Best Practices in Accelerating the Data Science Process in Python

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

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A Review of AI and Data Science Support for Cancer Management

10.1101/2020.08.07.20170191 ◽

2020 ◽

Author(s):

E. Parimbelli ◽

S. Wilk ◽

R. Cornet ◽

P. Sniatala ◽

K. Sniatala ◽

...

Keyword(s):

Quality Of Life ◽

Decision Support ◽

Best Practices ◽

Cancer Patients ◽

Predictive Modeling ◽

Data Science ◽

Cancer Management ◽

Research Challenges ◽

Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

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Best Practices in Structuring Data Science Projects

Advances in Intelligent Systems and Computing - Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 ◽

10.1007/978-3-319-99993-7_31 ◽

2018 ◽

pp. 348-357

Author(s):

Jedrzej Rybicki

Keyword(s):

Best Practices ◽

Data Science ◽

Science Projects

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The sharing economy: a hedge against recession setbacks

Journal of Business Strategy ◽

10.1108/jbs-09-2020-0210 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mohammad Reza Habibi ◽

Chiranjeev S. Kohli

Keyword(s):

Best Practices ◽

Design Methodology ◽

Data Science ◽

Science And Technology ◽

Sharing Economy ◽

Economic Sector ◽

Content Type ◽

Key Characteristics ◽

The Future ◽

Conceptual Paper

Purpose This paper aims to provide lessons from the emergence of the sharing economy after the 2008 recession and helps managers prepare more effectively for recessions in the future. Design/methodology/approach In this conceptual paper, the authors build on research on the sharing economy and study the best practices contributing to the sharing economy’s emergence and growth after the 2008 recession. The authors identify the key characteristics of this new economic sector and share lessons that can be used by other companies. Findings The authors recommend five major takeaways: seeking a more flexible supply; actively watching the trends; leveraging customers like employees; using advanced data science and technology like the sharing economy companies; and proactively avoiding panicked responses. This will help companies succeed during recessionary times – and the boom times that follow. Originality/value This is the first paper that, to the best of the authors’ knowledge, investigates the interplay between the sharing economy and recession and highlights practical lessons.

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Julia Lane Discusses Best Practices for Social Data Science

10.4135/9781526491848 ◽

2019 ◽

Keyword(s):

Best Practices ◽

Data Science ◽

Social Data

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Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00166 ◽

2020 ◽

pp. 108-116

Author(s):

Jill S. Barnholtz-Sloan ◽

Dana E. Rollison ◽

Amrita Basu ◽

Alexander D. Borowsky ◽

Alex Bui ◽

...

Keyword(s):

Best Practices ◽

Patient Outcomes ◽

National Cancer Institute ◽

Data Science ◽

Cancer Centers ◽

Face To Face ◽

The World ◽

Population Science ◽

History Of ◽

National Forum

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute–funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders’ titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was “intersections between informatics, data science, and population science.” We conclude with a discussion on “hot topics” on the horizon for cancer informatics.

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Teach an I-O To Fish: Integrating Data Science Into I-O Graduate Education

Industrial and Organizational Psychology ◽

10.1017/iop.2015.80 ◽

2015 ◽

Vol 8 (4) ◽

pp. 539-544 ◽

Cited By ~ 3

Author(s):

Juliet R. Aiken ◽

Paul J. Hanges

Keyword(s):

Big Data ◽

Informed Consent ◽

Consumer Behavior ◽

Best Practices ◽

National Security ◽

Graduate Education ◽

Data Science ◽

National Security Agency ◽

Internet Searches ◽

Corporate Language

Big data is becoming a buzzword in today's corporate language and lay discussions. From individually targeting advertising based on previous consumer behavior or Internet searches to debates by Congress concerning National Security Agency (NSA) access to phone metadata, the era of big data has arrived. Thus, the Guzzo, Fink, King, Tonidandel, and Landis (2015) discussion of the challenges (e.g., confidentiality, informed consent) that big data projects present to industrial and organizational (I-O) psychologists is timely. If the hype associated with these techniques is warranted, then our field has a clear imperative to debate the ethics and best practices surrounding use of these techniques. We believe that Guzzo et al. have done our field a service by starting this discussion.

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Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs) (Preprint)

10.2196/preprints.17264 ◽

2019 ◽

Author(s):

Jennifer Goldsack ◽

Andrea Coravos ◽

Jessie Bakker ◽

Brinnae Bent ◽

Ariel V. Dowling ◽

...

Keyword(s):

Best Practices ◽

Data Science ◽

Evidence Base ◽

Evaluation Framework ◽

Clinical Validation ◽

Analytical Validation ◽

Science Data ◽

Interdisciplinary Field ◽

Digital Medicine ◽

Monitoring Technologies

UNSTRUCTURED Digital medicine is an interdisciplinary field, drawing together stakeholders with expertise in engineering, manufacturing, clinical science, data science, biostatistics, regulatory considerations, ethics, patient advocacy, and healthcare policy, to name a few. While this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes 1) verification, 2) analytical validation, and 3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

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Challenges of Doing Data-Intensive Research in Teams, Labs, and Groups: Report from the BIDS Best Practices in Data Science Series

10.31235/osf.io/a7b3m ◽

2018 ◽

Author(s):

R. Stuart Geiger ◽

Dan Sholler ◽

Aaron Culich ◽

Ciera Martinez ◽

Fernando Hoces de la Guardia ◽

...

Keyword(s):

Best Practices ◽

Data Science ◽

Intensive Research ◽

Data Intensive ◽

The Social ◽

Pipeline Management ◽

Impostor Syndrome

What are the challenges and best practices for doing data-intensive research in teams, labs, and other groups? This paper reports from a discussion in which researchers from many different disciplines and departments shared their experiences on doing data science in their domains. The issues we discuss range from the technical to the social, including issues with getting on the same computational stack, workflow and pipeline management, handoffs, composing a well-balanced team, dealing with fluid membership, fostering coordination and communication, and not abandoning best practices when deadlines loom. We conclude by reflecting about the extent to which there are universal best practices for all teams, as well as how these kinds of informal discussions around the challenges of doing research can help combat impostor syndrome.

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A standards organization for Open and FAIR neuroscience: the International Neuroinformatics Coordinating Facility

10.31219/osf.io/3rt9b ◽

2019 ◽

Author(s):

Mathew Abrams ◽

Jan G. Bjaalie ◽

Samir Das ◽

Gary F. Egan ◽

Satrajit S Ghosh ◽

...

Keyword(s):

Best Practices ◽

Data Science ◽

Small Data ◽

Sustainable Infrastructure ◽

The World ◽

Formal Procedure ◽

Fair Principles ◽

Community Standards ◽

Diverse Data ◽

Independent Organization

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), established in 2005, is an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards. INCF has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

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