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制度化，数据科学在全国和全球范围内得到了惊人的迅速普及。在科学、州和行业中，新的数据收集和分析形式有望解决我们这个时代的重大社会和科学问题：从应对自然灾害到监测环境，再到开发将给行业带来革命性变化的基本技术见解。这一切都不会通过法令来实现？开发适当的数据分析必须伴随着研究、政策和行业之间的新的组织调整。大数据中心(BDHubs)计划是分析这些路线的性质的关键站点，以期基本了解地形变化时的利害关系。所有长期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机构灵活性：适应监管、政策和资金环境变化的战略、技术和组织创新。我们可以预期，但不能准确地预测，今天的社会技术、科学和制度生态--S的研究和科学基础设施--将继续发生变化。该项目将调查BDHub及其伙伴机构正在进行的活动及其未来的新计划，并将这些与发展研究基础设施的长期历史(50年)联系起来，以了解我们预计BDHub随着时间的推移会遇到哪些变化(科学、技术和机构)，以及它们在面对科学、信息技术和机构环境的变化时采取了什么战略。当代科学面临的许多挑战，如环境研究或慢性病管理，都需要长期研究和支持基础设施。该项目将直接为NSF和其他努力做出贡献，以建立跨科学、行业和政府的更开放、更有效和更可持续的知识社区。提高对研究基础设施长期轨迹的理解将导致项目负责人、参与者、工具制造商和资助者做出更明智、更可持续的投资和设计选择。许多其他科学机构正在资助这样的交叉项目。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，这项研究将对数据科学的大规模和长期努力的发展有一个总体的组织理解，并绘制现代数据科学的机构图景，特别是侧重于基础设施建设、政策制定和社区形成的活动。此外，这项研究将有助于加深对数据科学兴起的理解。在美国，无论是作为一种机构运动，还是作为一种技术研究形式，都特别注重建立一个框架，以评估和理解在大数据工具、技术和方法方面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.