Enhancing Quantitative and Data Science Education for Graduate Students in Biomedical Science

AbstractSubstantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science, but far less attention has been paid to the graduate curriculum. In this setting, we propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities. Due to the diversity of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We developed a novel prioritization approach in which we mined and analyzed quantitative concepts and skills from publications that faculty in relevant units deemed central to the scientific comprehension of their field. The analysis provides a prioritization of quantitative skills and concepts and could represent an effective method to drive curricular focus based upon program-specific faculty input for biological science programs of all types. Our results highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty.One Sentence SummaryWe developed a novel approach to prioritize quantitative concepts and methods for inclusion in a graduate biomedical science curriculum based upon approaches included in faculty-identified key publications.

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NPARS—A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science

Frontiers in Big Data ◽

10.3389/fdata.2021.725095 ◽

2021 ◽

Vol 4 ◽

Author(s):

Li Ma ◽

Erich A. Peterson ◽

Ik Jae Shin ◽

Jason Muesse ◽

Katy Marino ◽

...

Keyword(s):

High Throughput ◽

Quantitative Methods ◽

Data Science ◽

High Throughput Sequencing ◽

Genomic Data ◽

Scientific Data ◽

Genomic Research ◽

Data Sets ◽

Novel Approach ◽

Genomic Studies

Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating matters, in the field of genomic-based science high-throughput sequencing technologies generate considerable amounts of data that needs to be stored, manipulated, and analyzed using a plethora of software tools. Researchers are rarely able to reproduce published genomic studies.Results: Presented is a novel approach which facilitates accuracy and reproducibility for large genomic research data sets. All data needed is loaded into a portable local database, which serves as an interface for well-known software frameworks. These include python-based Jupyter Notebooks and the use of RStudio projects and R markdown. All software is encapsulated using Docker containers and managed by Git, simplifying software configuration management.Conclusion: Accuracy and reproducibility in science is of a paramount importance. For the biomedical sciences, advances in high throughput technologies, molecular biology and quantitative methods are providing unprecedented insights into disease mechanisms. With these insights come the associated challenge of scientific data that is complex and massive in size. This makes collaboration, verification, validation, and reproducibility of findings difficult. To address these challenges the NGS post-pipeline accuracy and reproducibility system (NPARS) was developed. NPARS is a robust software infrastructure and methodology that can encapsulate data, code, and reporting for large genomic studies. This paper demonstrates the successful use of NPARS on large and complex genomic data sets across different computational platforms.

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Data Science Curriculum

The Data Science Framework ◽

10.1007/978-3-030-51023-7_4 ◽

2020 ◽

pp. 75-108

Author(s):

Tomasz Wiktorski ◽

Yuri Demchenko ◽

Juan J. Cuadrado-Gallego

Keyword(s):

Data Science ◽

Science Curriculum

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Contribution to a Mixed Assessment of Patient Safety Culture

10.21203/rs.3.rs-36536/v1 ◽

2020 ◽

Author(s):

Youcef Oussama Fourar ◽

Mebarek Djebabra ◽

Wissal Benhassine ◽

Leila Boubaker

Keyword(s):

Patient Safety ◽

Safety Culture ◽

Quantitative Methods ◽

Principal Component ◽

Patient Safety Culture ◽

International Standards ◽

Systematic Evaluation ◽

Maturity Model ◽

Combined Method ◽

Novel Approach

Abstract Purpose: The evaluation of patient safety culture is conducted using quantitative methods based on the use of questionnaires and qualitative ones focused on the deployment of cultural maturity models. These methods are known to suffer from certain major limits. This article aims to overcome the difficulties encountered by both methods and to propose a novel approach to the assessment of PSC. Methodology: The approach proposed in this article consists of applying a combined method, based on Principal Component Analysis (PCA) and K-means algorithm, to group together PSC dimensions into macro-dimensions whose exploitation allows to overcome the difficulties encountered with dimensional analysis of PSC and then, serve as a basic support for the development of a patient safety culture maturity model. Findings: The results of the combined method PCA / k-means shows that PSC dimensions can be grouped into three macro-dimensions that were capitalized in a first place using factors related to the development of PSC and in a second place to develop a quantitative maturity matrix that helped in the identification of PSC maturity levels.Originality: The merit of our proposal is to work towards a quali-quantitative evaluation of safety culture recommended by a good number of researchers but, to our knowledge, few or no studies are devoted to this hybrid or systematic evaluation of safety culture. Thus, the results can also be projected to implicate PSC actors and to frame the evaluation pf PSC maturity by international standards.

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Introduction to the Special Issue: Applications of Internet of Things

Symmetry ◽

10.3390/sym10090374 ◽

2018 ◽

Vol 10 (9) ◽

pp. 374 ◽

Cited By ~ 1

Author(s):

Chi-Hua Chen ◽

Eyhab Al-Masri ◽

Feng-Jang Hwang ◽

Despo Ktoridou ◽

Kuen-Rong Lo

Keyword(s):

Internet Of Things ◽

Smart Home ◽

Data Science ◽

Cooperative Spectrum Sensing ◽

Smart Homes ◽

Recognition System ◽

Special Issue ◽

Smart Transportation ◽

User Classification ◽

Novel Approach

This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.

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The Nature of Intelligent Analytics

Advances in Data Mining and Database Management - Intelligent Analytics With Advanced Multi-Industry Applications ◽

10.4018/978-1-7998-4963-6.ch001 ◽

2021 ◽

pp. 1-21

Author(s):

Zhaohao Sun ◽

Andrew Stranieri

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Research And Development ◽

Business Intelligence ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Business Analytics ◽

Novel Approach ◽

Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

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Building an integrated enhanced virtual research environment metadata catalogue

The Electronic Library ◽

10.1108/el-09-2018-0183 ◽

2019 ◽

Vol 37 (6) ◽

pp. 929-951 ◽

Cited By ~ 2

Author(s):

Laurent Remy ◽

Dragan Ivanović ◽

Maria Theodoridou ◽

Athina Kritsotaki ◽

Paul Martin ◽

...

Keyword(s):

Data Model ◽

Data Science ◽

Science Research ◽

Dublin Core ◽

Content Type ◽

Knowledge Based ◽

Novel Approach ◽

Virtual Research Environment ◽

Definition Of ◽

Practical Implications

Purpose The purpose of this paper is to boost multidisciplinary research by the building of an integrated catalogue or research assets metadata. Such an integrated catalogue should enable researchers to solve problems or analyse phenomena that require a view across several scientific domains. Design/methodology/approach There are two main approaches for integrating metadata catalogues provided by different e-science research infrastructures (e-RIs): centralised and distributed. The authors decided to implement a central metadata catalogue that describes, provides access to and records actions on the assets of a number of e-RIs participating in the system. The authors chose the CERIF data model for description of assets available via the integrated catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings. Findings An integrated catalogue of research assets metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E and CKAN formats can be integrated into the catalogue. Metadata are stored in CERIF RDF in the integrated catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats. Practical implications The integrated catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications The integrated catalogue should boost the performance of multi-disciplinary research; thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value A novel approach for creation of the integrated catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.

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