EAGER: Collaborative Research: Scientific Collaboration in Time

该项目探索了一种创新的跨学科方法，用于研究科学合作的动态。在过去十年中，科学政策越来越关注知识共享、开放和协作的好处，这一政策主题促使NSF和其他资助机构最近在网络基础设施开发和部署方面进行了大量投资。然而，这些投资的成功取决于对可能影响甚至阻碍采用网络基础设施的特定领域做法和文化的关注。这一探索性项目旨在推进一种新的跨学科方法，将民族志实地研究与大型出版网络分析相结合，以加强理解科学合作领域差异的经验基础。 在方法上，该项目提供了一个创新的整合定量网络分析的大型出版网络与定性民族志领域的研究。从理论上和实质上，它说明了多层次的时间动态，形状，有时挫败，在科学合作和创新的努力。该项目开发的方法支持科学界的实地比较，填补了科学计量学、创新研究和科学政策研究能力的关键空白，这些研究能力往往在宏观尺度上分裂。（国家、学科、期刊）或微观尺度（个人，具体研究地点）分析，忽视了对科学知识生产的形式和结果具有决定性作用的中尺度动态。具体而言，本计画探讨在网路民族志方法中加入时间维度，以研究和比较跨研究领域科学合作的时间维度。测试案例由两个研究领域（生态学和物理与化学之间的边界）提供，这两个研究领域表现出时间节奏的显着变化，这些变化是研究组内以及该组与其合作者之间的短期和长期合作结构的基础。 添加时间性的分析增加了出版网络的集体结构的分辨率，以支持民族志领域网站的战略抽样，使研究的出现和演变的团队，亚社区，和外地的合作结构和检测特定领域的历史趋势，并支持搜索的合作活动的时间和节奏的领域差异。EAGER项目旨在探索和测试网络分析方法的适用性，以便在将其部署到更大规模的实证研究之前，在各种规模上捕捉科学合作动态。通过加强经验基础，为科学和创新政策提供信息，并指导投资决策，这一探索性项目为科学界协作动态的比较研究开发了一种具有广泛再利用潜力的方法。当政策和社会技术干预措施（如网络基础设施、数据共享任务和新的激励计划）的潜在效益和影响，并通过改进量化这些投资影响的指标，补充现有投资并为这些特定领域的未来投资提供信息时，了解协作实践的特定领域动态、紧张关系和挑战及其随时间的演变至关重要。

This project explores an innovative interdisciplinary approach for studying the dynamics of scientific collaboration. Over the last decade, science policy has increasingly focused on the benefits of knowledge sharing, openness, and collaboration, a policy theme that has motivated a number of large recent investments by the NSF and other funding agencies in cyberinfrastructure development and deployment. The success of these investments however depends on attention to field-specific practices and cultures that may influence or even block adoption of cyberinfrastructure. This exploratory project sets out to advance a new interdisciplinary methodology that integrates ethnographic field studies with the analysis of large-scale publication networks to strengthen the empirical basis for understanding field differences in scientific collaboration. Methodologically, this project offers an innovative integration of quantitative network analysis of large publication networks with qualitative ethnographic field studies. Theoretically and substantively, it illustrates on the multi-level temporal dynamics that shape, and sometimes frustrate, efforts at scientific collaboration and innovation. The methodology developed in this project supports field-level comparisons of scientific communities and fills a critical gap in the research capabilities of scientometrics, innovation studies, and science policy, which are too often split between macro-scale (countries, disciplines, journals) or micro-scale (individuals, specific research localities) analyses, neglecting meso-scale dynamics that are formative to the shape and outcome of scientific knowledge production within and across fields.Specifically, this project investigates the addition of a temporal dimension to a network ethnographic approach to study and compare temporal dimensions of scientific collaboration across research fields. The test cases are provided by two research fields (in ecology and at the boundary between physics and chemistry) that exhibit significant variation in the temporal rhythms that underlie the structure of short- and long-term collaborations within a research group and between this group and its collaborators. Adding temporality to the analysis increases the resolution of collective structures in publication networks to support the strategic sampling of ethnographic field sites; enables the study of the emergence and evolution of collaborative structures at the team, sub-community, and field level and the detection of field-specific historical trends; and supports the search for field differences in the timing and rhythms of collaborative activities. This EAGER projects serves to explore and test the suitability of a network-analytic approach to capture scientific collaboration dynamics at various scales before deploying it in larger-scale empirical studies.Broader Impacts: Through strengthening the empirical base to inform science and innovation policy and to guide investment decisions, this exploratory project develops a methodological approach with a wide re-use potential for the comparative study of collaboration dynamics in scientific communities. Understanding the field-specific dynamics, tensions, and challenges of collaboration practices and their evolution over time is critical when the potential benefit and impact of policy and socio-technical interventions (such as cyberinfrastructure, data sharing mandates, and new incentive schemes), and, by improving metrics to quantify the impacts of these investments, complements existing investments and informs future investments in these specific areas.