EAGER: DynaMo: A novel computational workflow for dynamic monographs and revisionary systematics

EAGER：DynaMo：用于动态专着和修订系统学的新颖计算工作流程

• 批准号: 1939128
• 负责人: Felipe Zapata
• 金额: $ 28万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939128&HistoricalAwards=false
• 关键词: EAGER; DynaMo; novel; computational; workflow

Taxonomic monographs combine information about the appearance, occurrence, genealogical relationships, and classification of organisms and provide methods for their identification. This baseline information is critical for understanding the diversity of life and helps inform other biological research questions. Current taxonomic monographs suffer from being static documents that do not provide an efficient means of accessing or analyzing the underlying data. This project will develop a linked set of computational tools (DynaMo) to produce a dynamic monograph that streamlines the workflow, integrates specimen-based data, and facilitates analysis of these data. Despite being in the early stages of development DynaMo shows immense potential to transform how taxonomic monographs are generated, analyzed and presented. The project will train a post-doctoral researcher and undergraduates, including members of underrepresented groups, in systematic biology, bioinformatics, and software development. Two workshops on dynamic monographs and quantitative taxonomy will help train the broader systematics community. The resulting DynaMo software, and all data generated by the project, will be made freely available through public databases and repositories.While other areas of systematics have adopted modern approaches, the production of taxonomic monographs has been hindered by the lack of efficient computational tools to efficiently integrate and update the large data sets and complex analyses that underly monographic research. This project will develop the digital infrastructure (DynaMo) to enable systematists to document and interact with all the data management and analysis tools needed to produce a monograph and help make monographic methods and analyses more transparent, reproducible, and extendable. The digital infrastructure underpinning this workflow will be a generic relational database that stores all primary biodiversity data as well as intermediate resources for faster retrieval. The data stored in the database will be integrated and analyzed via modular pipelines. This database will be continuously updated as new data and results become available and the analysis pipelines will automatically generate new results whenever the underlying data in the database change. The results of analyses, interactive visualizations, raw data, and text will be machine-readable and accessible via a dynamic monograph, an online document that is perpetually up-to-date because it provides an interface to the underlying specimen-based database.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分类学专着结合了有关外观，发生，家谱关系和生物体分类的信息，并为其鉴定提供了方法。 该基线信息对于理解生活的多样性并有助于告知其他生物学研究问题至关重要。 当前的分类学专着是静态文档，这些文档无法提供有效的方法来访问或分析基础数据。该项目将开发一组链接的计算工具集（Dynamo），以生成动态专着，该专着简化工作流程，集成基于样品的数据并促进对这些数据的分析。尽管处于开发的早期阶段，发电机表现出巨大的潜力，可以改变分类学专着的产生，分析和呈现。该项目将在系统生物学，生物信息学和软件开发中培训一名博士后研究人员和本科生，包括代表性不足的群体成员。 关于动态专着和定量分类学的两个讲习班将有助于培训更广泛的系统学界。由此产生的发电机软件以及该项目生成的所有数据将通过公共数据库和存储库免费提供。虽然Systematics的其他领域采用了现代方法，但由于缺乏有效的计算工具来制作分类学专着的生产，因此无法产生分类学专着。有效地整合和更新大型数据集和基础专业研究的复杂分析。该项目将开发数字基础架构（Dynamo），以使系统主义者能够记录并与生产专着所需的所有数据管理和分析工具进行交互，并有助于制作专有方法，并更加透明，可复制和可扩展。基于此工作流程的数字基础架构将是一个通用的关系数据库，该数据库存储所有主要的生物多样性数据以及中间资源，以更快地检索。数据库中存储的数据将通过模块化管道进行集成和分析。随着新数据和结果可用，该数据库将不断更新，并且分析管道将在数据库中的基础数据更改时自动生成新的结果。分析，交互式可视化，原始数据和文本的结果将是可以通过动态专着来访问的，这是一个永久最新的在线文档，因为它为基于基于标本的数据库提供了接口。奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响审查标准评估值得支持。

项目成果

Fast Likelihood Calculations for Automatic Identification of Macroevolutionary Rate Heterogeneity in Continuous and Discrete Traits

自动识别连续和离散特征宏观进化速率异质性的快速似然计算

• DOI: 10.1093/sysbio/syac035
• 发表时间: 2022
• 期刊: Systematic Biology
• 影响因子: 6.5
• 作者: Grundler, Michael C.;Rabosky, Daniel L.;Zapata, Felipe;Uyeda, ed., Josef
• 通讯作者: Uyeda, ed., Josef