Year: 2025
Type: data-paper
Abstract: The reflection in natural objects mediates an important fraction of the light reaching animal photoreceptors. Knowledge of the spectral properties of natural objects is increasingly valuable for different research fields. Measured datasets of natural objects' reflectance can offer insights into fundamental and applied research questions, contributing to investigations from coloration and color vision to color analysis and representation. Thus, datasets of natural objects' reflectance across different locations are crucial to assessing the universality and variability of physical visual inputs in diverse environments. However, the Southern Hemisphere is notably underrepresented in publicly available datasets of natural objects. To address this gap, we present a spectral dataset of natural objects' reflectance from the Southern cone of South America, specifically Northwestern Argentina. Our dataset encompasses 532 samples representing diverse natural objects such as barks, flowers, fruits, leaves, plant fruits, stones, and animal specimens, including birds, beetles, and butterflies. By openly sharing this dataset, as a publicly available online resource, we aim to facilitate research across various disciplines, from evolutionary biology to industrial applications.
Source: Scientific Data
Authors: Agustín Gutiérrez, Bárbara Silva, José M. Fanchini, Takuma Morimoto, Pablo A. Barrionuevo, María L. Sandoval-Salinas
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: Wellcome Trust, Pembroke College, University of Oxford, Deutsche Forschungsgemeinschaft, Consejo Nacional de Investigaciones Científicas y Técnicas, Ministerio de Ciencia, Tecnología e Innovación Productiva, Universidad Nacional de Tucumán, Agencia Nacional de Promoción Científica y Tecnológica
Awards: G1049007208, G2624448382, G5171468487, G522962523, Material classification by man and machine: Understanding human perception using psychophysics and convolutional neural networks, G6421489744, G6507561223, G664044631, G6643681439, G7138628335, G7458662893, TRR 135: Kardinale Mechanismen der Wahrnehmung: Prädiktion, Bewertung, Kategorisierung
OpenAlex ID: W4409310070
