II: Data Cooperatives: Rapid and Incremental Data Sharing with Applications to Bioinformatics

II：数据合作社：快速增量数据共享及其在生物信息学中的应用

• 批准号: 0513778
• 负责人: Susan Davidson
• 金额: $ 129.53万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0513778&HistoricalAwards=false
• 关键词: II; Data; Cooperatives; Rapid; Incremental

Generic tools and technologies for creating and maintaining data cooperatives- confederations whose purpose is distributed data sharing-will be developed to overcome the difficultiess encountered in the sharing of information in life sciences, specifically in bioinformatics.The vision of large-scale data sharing has been a long-time goal of the bioinformatics field, much of it proceeding through data integration efforts. However, conventional approaches to data integration do not have the necessary flexibility and adaptability to make the existing and future plethora of data accessible and usable to typical biologists, while keeping it rapidly extensible to new concepts, domains, and types of queries, and thus fostering new research developments. The main reasons are that (1) different biologists work with different types of data and at differing levels of abstraction; (2) schemas in the bioinformatics world are typically large and complex; (3) queries and mappings may "break" without warning because of asynchronous updates; (4) it is logistically, economically and politically difficult to operate centralized data integration facilities. In response to these difficulties data cooperatives emphasize: decentralization for both scalability and flexibility, incremental development of resources such as schemas, mappings, and queries, rapid discovery mechanisms for finding the resources relevant to a topic, and tolerance for intermittent participation of members and for approximate consistency of mappings.More specifically, the technical goals of the proposal include: (1)collaboratively developed yellow pages of biological topics; (2) schema templates, capturing the part of the structure of data pertaining to a specific interest and functioning also as visual templates from which a query form created; (3) incremental specification of mappings; (4) reasoning about uncertainty in mappings by measuring with statistical tools their degree of reliability and using it in query answering; (5) multi-path answering for queries with caching and replication in a large-scale data cooperative where the participation of individual members may not always be assured.Data cooperatives will have broader impact through applications in a variety of scientific and industrial fields, but it is in the field of bioinformatics that they are likely to have an immediate and significant impact. Therefore, a specific data cooperative as a biological testbed for evaluating the proposed technologies. This testbed is based on a small set of databases which are already collaborating and exchanging data related to Plasmodium falciparum. Broader impact will be also be achieved through the proposed educational initiatives, specifically through a "compu-tational orchestra" bioinformatics course which will expose students to data integration issues through project work, and a workshop for the Greater Philadelphia Bioinformatics Alliance (GPBA). Minority involvement will also be encouraged through a GPBA internship program.

通用的工具和技术，用于创建和维护数据合作社-联盟，其目的是分布式数据共享-将被开发，以克服遇到的困难，在生命科学，特别是在bioinformations.The大规模的数据共享的愿景信息共享一直是生物信息学领域的一个长期目标，其中大部分是通过数据集成的努力进行。然而，传统的数据集成方法不具有必要的灵活性和适应性，使现有的和未来的大量数据可访问和可用的典型的生物学家，同时保持它快速扩展到新的概念，域和类型的查询，从而促进新的研究发展。主要原因是：（1）不同的生物学家使用不同类型的数据和不同的抽象层次;（2）生物信息学世界中的模式通常是庞大而复杂的;（3）由于异步更新，查询和映射可能会在没有警告的情况下“中断”;（4）在逻辑上，经济上和政治上难以操作集中的数据集成设施。针对这些困难，数据合作社强调：分散化的可伸缩性和灵活性，资源的增量开发，如模式，映射和查询，快速发现机制，寻找与主题相关的资源，以及容忍成员的间歇性参与和映射的近似一致性。更具体地说，该提案的技术目标包括：（1）合作开发的生物主题黄页;（2）模式模板，捕获与特定兴趣有关的数据结构的一部分，并且还用作创建查询表单的视觉模板;（3）映射的增量规范;（4）通过统计工具测量映射的可靠性程度来推理映射中的不确定性，并将其用于查询回答;（5）在大规模的网络中，利用缓存和复制对查询进行多路径应答，大规模的数据合作社，其中个别成员的参与可能并不总是得到保证。数据合作社将通过在各种科学和工业领域的应用产生更广泛的影响，但在生物信息学领域，它们可能会产生直接和重大的影响。因此，一个特定的数据合作社作为生物试验平台，用于评估拟议的技术。该试验平台以一小部分数据库为基础，这些数据库已经在合作和交换与恶性疟原虫有关的数据。 更广泛的影响也将通过拟议的教育举措，特别是通过一个“计算管弦乐队”生物信息学课程，这将使学生接触数据集成问题，通过项目工作，并为大费城生物信息学联盟（GPBA）的研讨会。还将通过GPBA实习计划鼓励少数族裔参与。