Using Images and Optimized Workflow for Automated Data Acquisition of Mexican Plant Specimens at MICH

在Inigo Granzow-de la Cerda博士的指导下，密歇根大学获得了一笔赠款，用于使用高通量方法从密歇根大学植物标本馆（MICH）从墨西哥和中美洲收集的10万份植物标本的标本馆标签中获取数据。MICH的植物收藏是北美最大的该地区植物收藏之一。该项目将在早期项目的基础上进行扩展，并将这些标本添加到一个在线目录中，供全世界植物分类学和保护生物学的研究人员使用。这项工作的一个关键创新是使用名为HERBIS的软件，该软件使用光学字符识别（OCR）和自然语言处理来自动提取打印在现有植物标本馆标签上的数据，并将信息汇编到数据库中。这是第一个大规模使用HERBIS的项目之一，并且将为nsf赞助的名为“指定”的收集软件开发一个模块，该模块将向整个社区提供。密歇根大学的项目负责人将与墨西哥负责研究该国生物多样性的联邦机构CONABIO的研究人员合作。该跨学科项目将雇用计算机科学和信息科学专业的学生。美国和墨西哥的本科生将接受数据库设计和数据输入、图像捕获和文本数据处理以及植物标本室管理方面的培训。该项目将加强墨西哥的生物多样性数据，世界上10%以上的开花植物物种都分布在墨西哥。

A grant has been awarded to the University of Michigan under the direction of Dr. Inigo Granzow-de la Cerda to use a high throughput method for capturing data from herbarium labels from 100,000 plant specimens in the University of Michigan Herbarium (MICH) that were collected from Mexico and Central America. The collection at MICH is one of the largest in North America for plants from this region. The project will expand upon an earlier project and add to an online catalog for these specimens that can be used by researchers in plant taxonomy and conservation biology worldwide. A key innovation of the work is to use software called HERBIS, which uses optical character recognition (OCR) and natural language processing to automate the extraction of data printed on existing herbarium labels and compile the information into a database. This is one of the first projects to employ HERBIS on a large scale, and a module will be developed for the NSF-sponsored collection software called SPECIFY that will be made available to the community at large. The project leaders at Michigan will collaborate with researchers in Mexico at CONABIO, the federal institution of Mexico charged with studying biodiversity in that country. The interdisciplinary project will employ students in computer science and information science. Undergraduate students in the US and Mexico will be trained in database design and data entry, image capture and processing of textual data, and herbarium management. The project will enhance the biodiversity data for Mexico, in which more than 10% of the world's flowering plant species occur.