Gene Wiki: A community-maintained knowledge base of biomedical information

Gene Wiki：社区维护的生物医学信息知识库

• 批准号: 9900788
• 负责人: ANDREW I SU
• 金额: $ 56.33万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9900788
• 关键词: Academic achievement; Address; Area; Automobile Driving; Back; Biological; Biology; Biomedical Research; Communities; Computer software; Computers; Data; Data Reporting; Databases; Ecosystem; Ensure; FAIR principles; Foundations; Genes; Goals; Growth; Human; Incentives; Individual; Infectious Diseases Research; Informatics; Information Management; Information Resources Management; Infrastructure; Journals; Knowledge; Legal; Libraries; Metagenomics; Modeling; Occupations; Ontology; Organism; Paper; Provider; Publications; Publishing; Pythons; Reading; Reporting; Research; Research Project Grants; Resources; Rewards; Running; Scientist; Series; Shapes; Sister; Source; Structure; Sum; System; Text; Trust; Validation; Vision; Vocabulary; Work; Writing; annotation system; base; biomedical resource; crowdsourcing; data ingestion; data modeling; data pipeline; data quality; design; experimental study; genome annotation; genome database; improved; interoperability; journal article; knowledge base; pathogen; repository; software development; structured data; synergism; tool; wiki

ABSTRACT The biomedical research enterprise is incredibly productive, generating new knowledge at an unprecedented pace. However, as a community, we do a relatively poor job organizing and managing that knowledge so that it is maximally useful for the design and interpretation of other experiments. Scientific research is most efficient when new hypotheses are informed by the totality of past findings, and that scientific knowledge is Findable, Accessible, Interoperable, and Reusable (FAIR). Unfortunately the vast majority of research is published only in free-text, unstructured journal articles, rendering the findings very difficult to integrate and compute upon. This proposal describes the use of crowdsourcing to address this challenge in biomedical knowledge management. It specifically proposes to leverage Wikidata, which has the goal of creating a comprehensive knowledge base that both humans and computers can both read and edit. Wikidata is run by the same organization that runs Wikipedia, and like its sister project, it employs the principle of crowdsourcing to tackle a grand challenge in information management. Both Wikipedia and Wikidata invite and empower the community at large to collaboratively add, edit, and refine content. In this proposal, we continue our work to create the world's largest open and FAIR knowledge base of biomedical information within Wikidata. This proposal include three Specific Aims. First, we will improve both the quantity and quality of biomedical information in Wikidata. Quantity will be increased by loading several key biomedical vocabularies and ontologies, and data quality will be made more rigorous by the introduction of formal and computable data models. Second, we will facilitate and incentivize contributions of data by third- party data contributors. This Aim will be achieved by extending our python programming library for reading from and writing to Wikidata, and by creating automated reports that notify resource providers when new relevant content is added or edited. Third, we will also seek to encourage contributions from domain experts using targeted incentives. Specifically, this aim will develop interfaces to Wikidata that provide integrated data reports that are otherwise unavailable, as well as extend the Gene Wiki Reviews series of invited reviews, which rewards contributions with traditional metrics of academic achievement. Finally, underlying these three Specific Aims will be a Driving Biological Project focusing in infectious disease research, which will ensure the tools and resources developed will have practical benefit to discovery-oriented research projects.

摘要 生物医学研究企业的生产力令人难以置信，以前所未有的速度产生新知识。 步伐。然而，作为一个社区，我们在组织和管理这些知识方面做得相对较差， 对其他实验的设计和解释是最有用的。科学研究最有效率 当新的假设是由过去的全部发现所告知的，并且科学知识是可发现的， 可扩展、可互操作和可重用（FAIR）。不幸的是，绝大多数的研究成果只发表在 在自由文本，非结构化的期刊文章，使研究结果非常难以整合和计算。 该提案描述了如何利用众包来应对生物医学知识方面的这一挑战 管理它特别建议利用维基数据，其目标是创建一个全面的 人类和计算机都可以阅读和编辑的知识库。维基数据由同一个人运营 维基百科是一个运行维基百科的组织，就像它的姐妹项目一样，它采用众包的原则来解决一个 信息管理的巨大挑战。维基百科和维基数据都邀请并授权社区 协作地添加、编辑和细化内容。 在这项提案中，我们继续努力，创建世界上最大的开放和公平的知识库， 维基数据中的生物医学信息。该提案包括三个具体目标。首先，我们将改善两者 维基数据中生物医学信息的数量和质量。数量将增加通过加载几个关键 生物医学词汇表和本体，以及数据质量将更加严格的引进， 形式化和可计算的数据模型。第二，我们将促进和激励第三方的数据贡献- 数据贡献者。这个目标将通过扩展我们的python编程库来实现阅读 从维基数据和向维基数据写入，并通过创建自动报告，通知资源提供者，当新的 添加或编辑相关内容。第三，我们还将寻求鼓励领域专家的贡献 有针对性的激励措施。具体来说，这一目标将开发维基数据的接口，提供集成数据 报告，否则无法获得，以及扩展基因维基评论系列邀请评论， 它用传统的学术成就衡量标准来奖励贡献。最后，在这三个 具体目标将是一个驱动生物项目，重点是传染病研究，这将确保 开发的工具和资源将对以发现为导向的研究项目产生实际效益。