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Jimmy K Yu1, Marcos Martínez-Romero1, Matthew Horridge1

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AMIA ... Annual Symposium proceedings. AMIA Symposium
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Este estudio presenta un marco de software para simplificar la armonización de datos, haciendo que los datos de investigación sean más encontrables, accesibles, interoperables y reutilizables (FAIR). La herramienta agiliza los procesos complejos de alineación de datos para mejorar la calidad y usabilidad de los datos.

Palabras clave:
armonización de datosintegración de datosFAIR datareproducibilidadinteroperabilidadRADx Data Hubciencia de datosbioinformáticabiología computacional

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Área de la Ciencia:

  • Ciencia de datos
  • Bioinformática
  • Biología computacional

Sus antecedentes:

  • Los datos primarios de investigación deben cumplir con los principios FAIR (localizables, accesibles, interoperables y reutilizables) en la era del big data.
  • La armonización de datos es crucial para mejorar la interoperabilidad y la reutilización, pero es un desafío debido a la experiencia requerida.

Objetivo del estudio:

  • Presentar un marco de software que facilite protocolos de armonización de datos basados en principios y reproducibles.
  • Permitir a los profesionales de datos ejecutar una armonización transparente y alineada con los objetivos.

Principales métodos:

  • Se desarrolló un marco de software utilizando operaciones primitivas parametrizables para construir transformaciones de armonización.
  • Se implementó la contabilidad automatizada de las transformaciones ejecutadas.
  • Se estableció un modelo de representación de datos novedoso y una estrategia de armonización.

Principales resultados:

  • Se demostró una aplicación de prueba de concepto dentro del RADx Data Hub.
  • El marco permite la creación de protocolos de armonización transparentes y reproducibles.

Conclusiones:

  • El marco de software simplifica la armonización compleja de datos, mejorando la interoperabilidad y reutilización de los datos.
  • Facilita el cumplimiento de los principios de datos FAIR para la investigación de big data.