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Big Data. A briefing.

Virginia Todde1, Alessandro Giuliani1

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Summary
This summary is machine-generated.

Biomedical scientists face a data deluge, requiring careful thought to avoid misusing Big Data. This study offers guidance for responsible data mining to address the reproducibility crisis.

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Area of Science:

  • Biomedical Sciences
  • Data Science
  • Epistemology

Background:

  • The increasing volume of biomedical data, termed "Big Data", presents significant challenges for scientific interpretation.
  • The current reproducibility crisis in biomedical sciences necessitates a re-evaluation of data handling and analysis methodologies.

Purpose of the Study:

  • To propose epistemological considerations for the responsible use of Big Data in biomedical research.
  • To provide practical indications for data mining approaches to mitigate the reproducibility crisis.

Main Methods:

  • Conceptual analysis of data interpretation in the context of Big Data.
  • Literature review on data mining techniques and their application in reproducibility.

Main Results:

  • Identified risks of "data base idolatry" and "preconceived refusal" in Big Data analysis.
  • Outlined strategies for a sensible and critical application of data mining in biomedical research.

Conclusions:

  • A thoughtful epistemological approach is crucial for navigating the Big Data landscape in biomedicine.
  • Implementing sensible data mining practices can help improve the reliability and reproducibility of scientific findings.