CIF21 DIBBs: Conceptualization of the Social and Innovation Opportunities of Data Analysis

CIF21 DIBBs：数据分析的社会和创新机会的概念化本提案提供了一个机会，以解决科学家跨越经济和地理边界访问不断增长的数据存储库的问题。然而，无论是个体创新还是丰富合作的形成，仍然依赖于传统的研究和社会机制。虽然虚拟组织有助于访问组间的数据环境，但成员仍然必须主动寻求协作。共享分析工具的困难，以及缺乏对这些工具如何使用的理解，造成了阻碍从数据及其使用中提取最大利益的摩擦。如果科学界能够形式化用户数据交互（UDI）数据的收集，并从中开发出可操作的特征行为模式，那么摩擦就可以缓解，科学家们就可以以目前无法想象的有意义的行为方式联系起来。在这个建议中讨论了解决这个问题的机会。数据是科学的命脉。最近的投资机会推动了以更正式和标准的方式上传、归档和管理数据的支持。然而，通过数据探索工具对数据的实际使用仍然是一个高度可变的过程。交互式数据探索工具提供了记录研究人员在探索过程中的互动的机会。这些用户在搜索、探索和使用数据时进行的交互模式是一个很大程度上未开发的新连接和新学习的机会，可以帮助研究人员确定有用的探索模式或差距，甚至是可以增加交互和创新的新合作伙伴。这些关于用户如何探索数据的数据在这里被称为？用户数据交互（UDI）数据创建网络基础设施构建模块来支持UDI数据的收集标准、数据探索工具的社区开发以及UDI数据的探索可以从根本上改变科学和工程的实践。将这些数据和分析工具托管在共享的网络基础设施中，还可以对它们的使用和有效性进行前所未有的研究。这个概念化研究的目标是为DIBBs计划定义一个实现项目。要实现这一目标，方法将是了解各种用户社区当前使用的数据分析工具的种类，以及他们想要创建和共享的工具，并探索可以收集和利用的后续uddi数据。数据源将被描述为用户可以在其中指定查询并接收半结构化结果的任何服务。举例来说，这可能包括用户通过表单与之交互的在线数据库、数据多维数据集的图形界面，甚至是在线模拟工具。提议团队可以访问目前成千上万的用户正在使用的三个这样的工具包（Rappture Toolkit、iKNEER和DataView），作为分析工具和uddi数据的来源进行研究。具体来说，访问这些系统的开发人员将提供有关这些系统如何生成UDI数据以及其重要特性可能是什么的信息。在从活跃的社区和小组讨论中建立了理解之后，信息收集的最后一步将是与两个活动一起举行的两个更大的讨论：HUBbub 2013和NSF S2I2概念化项目会议。知识价值：这项研究将确定共享数据分析工具的社会和技术障碍，以及UDI数据的转型潜力。这项活动的智力价值将是一个基于证据的网络基础设施环境蓝图，该环境将自动收集UDI数据，从这些数据中开发模式，并以一种可接受的平衡效率和隐私的方式促进基于这些模式的扩大发现和协作。合作将会增加，内容也会更丰富。更广泛的影响：这项工作将为研究人员、教育工作者和学生之间建立新的科学联系铺平道路，从而加速研究和创新。在建立科学合作的传统方法中，代表性不足的群体固有地面临的困难将通过实施拟议的工作来弥合，允许每个人都与工具和其他研究人员建立联系。不仅仅是建立声誉，而是基于他们与数据的互动。由于这项工作不是特定于一个虚拟组织或数据工具，因此它将在使用数据和数据分析工具的不同科学社区中具有广泛的影响力。

CIF21 DIBBs: Conceptualization of the Social and Innovation Opportunities of Data AnalysisThis proposal presents an opportunity to work on the problem that scientists have access to continuously growing data repositories across economic and geographic boundaries. However, both individual innovation and the formation of rich collaborations still rely on traditional research and social mechanisms. While virtual organizations help with access to data environments among groups, members must still proactively seek to collaborate. The difficulty of sharing analysis tools, and the lack of understanding of how such tools are used, create friction that impedes extracting the greatest benefit from data and its usage. If the scientific community can formalize collection of User Data Interaction (UDI) data and develop actionable characteristic behavior patterns from it, the friction can be relieved and scientists can be connected in behaviorally meaningful ways that are not currently imagined. In this proposal is discussed the opportunity for working on the problem. Data is the lifeblood of science. Recent funding opportunities have fueled support for uploading, archiving, and managing data in more formal and standard ways. However, the actual use of data through data exploration tools is still a highly variable process. Interactive data exploration tools provide the opportunity to record researcher interactions during the exploration process. The pattern of interactions such users undertake while searching, exploring, and using data is a largely unexploited opportunity for new connections and new learning that could help researchers identify useful exploration modes or gaps, and even new collaborative partners that could increase interactions and innovation. Such data about how users explore data are here termed, ?User-Data Interaction (UDI) Data.? Creating cyberinfrastructure building blocks to support a standard for collecting UDI Data, community development of data exploration tools, and the exploration of UDI data could fundamentally change the practice of science and engineering. Having such data and analysis tools hosted within a shared cyberinfrastructure could also allow for unprecedented study of their use and effectiveness.The goal of this conceptualization research will be to define an implementation project for the DIBBs program. To achieve this goal, the approach will be to understand the kinds of data analysis tools that various user communities currently use, those that they would like to create and share, and to explore the ensuing UDI data that could be collected and leveraged. A data source will be characterized as any service into which a user can specify a query and receive a semi-structured result. By way of example, this may include an online database with which users interact through forms, a graphical interface to a data cube, or even an online simulation tool. The proposing team has access to three such toolkits in use by thousands of users today (Rappture Toolkit, iKNEER, and DataView) to study as sources of analysis tools and UDI data. Specifically, access to the developers of these systems will provide information about how such systems could generate UDI data and what its important features may be. Having built an understanding from active communities and small group discussions, the final step of information gathering will be two larger discussions held in conjunction with two events: HUBbub 2013 and an NSF S2I2 conceptualization project meeting. The Intellectual Merit: This research will identify the social and technological roadblocks to sharing data analysis tools, and the transformational potential of UDI data. The intellectual merit of this activity will be an evidence-based blueprint for a cyberinfrastructure environment that will automatically gather UDI data, develop patterns from those data, and facilitate amplified discovery and collaboration based on those patterns in a way that acceptably balances efficacy and privacy. Collaborations will increase and will be of greater substance. Broader Impacts: This work will pave the way for new scientific connections among researchers, educators, and students that will accelerate research and innovation. The difficulties that underrepresented groups inherently face in traditional methods of establishing scientific collaborations will be bridged by an implementation of the proposed work, allowing everyone to connect to tools and other researchers?not solely by established reputation, but based on their interactions with data. Because the work is not specific to one virtual organization or data tool, it will have a broad reach across diverse scientific communities that use data and data analysis tools.