Integrating data science into the translational science research spectrum: A substance use disorder case study

Abstract The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.

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Enhancing reproducibility using interprofessional team best practices

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.512 ◽

2020 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Betsy Rolland ◽

Elizabeth S. Burnside ◽

Corrine I. Voils ◽

Manish N. Shah ◽

Allan R. Brasier

Keyword(s):

Best Practices ◽

Science Research ◽

Research Process ◽

Reproducible Research ◽

Scientific Integrity ◽

Team Science ◽

Translational Science ◽

Preclinical Research ◽

Minimal Impact ◽

The Impact

Abstract The pervasive problem of irreproducibility of preclinical research represents a substantial threat to the translation of CTSA-generated health interventions. Key stakeholders in the research process have proposed solutions to this challenge to encourage research practices that improve reproducibility. However, these proposals have had minimal impact, because they either 1. take place too late in the research process, 2. focus exclusively on the products of research instead of the processes of research, and/or 3. fail to take into account the driving incentives in the research enterprise. Because so much clinical and translational science is team-based, CTSA hubs have a unique opportunity to leverage Science of Team Science research to implement and support innovative, evidence-based, team-focused, reproducibility-enhancing activities at a project’s start, and across its evolution. Here, we describe the impact of irreproducibility on clinical and translational science, review its origins, and then describe stakeholders’ efforts to impact reproducibility, and why those efforts may not have the desired effect. Based on team-science best practices and principles of scientific integrity, we then propose ways for Translational Teams to build reproducible behaviors. We end with suggestions for how CTSAs can leverage team-based best practices and identify observable behaviors that indicate a culture of reproducible research.

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Data Science & Engineering into Food Science: A novel Big Data Platform for Low Molecular Weight Gelators’ Behavioral Analysis

Journal of Computer Science and Technology ◽

10.24215/16666038.20.e08 ◽

2020 ◽

Vol 20 (2) ◽

pp. e08

Author(s):

Verónica Cuello ◽

Gonzalo Zarza ◽

Maria Corradini ◽

Michael Rogers

Keyword(s):

Molecular Weight ◽

Big Data ◽

Data Science ◽

Data Entry ◽

Science Research ◽

Food Science ◽

Redundant Data ◽

Data Platform ◽

Assembly Behavior ◽

Low Molecular Weight Gelators

The objective of this article is to introduce a comprehensiveend-to-end solution aimed at enabling the applicationof state-of-the-art Data Science and Analyticmethodologies to a food science related problem. Theproblem refers to the automation of load, homogenization,complex processing and real-time accessibility tolow molecular-weight gelators (LMWGs) data to gaininsights into their assembly behavior, i.e. whether agel can be mixed with an appropriate solvent or not.Most of the work within the field of Colloidal andFood Science in relation to LMWGs have centered onidentifying adequate solvents that can generate stablegels and evaluating how the LMWG characteristics canaffect gelation. As a result, extensive databases havebeen methodically and manually registered, storingresults from different laboratory experiments. Thecomplexity of those databases, and the errors causedby manual data entry, can interfere with the analysisand visualization of relations and patterns, limiting theutility of the experimental work.Due to the above mentioned, we have proposed ascalable and flexible Big Data solution to enable theunification, homogenization and availability of the datathrough the application of tools and methodologies.This approach contributes to optimize data acquisitionduring LMWG research and reduce redundant data processingand analysis, while also enabling researchersto explore a wider range of testing conditions and pushforward the frontier in Food Science research.

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A Microservice-Based Big Data Analysis Platform for Online Educational Applications

Scientific Programming ◽

10.1155/2020/6929750 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Kehua Miao ◽

Jie Li ◽

Wenxing Hong ◽

Mingtao Chen

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Science ◽

Modular Design ◽

Science Research ◽

Big Data Analysis ◽

Research Field ◽

Traditional Work ◽

Educational Applications ◽

Analysis Platform

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.

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Facilitating translational team science: The project leader model

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.398 ◽

2019 ◽

Vol 3 (4) ◽

pp. 140-146

Author(s):

Lynn Sutton ◽

Lisa G. Berdan ◽

Jean Bolte ◽

Robert M. Califf ◽

Geoffrey S. Ginsburg ◽

...

