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Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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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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Data Integration for the Study of Outstanding Productivity in Biomedical Research.

Clément Aubert1, E Andrew Balas1, Tiffany Townsend1

  • 1Augusta University, GA, USA.

Procedia Computer Science
|August 4, 2023
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Summary

This study addresses challenges in measuring scientific performance beyond traditional metrics. It proposes a multidimensional approach for US biomedical research, integrating diverse data for a comprehensive assessment.

Keywords:
Biomedical ResearchMatching of Research DatabasesResearch EvaluationScientific Performance

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Area of Science:

  • Biomedical research
  • Scientific performance analysis
  • Research impact assessment

Background:

  • Traditional bibliometric measures (publications, citations) are insufficient for assessing scientific productivity.
  • Growing diversity of research databases challenges single-metric evaluations.
  • Need for a broader outcome space beyond publications.

Purpose of the Study:

  • To analyze the improvement of scientific performance in a multidimensional outcome space.
  • Focus on US-based biomedical research.
  • To present a solution for data aggregation challenges in research assessment.

Main Methods:

  • Developing a common platform to gather heterogeneous datasets (CSV, XML, XLS).
  • Addressing data merging and linking complexities.
  • Exploring methods for assessing impact in a multidimensional space.

Main Results:

  • A solution for the initial challenge of data gathering from diverse sources has been developed.
  • Identified strategies for merging and linking overlapping datasets.
  • Outlined approaches for assessing research production and inclusive practices.

Conclusions:

  • A comprehensive, multidimensional approach is crucial for evaluating scientific performance.
  • Data aggregation is a key first step in overcoming limitations of traditional metrics.
  • Further research is needed to fully integrate and assess diverse research outcomes.