary needs.

Research Transparency: A Preliminary Study of Disciplinary Conceptualisation, Drivers, Tools and Support Services

This paper describes a preliminary study of research transparency, which draws on the findings from four focus group sessions with faculty in chemistry, law, urban and social studies, and civil and environmental engineering. The multi-faceted nature of transparency is highlighted by the broad ways in which the faculty conceptualised the concept (data sharing, ethics, replicability) and the vocabulary they used with common core terms identified (data, methods, full disclosure). The associated concepts of reproducibility and trust are noted. The research lifecycle stages are used as a foundation to identify the action verbs and software tools associated with transparency. A range of transparency drivers and motivations are listed. The role of libraries and data scientists is discussed in the context of the provision of transparency services for researchers

Learning by Teaching about RDM: An Active Learning Model for Internal Library Education

This conference presentation reported on the design, delivery, and assessment of a model for internal library education around research data management (RDM). Conducted at the University of Pittsburgh Library System (ULS), the exercise and resultant instructional session employed an active learning approach, in which a group of librarians and archivists explored data issues and conventions in a discipline of their own selection and presented their findings to an audience of library colleagues. In this presentation, we put forth an adaptable active learning model for internal RDM education and offer guidance for its implementation by peer libraries that are similarly building internal capacity for the design and delivery of RDM services that are responsive to disciplinary needs

Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance

Purpose: Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. Methods: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. Results: Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. Conclusion: Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.This research was funded by National Institute for Health Research (NIHR) and the NewLife Foundation. The Baralle lab is supported by NIHR Research Professorship to D.B. (RP-2016-07-011).published version, accepted version (6 month embargo), submitted versio

Probiotics, prebiotics and immunomodulation of gut mucosal defences: homeostasis and immunopathology.

Probiotics are beneficial microbes that confer a realistic health benefit on the host, which in combination with prebiotics, (indigestible dietary fibre/carbohydrate), also confer a health benefit on the host via products resulting from anaerobic fermentation. There is a growing body of evidence documenting the immune-modulatory ability of probiotic bacteria, it is therefore reasonable to suggest that this is potentiated via a combination of prebiotics and probiotics as a symbiotic mix. The need for probiotic formulations has been appreciated for the health benefits in "topping up your good bacteria" or indeed in an attempt to normalise the dysbiotic microbiota associated with immunopathology. This review will focus on the immunomodulatory role of probiotics and prebiotics on the cells, molecules and immune responses in the gut mucosae, from epithelial barrier to priming of adaptive responses by antigen presenting cells: immune fate decision-tolerance or activation? Modulation of normal homeostatic mechanisms, coupled with findings from probiotic and prebiotic delivery in pathological studies, will highlight the role for these xenobiotics in dysbiosis associated with immunopathology in the context of inflammatory bowel disease, colorectal cancer and hypersensitivity