Keyword(s):

Project Management ◽

Critical Role ◽

Academic Research ◽

Science Research ◽

Team Science ◽

Project Leader ◽

Translational Science ◽

Project Leadership ◽

Research Organizations ◽

Academic Settings

AbstractProject management expertise is employed across many professional sectors, including clinical research organizations, to ensure that efforts undertaken by the organization are completed on time and according to specifications and are capable of achieving the needed impact. Increasingly, project leaders (PLs) who possess this expertise are being employed in academic settings to support clinical and preclinical translational research team science. Duke University’s clinical and translational science enterprise has been an early adopter of project management to support clinical and preclinical programs. We review the history and evolution of project management and the PL role at Duke, examine case studies that illustrate their growing value to our academic research environment, and address challenges and solutions to employing project management in academia. Furthermore, we describe the critical role project leadership plays in accelerating and increasing the success of translational team science and team approaches frequently required for systems biology and “big data” scientific studies. Finally, we discuss perspectives from Duke project leadership professionals regarding the training needs and requirements for PLs working in academic clinical and translational science research settings.

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Evaluation of Risk Mitigation Measures for People With Substance Use Disorders to Address the Dual Public Health Crises of COVID-19 and Overdose in British Columbia: A Mixed-Methods Study Protocol.

10.21203/rs.3.rs-125015/v1 ◽

2020 ◽

Author(s):

Bohdan Nosyk ◽

Amanda Slaunwhite ◽

Karen Urbanoski ◽

Natt Hongdilokkul ◽

Heather Palis ◽

...

Keyword(s):

Public Health ◽

British Columbia ◽

Substance Use ◽

Substance Use Disorder ◽

Mixed Method ◽

Risk Mitigation ◽

Population Level ◽

Mixed Method Study ◽

Control Group ◽

The Impact

Abstract BackgroundThe COVID-19 pandemic was preceded by an ongoing overdose crisis and linked to escalating drug overdose deaths in British Columbia (BC). At the outset of these dual public health emergencies, the BC government announced interim Risk Mitigation Guidance (RMG) that permitted prescribing medication alternatives to substances, including opioids, alcohol, stimulants, and benzodiazepines, an intervention sometimes referred to as ‘Safe Supply’. This protocol outlines the approach for a study of the implementation of RMG and its impacts on COVID-19 infection, drug-related and systemic harms, continuity of care for people with substance use disorder, as well as their behavioural, psychosocial, and well-being outcomes.MethodsWe conduct a parallel mixed-method study that involves both analysis of population-level administrative health data and primary data collection, including a 10-week longitudinal observational study (target n=200), a cross-sectional survey (target n=200), and qualitative interviews (target n=60). We have implemented a participatory approach to this evaluation, partnering with people with lived or living experience of substance use, as well as researchers and public health decision-makers across the province. Linked population-level administrative databases will analyze data from a cohort of BC residents with an indication of substance use disorder between 1996 and 2000. We will conduct a high-dimensional propensity score matching and marginal structural modeling to construct a control group and assess the impact of RMG dispensation receipt on a collaboratively-determined set of primary and secondary outcomes.DiscussionThis study constitutes the first formal evaluation of a province-wide program providing regulated pharmaceutical alternatives to the toxic drug supply. The study features an integrated knowledge translation approach, including communications with people with lived/living experience of substance use and consortium meetings with various stakeholders. Supported by the unique research context in BC, our selected mixed method study design will provide an exceptionally strong evidence base to judge not only the impact of the initial implementation of RMG, but also critical evidence on the implementation of the program, which can be used to adapt its future iterations if deemed successful.

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"I can't live like that": The experience of caregiver stress of caring for a relative with substance use disorder

10.21203/rs.3.rs-66075/v2 ◽

2021 ◽

Author(s):

Geoffrey Maina ◽

Marcella Ogenchuk ◽

Taryn Phaneuf ◽

Abukari Kwame

Keyword(s):

Substance Use ◽

Substance Use Disorder ◽

Behavioral Problems ◽

Self Care ◽

Health Impact ◽

Physical And Mental Health ◽

Individual Interviews ◽

Specific Care ◽

The Individual ◽

The Impact

Abstract Background: The impact of addiction extends beyond the individual using a substance. Caring for an individual with addiction creates persistent stressful circumstances that cause worry, anger, depression, shame, guilt, anxiety, and behavioral problems within the family unit.The aim of the study: The paper aims to explore the experiences of caring for a relative with a substance use disorder (SUD) and self-care strategies caregivers employ.Methods: The study adopted an exploratory qualitative design. To be included in the study, participants were required to have a relative with a (SUD) disorder and not be actively using the substance themselves. Individual interviews were conducted to gather their experiences, meanings, and how they made sense of caring for a relative with a SUD.Results: 21 Participants were involved in the study, of which 17 were women, and four were men of which there had a sister, four had a brother, eight had a parent, six had a dependent, and one participant had a grandparent with a SUD. Four themes, whose overarching focus is the pains of living and caring for a family with a SUD, caused the participants and how the participants mitigated these experiences.Conclusion: The stress associated with caring for individuals with a SUD impacts the caregiver's physical and mental health. Specific care modalities targeting caregivers need to be developed to address the health impact and to support self-care.

