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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Data models, representation and adequacy-for-purpose.

Alisa Bokulich1, Wendy Parker2

  • 1Department of Philosophy, Boston University, 745 Commonwealth Avenue, Room 516, Boston, MA 02215 USA.

European Journal for Philosophy of Science
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the pragmatic-representational (PR) view of scientific data, defining data as purpose-fit representations. It explores implications for data assessment, reuse, and evolutionary history in open data initiatives.

Keywords:
AstrophysicsClimate scienceDataData modelsData processingFit-for-purposeGeosciencesLegacy dataPragmatismScientific models

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Area of Science:

  • Philosophy of Science
  • Data Science
  • Information Science

Background:

  • Traditional views of scientific data lack a comprehensive framework for evaluation and reuse.
  • The iterative reuse and repurposing of datasets highlight the need for understanding data's origin and history.

Purpose of the Study:

  • To introduce and elaborate on the pragmatic-representational (PR) view of data.
  • To explore the implications of the PR view for data assessment, including issues of misrepresentation and context-sensitivity.
  • To connect the PR view to contemporary data practices like open data and data rescue.

Main Methods:

  • Philosophical analysis and conceptual development of the pragmatic-representational (PR) view of data.
  • Critical engagement with existing philosophical accounts of scientific data.
  • Exploration of the implications of the PR view for data evaluation and reuse.

Main Results:

  • The pragmatic-representational (PR) view posits data as purpose-driven representations.
  • Data evaluation should consider their fitness for specific purposes, acknowledging context-sensitivity and potential for misrepresentation.
  • Datasets possess a "phylogeny"—an origin and evolutionary history—crucial for their assessment and future application.

Conclusions:

  • The PR view offers a novel framework for understanding and evaluating scientific data.
  • This perspective enhances insights into data reuse, repurposing, and the significance of data "phylogeny".
  • The PR view has implications for open-data and data-rescue movements, suggesting new research directions.