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Related Concept Videos

X-ray Diffraction of Biological Samples01:10

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Polymer Microarrays for High Throughput Discovery of Biomaterials
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FAIR data enabling new horizons for materials research.

Matthias Scheffler1,2, Martin Aeschlimann3, Martin Albrecht4

  • 1Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany.

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|April 28, 2022
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Summary
This summary is machine-generated.

Making research data findable, accessible, interoperable, and reusable (FAIR) is crucial for advancing materials science. Preparing data for artificial intelligence (AI) analysis will transform scientific discovery.

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Area of Science:

  • Condensed matter physics
  • Materials science
  • Chemistry

Background:

  • Societal progress relies on advancements in materials science, impacting energy, health, and IT sectors.
  • Vast amounts of research data are generated daily, holding significant potential value.
  • Current data practices limit the utility of this research data, hindering knowledge extraction.

Purpose of the Study:

  • To discuss the necessity of a FAIR data infrastructure for materials science.
  • To explore how to transform raw research data into valuable knowledge.
  • To prepare the materials science field for data-driven discovery using AI.

Main Methods:

  • Discussing the principles of FAIR data management.
  • Highlighting the role of data analytics and artificial intelligence (AI).
  • Proposing strategies for making materials science data 'findable and AI ready'.

Main Results:

  • FAIR data infrastructure is essential for unlocking the value of research data.
  • Data analytics and AI can refine research data into actionable knowledge.
  • A proactive approach to data preparation is needed for future scientific endeavors.

Conclusions:

  • Implementing FAIR data principles is critical for materials science innovation.
  • Making data 'findable and AI ready' will revolutionize scientific research.
  • The field must adapt to new data-centric methodologies for continued progress.