捕捉马拉维湖慈鲷骨骼多样性的全身微型 CT 扫描库。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Callum V Bucklow, Martin J Genner, George F Turner, James Maclaine, Roger Benson, Berta Verd
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引用次数: 0

摘要

在这里,我们描述了一个可免费获取、易于处理的马拉维湖慈鲷鱼类 56 个物种(116 个标本)的全身 μCT 扫描数据集,该数据集捕捉到了这一显著适应性辐射中存在的绝大多数形态变异。我们在讨论各自的生态群落时对扫描标本进行了背景分析,并提出了利用这些数据进行宏观进化研究的可能性。此外,我们还介绍了一种每小时有效μCT扫描(平均)23个标本的方法,在保持高分辨率的同时,限制了扫描时间,降低了经济成本。我们从数据集中的多个标本中重建了多个骨骼的三维模型,证明了这种方法的实用性。我们希望该数据集将有助于对这一迷人的系统进行进一步的形态学研究,并与其他慈鲷的适应性辐射进行更广泛的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A whole-body micro-CT scan library that captures the skeletal diversity of Lake Malawi cichlid fishes.

Here we describe a dataset of freely available, readily processed, whole-body μCT-scans of 56 species (116 specimens) of Lake Malawi cichlid fishes that captures a considerable majority of the morphological variation present in this remarkable adaptive radiation. We contextualise the scanned specimens within a discussion of their respective ecomorphological groupings and suggest possible macroevolutionary studies that could be conducted with these data. In addition, we describe a methodology to efficiently μCT-scan (on average) 23 specimens per hour, limiting scanning time and alleviating the financial cost whilst maintaining high resolution. We demonstrate the utility of this method by reconstructing 3D models of multiple bones from multiple specimens within the dataset. We hope this dataset will enable further morphological study of this fascinating system and permit wider-scale comparisons with other cichlid adaptive radiations.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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