Data Coding, Analysis, Archiving, and Sharing for Open Collaboration: From OpenSHAPA to Open Data Sharing

研究人员现在可以获得比以往更丰富、更详细的行为数据。例如，在研究儿童如何学会走路时，研究人员可以从微型头戴式摄像机收集眼球追踪数据，记录婴儿的眼球运动和视野，从而可以准确地看到孩子在环境中导航时所看的位置。同时，研究人员可以收集高速运动跟踪数据，详细说明儿童肢体运动的轨迹，以及儿童相对于照顾者和障碍物的路径，与人、物体和表面的互动，以及行走、跌倒和互动时的情感反应的视频数据。尽管视频和其他记录技术的广泛可用性，行为研究人员通常只满足于分析一个数据流中的一个变量，而不是在多个数据流中寻找多个变量之间的关系。需要强大的数据分析工具和复杂的数据管理实践来整合不同类型的数据，并将它们相互关联，而很少有研究人员拥有这些工具和实践。此外，研究人员通常是孤立地工作，很少分享可能启发他人研究的数据。如果没有更丰富的分析和数据共享，发展心理学和其他行为科学领域的理论进步就会受到阻碍。本次研讨会的目的是深入研究概念、技术和管理问题，这些问题一旦解决，将允许研究人员在大型、共享的数据集上进行更丰富的分析。研讨会将部分关注新兴开源软件工具OpenSHAPA的未来发展，并将探讨如何将OpenSHAPA扩展到包含新的数据探索和可视化工具，并促进数据管理和数据共享。22位研究人员将参加本次研讨会，他们分别来自认知、感知、社会、语言和运动发展、人机交互、视觉分析、计算机科学、电子研究、认知科学和人为因素等领域。总的来说，受邀的研究人员在探索丰富的行为数据问题的不同方面都有经验，例如执行大规模数据可视化，创新数据分析，集成多个数据流，执行共享数据集的托管，以及创建eResearch社区和数据管理工具。研讨会的成果将有助于提高行为科学的质量。首先，研讨会的发现将对OpenSHAPA工具的进一步开发产生直接影响，在这个工具中，开发成果将在一个迅速发展的用户社区中共享。可能的方向是改变体系结构，为扩展数据管理和数据共享能力做准备，建立与现有软件的链接，为用户创建脚本库，以便以标准化的方式管理数据，创建基于web的用户指南和最佳实践，扩展用户论坛，以及提供有效的技术支持。研究社区成员可以自由地采用OpenSHAPA，扩展他们目前对它的使用，或者在它和其他开源工具之间建立桥梁，并将新用户带入现有用户和开发人员的社区。第二，更丰富的数据分析结果应该支持更丰富的理论见解。更好的数据管理实践将支持更可靠和可复制的研究，并将更好地保存数据，以便将来在实验室内部和跨实验室使用。开放数据共享实践的社区将通过允许研究人员检查彼此的数据集和分析，从而减少令人困惑的复制失败，产生新的假设，并向学生展示任务和发现的原始片段，从而提高研究和教学的透明度和效率。

Researchers now have access to richer and more detailed behavioral data than ever before. For example, when studying how children learn to walk, researchers can collect eye-tracking data from miniature head-mounted cameras recording the infant's eye movements and field of view, making it possible to see exactly where the child looks while navigating through the environment. Simultaneously, researchers can collect high-speed motion-tracking data detailing the trajectories of the child's limb movements and video data about the child's path relative to caregivers and obstacles, interactions with people, objects, and surfaces, and affective responses while walking, falling, and interacting. Despite the widespread availability of video and other recording technologies, behavioral researchers typically settle for analyzing only one variable in one stream of data, rather than seeking relations among multiple variables across multiple data streams. Powerful data analysis tools and sophisticated data management practices are needed to integrate different kinds of data and relate them to each other tools and practices that few researchers have. In addition, researchers usually work in isolation, seldom sharing data that might illuminate others' research. Without richer analyses and data sharing, theoretical progress in developmental psychology and other fields of behavioral science is hampered. The purpose of this workshop is to delve into the conceptual, technical, and management issues that, when resolved, will allow researchers to perform richer analyses across large, shared, data sets. The workshop will focus in part on the future development of an emerging open-source software tool, OpenSHAPA, and will explore how OpenSHAPA might be extended to encompass new data exploration and visualization tools and promote data management and data sharing. Twenty-two researchers will participate in the workshop, representing the fields of cognitive, perceptual, social, language, and motor development, human-computer interaction, visual analytics, computer science, eResearch, cognitive science, and human factors. Collectively, the invited researchers have experience with different aspects of the problem of exploring rich behavioral data, such as performing massive data visualization, innovative data analyses, integrating multiple data streams, performing custodianship of shared data sets, and creating eResearch communities and data management tools.The outcomes from the workshop will help to improve the quality of behavioral science. First, findings from the workshop will have an immediate impact on further development of the OpenSHAPA tool, where development is shared across a burgeoning community of users. Possible directions are changes to the architecture to prepare for expansion of data management and data sharing capabilities, building links to existing software, creating libraries of scripts for users to manage data in standardized ways, creating web-based user guides and best practices, expanding user forums, and providing efficient technical support. Research community members can freely adopt OpenSHAPA, expand their current use of it, or build bridges between it and other open source tools, and will bring new users into the community of current users and developers. Second, the richer data analysis that results should support richer theoretical insights. Better data management practices will support more reliable and replicable research, and will better preserve data for future use within and across laboratories. A community of open data sharing practices will lead to greater transparency and efficiency in research and teaching by allowing researchers to inspect each other's data sets and analyses, thereby reducing puzzling failures to replicate, generating new hypotheses, and exposing students to original footage of tasks and findings.