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Related Experiment Video

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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Harvest: a web-based biomedical data discovery and reporting application development platform.

Michael J Italia1, Jeffrey W Pennington, Byron Ruth

  • 1Center for Biomedical Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA;

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|December 5, 2013
PubMed
Summary
This summary is machine-generated.

Biomedical researchers can now use Harvest, an open-source framework, to build custom data discovery applications. This tool simplifies exploring complex lab and clinical data through intuitive web interfaces and visualizations.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Software Engineering

Background:

  • Biomedical researchers face challenges in managing and interpreting large volumes of complex laboratory and clinical data.
  • The increasing complexity and sheer amount of data necessitate advanced tools for effective data discovery and analysis.

Purpose of the Study:

  • To develop an open-source framework, Harvest, that facilitates the creation and deployment of custom data discovery software applications.
  • To provide researchers with an intuitive, web-based interface for exploring multidimensional data in real-time.

Main Methods:

  • Harvest is an open-source framework comprising modular components for rapid software development.
  • It utilizes standards-based, open-source technologies for data modeling, abstraction, and a customizable web client.
  • The framework supports visual representations of multidimensional data types.

Main Results:

  • Harvest enables the development of custom applications for enhanced data discovery.
  • It offers an intuitive, web-based interface for iterative exploration of complex datasets.
  • The framework supports domain-specific data modeling and abstraction of complex data models.

Conclusions:

  • Harvest addresses the critical need for accessible and understandable complex biomedical data.
  • The framework promotes efficient data exploration and discovery in research and development settings.
  • Its open-source nature and modular design allow for flexible and customizable solutions.