Applications and Data Reuse Use Cases

The INFRA-ART Spectral Library follows the European Commission’s recommendation on access to scientific information as well as the FAIR Guiding Principles on research data that result from publicly funded research. This resource supports cross-disciplinary research and the reliable reuse of scientific data for academic, research, innovation, and educational purposes.

The INFRA-ART Spectral Library provides access to a comprehensive collection of (multi)spectral datasets, supporting research across various scientific fields. This page highlights some of the academic institutions, research centers, and facilities that have requested access to our datasets, along with a selection of publications that showcase the use of the INFRA-ART Spectral Library data service. Data reuse tracking is based on the File Access Request Forms recevied and the citations of our data service in publications, as recorded in Google Scholar.

Users and Publications Showcasing Data Reuse

Academic and research institutions using the INFRA-ART Spectral Library datasets


Citations in literature of the INFRA-ART Spectral Library data service

FAIR / good data practices


Applications & use cases in heritage science

  • • Reichert et al. (2025) Database of Pigments and Dyes in Historical Manuscripts Using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (Drifts) and Hyperspectral Imaging (Hsi). Available at SSRN (preprint). http://dx.doi.org/10.2139/ssrn.5189960
  • • Angheluță et al (2025) Documenting Romania's Wooden Churches: Integrating Modern Digital Platforms with Vernacular Conservation, in Heritage 8(3), 103. https://doi.org/10.3390/heritage8030103
  • • Bombini et al. (2025) Towards virtual painting recolouring using vision transformer on x-ray fluorescence datacubes, in Machine Learning-Science and Technology 6(1), 015058. https://doi.org/10.1088/2632-2153/adb937
  • • Innocenti et al. (2024) Historical Pigments and Paint Layers: Raman Spectral Library with 852 nm Excitation Laser, in Minerals, 14(6), 557. https://doi.org/10.3390/min14060557
  • • Sathiyamani et al. (2024) Material Characterisation of 19–20th Century Manuscripts from Northern Thailand, in Restaurator. International Journal for the Preservation of Library and Archival Material 45(2-3): 117-140. https://doi.org/10.1515/res-2023-0028
  • • Ratoiu et al (2023). A multi-analytical study of a 17th-century Wallachian icon depicting the "Mother of God with Child", in Heritage 6(10): 6931-6948; https://doi.org/10.3390/heritage6100362
  • • Ghervase and Cortea (2023) Lighting up the heritage sciences: the past and future of laser-induced fluorescence spectroscopy in the field of cultural goods, in Chemosensors 11(2), 100. https://doi.org/10.3390/chemosensors11020100
  • • Bombini (2023) ganX -- generate artificially new XRF a python library to generate MA-XRF raw data out of RGB images, arXiv:2304.14078 (preprint). https://doi.org/10.48550/arXiv.2304.14078

Applications & use cases in other research fields

  • • Bolea-Fernandez et al. (2024) Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials, in Journal of Analytical Atomic Spectrometry, https://doi.org/10.1039/D4JA90052A
  • • Bombini et al. (2024) Datacube segmentation via Deep Spectral Clustering, in Machine Learning-Science and Technology 5(3): 035024. https://doi.org/10.1088/2632-2153/ad622f

Master & PhD theses

  • • N. Deschand, Machine Learning for Recognition of Planetary Materials from X-ray Fluorescence Spectral Data, KTH Royal Institute of Technology, Stockholm, Sweden, 2024. – master thesis

Data Access and Collaboration Opportunities

We encourage researchers and academic institutions to access the datasets available in the INFRA-ART Spectral Database for their own work. To find out on how to access the data please refer to our Data Access Policy.


Why Reuse Data?

Reusing spectral data offers a number of significant benefits, both to researchers and the broader scientific community. By promoting the reuse of scientific data, we not only enhance the efficiency and transparency of research but also contribute to the broader principles of open science, fostering global knowledge-sharing and collaboration. When data is reused effectively, it amplifies its impact, reducing duplication of effort and enabling more innovative and interdisciplinary discoveries.

Key benefits of data reuse:

  • • Resource optimization: Utilizing pre-existing datasets allows researchers to bypass the time-intensive and costly process of data collection. This enables them to dedicate more effort to data analysis, interpretation, and generating new insights while conserving valuable resources.
  • • Interdisciplinary applications: Spectral data can be applied across various research domains — such as material science, environmental science, forensics, and biotechnology, among others — encouraging cross-sector collaboration and solutions.
  • • Alignment with FAIR principles: Data reuse supports the FAIR Guiding Principles, ensuring that data remains Findable, Accessible, Interoperable, and Reusable. This enhances data longevity and usability across different research domains.
  • • Enhanced collaboration and impact: Data reuse promotes collaboration, increases research visibility, and ensures that results can be independently verified and built upon by other researchers.