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

将开发用于创建和维护数据合作社（目的是分布式数据共享的联盟）的通用工具和技术，以克服在生命科学，特别是生物信息学中共享信息时遇到的困难。大规模数据共享的愿景一直是生物信息学领域的长期目标，其中大部分是通过数据集成努力进行的。然而，传统的数据集成方法没有必要的灵活性和适应性，使现有和未来的大量数据对典型的生物学家来说是可访问和可用的，同时保持它快速扩展到新的概念、领域和查询类型，从而促进新的研究发展。主要原因是：(1)不同的生物学家处理不同类型的数据和不同的抽象层次；(2)生物信息学领域的模式通常是庞大而复杂的；(3)由于异步更新，查询和映射可能在没有警告的情况下“中断”；(4)运营集中式数据集成设施在后勤、经济和政治上都存在困难。为了应对这些困难，数据合作社强调：可伸缩性和灵活性的去中心化，模式、映射和查询等资源的增量开发，用于查找与主题相关的资源的快速发现机制，以及对成员间歇性参与和映射近似一致性的容忍。更具体地说，该提案的技术目标包括：(1)合作开发生物主题黄页；(2)模式模板，捕获与特定兴趣相关的数据结构部分，并作为创建查询表单的可视化模板；(3)映射增量规范；(4)通过统计工具测量映射的可靠度来推理映射的不确定性，并将其用于查询回答；(5)在大型数据协作中，个体成员的参与可能不总是得到保证的情况下，具有缓存和复制的查询的多路径应答。通过在各种科学和工业领域的应用，数据合作社将产生更广泛的影响，但它们可能在生物信息学领域产生直接和重大的影响。因此，一个特定的数据合作社作为评估所提出的技术的生物试验台。这个试验台基于一组已经在合作和交换与恶性疟原虫有关的数据的数据库。更广泛的影响还将通过拟议的教育举措实现，特别是通过“计算管弦乐队”生物信息学课程，该课程将通过项目工作使学生接触数据集成问题，并为大费城生物信息学联盟（GPBA）举办研讨会。少数族裔的参与也将通过GPBA实习项目得到鼓励。

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.