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.

分类学专著结合了有关生物的外观、发生、谱系关系和分类的信息，并提供了鉴定它们的方法。这些基线信息对于了解生命的多样性至关重要，并有助于为其他生物学研究问题提供信息。目前的分类学专著都是静态文档，不能提供访问或分析底层数据的有效方法。该项目将开发一套相关的计算工具（DynaMo），以生成一个动态专著，简化工作流程，集成基于样本的数据，并促进这些数据的分析。尽管还处于开发的早期阶段，但DynaMo显示出巨大的潜力来改变分类专著的生成、分析和呈现方式。该项目将在系统生物学、生物信息学和软件开发方面培养一名博士后研究员和本科生，包括代表性不足的群体成员。关于动态专著和数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DynaMo软件和项目产生的所有数据将通过公共数据库和存储库免费提供。虽然分类学的其他领域已经采用了现代方法，但由于缺乏有效的计算工具来有效地整合和更新大型数据集和复杂的分析，分类学专著的生产受到阻碍，这些专著是专著研究的基础。该项目将开发数字基础设施（DynaMo），使系统学家能够记录并与生成专题论文所需的所有数据管理和分析工具进行交互，并帮助使专题论文的方法和分析更加透明、可复制和可扩展。支撑这一工作流程的数字基础设施将是一个通用关系数据库，该数据库存储所有主要生物多样性数据以及用于更快检索的中间资源。存储在数据库中的数据将通过模块化管道进行集成和分析。当新的数据和结果可用时，该数据库将不断更新，每当数据库中的底层数据发生变化时，分析管道将自动生成新的结果。分析结果、交互式可视化、原始数据和文本将是机器可读的，并可通过动态专著访问，动态专著是一种永久更新的在线文档，因为它为底层基于样本的数据库提供了一个接口。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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