Science and Research Content

Wiley partners with AMNH to build comprehensive Raman spectral mineral database -

Wiley has announced a collaborative project with the American Museum of Natural History (AMNH) to extend access to the museum’s mineral collection to scientists worldwide. The project aligns Wiley’s focus on research infrastructure with the American Museum of Natural History’s objective of disseminating knowledge through scientific research and education.

The collaboration involves creating spectral reference data derived from the museum’s mineral collection to support scientific research. The project provides access to reference data and supports the use of spectral libraries by researchers. Also, the initiative will establish a Raman spectral database intended to serve as a reference for researchers, students, and industry professionals across fields such as geology, chemistry, archaeology, forensics, and materials science.

The datasets support mineral identification and analysis for academic and commercial purposes and may contribute to the development of analytical models. Wiley will provide access to the data through its KnowItAll analytical chemistry software platform and SpectraBase, its online spectral database. This approach enables access for both licensed software users and the broader research community.

The first release of data is scheduled for spring, with additional data to be added over time. The American Museum of Natural History maintains a collection of more than 120,000 mineral specimens and 5,000 gems. The collection provides a foundation for building spectral libraries covering different mineral compositions. The project supports the museum’s objective to digitize its collection to expand access and support research activities.

Wiley’s spectral database operations provide reference data for identifying and analyzing chemical compounds and materials. Spectral databases contain analytical signatures of substances obtained through techniques such as Raman spectroscopy, enabling comparison of unknown materials with reference standards. These datasets support quality control, research verification, and applications across industries, including pharmaceuticals and materials science.

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