Collaborative Research: Institutionalizing the Data Sciences, a Sociotechnical Investigation of BDHubs

大数据正在改变世界，随着数据科学在大学课程、国家治理和行业战略中的制度化，它在全国和全球范围内得到了惊人的快速发展。在科学、国家和行业中，新形式的数据收集和分析使我们能够解决我们这个时代的主要社会和科学问题：从应对自然灾害到监测环境，再到发展将彻底改变工业的基本技术见解。这一切都不会通过法令实现吗？适当的数据分析的发展必须伴随着研究、政策和行业之间新的组织协调。大数据中心和辐条（BDHubs）项目是分析这些排列性质的关键站点，目的是在地形变化时获得对利害关系的基本了解。所有长期的研究和创新基础设施都必须适应这种持续的变化。这项研究将有助于理解制度灵活性：适应监管、政策和资助环境变化的战略、技术和组织创新。我们可以期待，但不能精确地预测，当今社会技术、科学和制度生态的持续变革。美国的研究和科学基础设施。该项目将调查bdhub及其合作机构正在进行的活动，他们对未来的新计划，并将这些与发展研究基础设施的悠久历史（50多年）联系起来，以了解我们可以预期bdhub随着时间的推移会遇到的变化（科学、技术和制度），以及他们在面对科学、信息技术和制度环境的转变时采取的策略。当代科学面临的许多挑战，如环境研究或慢性病管理，需要长期研究和支持基础设施。这个项目将直接为NSF和其他在科学、工业和政府间建立更加开放、有效和可持续的知识社区的努力做出贡献。提高对研究基础设施长期发展轨迹的理解，将使项目负责人、参与者、工具建设者和资助者做出更明智、更可持续的投资和设计选择。许多其他科学机构也在资助这类跨领域项目。这项研究将为科学政策和监管环境提供信息，以帮助发展可持续和富有成效的研究基础设施。为了做到这一点，本研究将对数据科学中大规模和长期努力的发展形成一个总体的组织理解，并绘制现代数据科学的制度景观，特别关注基础设施建设、政策制定和社区形成的活动。此外，这项研究将有助于发展对？数据科学的崛起？作为一种制度运动和一种技术研究形式，特别侧重于建立一个框架，以评估和理解关于大数据工具、技术和方法的大规模跨学科合作。

The world is being changed by Big Data, and data science is receiving a remarkably rapid uptake across the nation and the globe as it institutionalized in university curricula, state governance, and industry strategy. Across the sciences, state and industry, new forms of data collection and analysis hold the prospect of our being able to address the major social and scientific issues of our times: from responding to natural disasters to monitoring the environment to developing fundamental technological insights which will revolutionize industry. None of this will happen by fiat ? the development of the appropriate data analytics must be accompanied by new organizational alignments between research, policy and industry. The Big Data Hubs and Spokes (BDHubs) program is a key site for analyzing the nature of these alignments with a view to gaining a basic understanding of the stakes as the terrain shifts. All long-term research and innovation infrastructure must adapt to such ongoing changes. This research will contribute to understanding institutional flexibility: strategies, techniques and organizational innovations to adapt to changes in regulatory, policy and funding environments. We can expect but not precisely predict, continuing transformations in the sociotechnical, scientific and institutional ecologies of today?s research and scientific infrastructure. This project will investigate the ongoing activities at the BDHubs and its partner institutions, their emerging plans for the future, and tie these to the long-history of developing research infrastructures (50+ years) to understand the changes we can expect BDHubs to encounter over time (scientific, technological and institutional), and what strategies they employ in the face of transformations to the landscape of science, information technology and institutional environment. Many challenges facing contemporary science such as environmental research or chronic disease management require long-term studies and supporting infrastructures. This project will contribute directly to NSF and other efforts to build more open, effective, and sustainable knowledge communities across the sciences, industry and government. Improving understanding of the long-term trajectory of research infrastructure will lead to smarter and more sustainable investment and design choices on the part of project leaders, participants, tool builders, and funders. Many other science agencies are in the midst of funding such cross-cutting projects. This research will inform science policy and regulatory environments to help develop sustainable and productive research infrastructures. In order to do so, this study will develop a general organizational understanding of the development of large-scale and long-term endeavors in the data sciences and map the institutional landscape of modern data science with particular focus on the activities of infrastructure building, policy development and community formation. In addition, this study will help to develop an understanding of the ?rise of the data sciences? in the US both as an institutional movement and as a form of technical research, with special focus on establishing a framework for evaluating and understanding large-scale, cross-disciplinary collaboration on big data tools, techniques and methods.