Data from: Crowdsourced geometric morphometrics enable rapid large-scale collection and analysis of phenotypic data

1. Advances in genomics and informatics have enabled the production of large phylogenetic trees. However, the ability to collect large phenotypic datasets has not kept pace. 2. Here, we present a method to quickly and accurately gather morphometric data using crowdsourced image-based landmarking. 3. We find that crowdsourced workers perform similarly to experienced morphologists on the same digitization tasks. We also demonstrate the speed and accuracy of our method on seven families of ray-finned fishes (Actinopterygii). 4. Crowdsourcing will enable the collection of morphological data across vast radiations of organisms, and can facilitate richer inference on the macroevolutionary processes that shape phenotypic diversity across the tree of life.

1. 基因组学（genomics）与信息学领域的进步，已使得构建大型系统发育树（phylogenetic tree）成为现实。然而，大型表型数据集（phenotypic dataset）的采集能力却未能与之同步发展。
2. 本研究提出一种方法，可借助基于图像的众包地标标注（crowdsourced image-based landmarking）技术，快速且精准地获取形态计量数据（morphometric data）。
3. 研究结果表明，在同类数字化任务中，众包工作者的表现与经验丰富的形态学家不相上下。此外，我们还针对隶属于辐鳍鱼纲（Actinopterygii）的7个辐鳍鱼类（ray-finned fishes）科，验证了本方法的速度与准确性。
4. 众包技术将能够支持跨越多类发生辐射演化的生物类群的形态学数据采集，并有助于针对塑造生命之树（tree of life）上表型多样性的宏观进化过程（macroevolutionary processes）开展更为丰富的推断。