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ANALYSIS OF FACTORS CAUSING THE PRODUCT PURCHASED BY CONSUMERS BASED ON DATA ON THE SALE OF GOODS IN A MARKET PLACE IN INDONESIA

ABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat ◽

10.32734/abdimastalenta.v3i1.2337 ◽

2018 ◽

Vol 3 (1) ◽

pp. 48-53

Author(s):

Baihaqi Siregar ◽

Erna B Nababan ◽

Opim S Sitompul

Keyword(s):

Information Technology ◽

Big Data ◽

Data Science ◽

Product Information ◽

Economic Value ◽

Science Research ◽

Research Unit ◽

Complex Data ◽

Market Place ◽

The World

Starting from the success of giant web service companies as well as Google and Facebook in managing and utilizing unstructured data in the form of consumer generated media and click stream in a very large volume, a concept known as Big Data then became the center attention in the world of information technology. The fact also shows that more and more organizations in the world, whether private companies or government agencies, have difficulty managing data whose volumes are growing and their types are increasingly complex. They have to organize and analyze these data, and they must find the meaning or value of the ever-expanding and increasingly complex data pile, which is said to have exceeded the capability of conventional data processing applications to process it. The condition of this kind of data is also categorized as Big Data, which is interpreted as a set of data in a very large number of challenges lies in how the data should be stored, how to search in the pile of data, how to distribute it, how to visualize it, and how the data should be analyzed. The long-term goal of IbKIK's proposed program is the establishment of a startup company in the field of analytic data from the world of campus directly. Within the planned three-year period, it is desirable that the company be financially self-sufficient by being a data analytics consultant and also creating a sophisticated and advanced Big Data Analytic application platform product. The advantages possessed when the company started from the academic world is the quantity and quality of human resources as intellectual actors can be selected quickly and accurately. Especially with the synchronization between the curriculum content that is taught with its implementation directly through the program IbKIK become useful products and economic value. From the academic point of view, the desired outcomes are from this program published several journals and proceedings of national and international scale, the publication of textbooks, getting HKI, and publications in the mass media. Also, with the success of this company can produce a derivative company engaged in other areas that are still related as a supporter of the business. The product output of the community service activity that has been done for the first year of the planned three years period is the establishment of a Product Information System Sold on E-Commerce Transactions at Market Place. Also, has been established research unit as the forerunner of the business unit under the auspices of the Faculty of Computer Science and Information Technology University of Sumatera Utara under the name Data Science Research Group.

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NIDA vision for big data science to understand the biological underpinnings of substance use disorders

Neuropsychopharmacology ◽

10.1038/s41386-020-00850-1 ◽

2020 ◽

Vol 46 (1) ◽

pp. 262-262

Author(s):

Susan N. Wright ◽

A. Roger Little

Keyword(s):

Substance Use ◽

Big Data ◽

Substance Use Disorders ◽

Data Science

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“I can’t live like that”: the experience of caregiver stress of caring for a relative with substance use disorder

Substance Abuse Treatment Prevention and Policy ◽

10.1186/s13011-021-00344-3 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Geoffrey Maina ◽

Marcella Ogenchuk ◽

Taryn Phaneuf ◽

Abukari Kwame

Keyword(s):

Substance Use ◽

Substance Use Disorder ◽

Behavioral Problems ◽

Self Care ◽

Health Impact ◽

Physical And Mental Health ◽

Individual Interviews ◽

Specific Care ◽

The Individual ◽

The Impact

Abstract Background The impact of addiction extends beyond the individual using a substance. Caring for an individual with addiction creates persistent stressful circumstances that cause worry, anger, depression, shame, guilt, anxiety, and behavioral problems within the family unit. The aim of the study The paper aims to explore the experiences of caring for a relative with a substance use disorder (SUD) and self-care strategies caregivers employ. Methods The study adopted an exploratory qualitative design. To be included in the study, participants were required to have a relative with a (SUD) disorder and not be actively using the substance themselves. Individual interviews were conducted to gather their experiences, meanings, and how they made sense of caring for a relative with a SUD. Results Twenty one participants were involved in the study, of which 17 were women, and four were men of which there had a sister, four had a brother, eight had a parent, six had a dependent, and one participant had a grandparent with a SUD. Four themes, whose overarching focus is the pains of living and caring for a family with a SUD, caused the participants and how the participants mitigated these experiences Conclusion The stress associated with caring for individuals with a SUD impacts the caregiver’s physical and mental health. Specific care modalities targeting caregivers need to be developed to address the health impact and to support self-care.

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