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这项建议提供了一个机会，以解决科学家可以跨经济和地理边界访问不断增长的数据存储库这一问题。然而，无论是个人创新还是丰富合作的形成，都仍然依赖于传统的研究和社会机制。虽然虚拟组织有助于小组之间访问数据环境，但成员仍然必须主动寻求协作。分享分析工具的困难，以及对如何使用这些工具缺乏了解，造成了摩擦，阻碍了从数据及其使用中获取最大利益。如果科学界能够将用户数据交互(UDI)数据的收集正规化，并根据这些数据开发出可操作的特征行为模式，那么这种摩擦就可以缓解，科学家们可以用目前想象不到的有行为意义的方式联系起来。在这份提案中，讨论了解决这个问题的机会。数据是科学的命脉。最近的融资机会推动了对以更正式和标准的方式上传、归档和管理数据的支持。然而，通过数据探索工具实际使用数据仍然是一个变数很大的过程。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交互的机会。这类用户在搜索、探索和使用数据时进行的交互模式是一个很大程度上尚未开发的新联系和新学习的机会，可以帮助研究人员确定有用的探索模式或差距，甚至可以增加交互和创新的新合作伙伴。这种关于用户如何浏览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块以支持收集UDI数据的标准、社区开发数据探索工具以及探索UDI数据可能会从根本上改变科学和工程实践。将这种数据和分析工具托管在一个共享的网络基础设施中，还可以对其使用和有效性进行前所未有的研究。这项概念化研究的目标将是为DIBBS方案确定一个实施项目。为了实现这一目标，方法将是了解各种用户社区目前使用的数据分析工具的种类，以及他们希望创建和共享的工具，并探索随后可以收集和利用的UDI数据。数据源将被描述为用户可以指定查询并接收半结构化结果的任何服务。举例来说，这可以包括用户通过表单与之交互的在线数据库、数据立方体的图形界面、甚至在线模拟工具。Proposing团队可以访问目前数千用户正在使用的三个这样的工具包(Rappture Toolkit、iKNEER和DataView)，以作为分析工具和UDI数据的来源进行研究。具体地说，接触这些系统的开发人员将提供有关这些系统如何生成UDI数据以及它的重要功能可能是什么的信息。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结合两个活动举行的两个更大的讨论：Hubbub 2013和NSF S2I2概念化项目会议。智力价值：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数据的转换潜力。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，根据这些数据开发模式，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合作。合作将增加，并将具有更大的实质性。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，这将加速研究和创新。在建立科学合作的传统方法中，代表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联系--不仅仅是通过既定的声誉，而是基于他们与数据的互动。由于这项工作不是专门针对一个虚拟组织或数据工具的，它将在使用数据和数据分析工具的不同科学界拥有广泛的影响。

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.