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Large Pigment Database

<div> <div> <p><em>Nowadays, large language models are increasingly human-level intelligent, yet for primitive tasks such as sensing chemicals &ndash; dogs continue to surpass any man-made technology. A main reason for the underdevelopment of AI technology for chemosensing is the lack of a large and open-access dataset. As a step to fix this, we may create a large pigment model by curating already published open-access datasets containing the spectral reflectance properties of 1000s of pigments. This can lead to novel hyperspectral computer-vision technology that can detect the mixing ratios of pigments used in a painting, monitor the nutritional profile of crops, detect skin diseases etc.&nbsp;&nbsp;&nbsp;&nbsp;</em></p> <p>Your&nbsp;<strong>reflectance spectra datasets</strong><em>&nbsp;</em>are highly welcome because presently available datasets on spectrochemical analysis of pigments have the following limitations:</p> <p><a href="https://spectrabase.com/">SpectraBase</a>&nbsp;(belongs to a company that forbids open-access machine learning development)</p> <p><a href="https://www.sciencebase.gov/catalog/item/5807a2a2e4b0841e59e3a18d">USGS Spectral Library Version 7 Data - ScienceBase-Catalog</a> (contains some spectra with reflectance values above 1)</p> <p><a href="https://chsopensource.org/pigments-checker/">Pigments Checkers &ndash; Cultural Heritage Science Open Source (chsopensource.org)</a><strong>&nbsp;</strong>(contains less than 100 pigments)</p> <p><a href="https://www.nature.com/articles/nature14576">A colloidal quantum dot spectrometer | Nature</a><strong>&nbsp;</strong>(contains spectra plotted in images, needs conversion to an array of numbers)</p> <p><a href="https://soprano.kikirpa.be/">SOPRANO (kikirpa.be)</a><strong>&nbsp;</strong>(contains Raman spectra where the excitation frequency is not standardised)</p> <p><a href="https://www.fpbase.org/protein/dlanyfp/">dLanYFP :: Fluorescent Protein Database (fpbase.org)</a><strong>&nbsp;</strong>(contains Fluorescence spectra where the excitation frequency is not standardised)</p> </div> </div>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

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