ArticleScientific data2025
Spectral dataset of natural objects' reflectance from the Southern cone of South America.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Are Toxic Butterflies More Easily Detected by Human 'Predators'?Ecology and evolution · 2026Article
- Spectral dataset of natural objects' reflectance from the Southern cone of South America.Scientific data · 2025Article
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Authors and funding
6 authors.
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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.
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