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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Eight practices for data management to enable team data science.

Andrew McDavid1, Anthony M Corbett2, Jennifer L Dutra2

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.

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|May 5, 2021
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Summary
This summary is machine-generated.

This study introduces a new operational framework and data management best practices for clinical and translational research. It enhances data accessibility and interdisciplinary collaboration for team-based data science.

Keywords:
Data analysisbioinformaticsdata managementdata sciencedatabasespediatricresearch informaticssystems biology

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Area of Science:

  • Clinical and Translational Research
  • Data Science
  • Bioinformatics

Background:

  • Data science integration with data collection is unique in clinical research.
  • Traditional data science roles often isolate scientists from data collectors.
  • Effective translational research requires innovative data organization and management.

Purpose of the Study:

  • To propose an operational framework for clinical and translational data science.
  • To define best practices for data management in this setting.
  • To enhance the accuracy and speed of data science in translational research.

Main Methods:

  • Developed an operational framework tailored to integrated research teams.
  • Defined eight best practices for data management.
  • Utilized a customized open-source LabKey platform for data management and collaboration.

Main Results:

  • Implemented best practices in a customized LabKey platform.
  • Applied the platform to longitudinal multidomain data from over 3000 subjects.
  • Demonstrated improved availability of analytical datasets.

Conclusions:

  • The framework facilitates team-based data science unique to clinical and translational research.
  • Lowered barriers to interdisciplinary collaboration.
  • Enhanced the accessibility of analytical datasets for research.