ABI Development: An ontology of evidence types to support biological data management

研究人员通过许多不同的方法产生生物数据，这些方法从实验室实验到基于计算机的分析。这些数据作为研究人员用来进行推断和得出科学结论的证据。生物采集过程试图以标准化的方式获取这些结论和导致这些结论的证据，以便整个科学界都能容易地获取这些信息。实现这一点的最有效方法是使用本体来描述证据类型。本体是术语的受控词汇表，其中每个术语都经过仔细定义，并通过精确的关系与其他术语相关联。证据本体论(EO)是一种社区标准，用于描述用于支持生物学研究中的科学结论的研究证据类型。EO被世界上一些最著名的蛋白质数据库和基因组资源用来捕捉证据信息。该项目的目标是改善EO，并促进更大范围的研究人员使用EO。将通过外展、培训和教育工作，包括讲习班和实习，促进执行主任的工作。更广泛的影响将包括向巴尔的摩市公立学校的学生进行外联工作，重点是教授以受控方式组织信息的重要性。暑期实习生将参与EO开发和生物信息学活动。大量研究广泛生物学主题的科学家将从EO的持续发展和扩大使用中受益。能够以一致和可计算的方式描述证据和断言方法(即无论是人还是机器)是至关重要的，原因有很多。方法的掌握是科学方法的核心，可以影响结果的评估，将结构化证据与存储的数据相关联，即使是从最大的数据库也可以选择性地查询和检索数据，结构化证据系统使自动化质量控制成为可能，这对大规模数据管理至关重要。目前，包括蛋白质数据库、模式生物数据库、表型资源和基因表达数据库在内的近30个生物资源正在使用证据本体(EO)来获取证据信息、支持结构化数据查询、分组相关数据或建立质量控制机制。EO将进一步发展，以解决结构问题，澄清主轴，增加逻辑约束，并将EO映射到相关资源。新的证据类型将根据研究社区的需求不断添加到本体中。将创建一个Web资源，其中包括改进的可视化工具，用于与EO术语相关的证据和数据、完整的用户文档和可下载的内容。选举事务处还将制定质量评估方法，使研究人员能够更好地评估证据。将开展外联、培训和教育，以扩大EO用户基础，并教育研究人员和学生关于捕获证据的价值和方法。EO开发人员将出席科学会议，发表论文，接待实习生，并举办研讨会和科学推广活动。通过改善EO和提高用户意识，研究人员将能够更好地充分利用证据和相关数据。欲了解更多信息，请访问：http://evidenceontology.org.。

Researchers generate biological data from many diverse methods that range from laboratory experiments to computer-based analyses. These data serve as the evidence that researchers use to make inferences and draw scientific conclusions. The process of biocuration seeks to capture these conclusions and the evidence that led to them in a standardized way so that the information is readily accessible to the entire scientific community. The most efficient way to accomplish this is to use an ontology to describe the evidence types. An ontology is a controlled vocabulary of terms where each term is carefully defined and linked to other terms by precise relationships. The Evidence Ontology (EO) is a community standard for describing types of research evidence used to support scientific conclusions in biological research. The EO is used by some of the world?s most prominent protein databases and genomic resources to capture evidence information. The goal of this project is to improve EO and promote its use by a larger community of researchers. The EO will be promoted through outreach, training, and education efforts, including workshops and internships. Broader impacts will include outreach efforts to Baltimore City Public Schools students focusing on teaching the importance of structuring information in a controlled way. Summer interns will engage in EO development and bioinformatics activities. A vast number of scientists researching a wide range of biological topics will benefit from the continued development and expanded use of the EO.The ability to describe both evidence and assertion method (i.e. whether a human or a machine makes a statement) in a consistent and computable fashion is essential for multiple reasons. Capture of methodology is central to the scientific method and can impact evaluation of results, associating structured evidence with stored data allows for selective data queries and retrieval from even the largest databases, and structured evidence systems make automated quality control possible, which is essential for large-scale data management. Nearly 30 biological resources including protein databases, model organism databases, phenotype resources, and gene expression databases currently are using the Evidence Ontology (EO) to capture evidence information, support structured data queries, group related data, or establish quality control mechanisms. EO will be developed further to address structural issues, clarify the main axis, add logical constraints, and map EO to related resources. New evidence types will be continually added to the ontology based on the needs of the research community. A web resource will be created that includes improved visualization tools for evidence and data associated with EO terms, complete user documentation, and downloadable content. EO will also develop quality assessment methodologies to enable researchers to better evaluate evidence. Outreach, training, and education will be conducted to grow the EO user base and educate researchers and students about the value and means of capturing evidence. EO developers will present at scientific conferences, publish papers, host interns, and conduct workshops and science outreach activities. By improving EO and increasing user awareness, researchers will be better able to make the most of evidence and associated data. For more information, please visit: http://evidenceontology.org.