Symposium on the Science of Science

越来越多的大规模数据可用于捕捉科学领域的主要活动“出版物、专利、引文、资助申请，作为其相关的元数据“，这为定量探索科学生产和奖励模式创造了前所未有的机会。该研讨会汇集了社会，计算和自然科学家，讨论科学和创新政策的科学及其改变科学实践，机构重新设计和科学政策实践的潜力。与会者包括来自各个学科的主要研究人员，国防部的项目主管，私人慈善基金会，科学出版商和进行研发和提供科学服务的公司。来自广泛学科的研究人员已经开始将科学作为一个观察站，来探索比科学机构本身更普遍和更广泛适用的社会现象。因此，工具和观点各不相同，涉及社会科学家、信息和计算机科学家、经济学家、物理学家和数学家，结果发表在读者群不重叠的地方。与标准的文献计量学研究相比，最近科学定量研究的激增具有几个明显的特点：（i）它们通常依赖于大规模的数据集来研究科学，从数十万到数百万的作者、论文和引用;以及，（ii）他们不仅评估指标，还使用模型更深入地探索推动科学的机制，从知识生产到科学和社会影响，系统地区分可预测模式和随机模式。

The increasing availability of large-scale data to capture major activities in science"publications, patents, citations, grant proposals, as their associated meta-data"has created an unprecedented opportunity to quantitatively explore the patterns of scientific production and reward. This workshop brings together social, computational and natural scientists to discuss the science of science and innovation policy and its potential to transform the practice of science, the redesign of institutions, and the practice of science policy. Participants include leading researchers from various disciplines, program directors from the Department of Defense, and private philanthropic foundations, scientific publishers and companies that conduct R&D and provide scientific services. Researchers from a wide range of disciplines have begun to use science as an observatory to probe social phenomena that are more universal and widely applicable than the institutions of science themselves. As such, the tools and perspectives vary, involving social scientists, information and computer scientists, economists, physicists and mathematicians, with results published in venues with non-overlapping readership. In contrast with standard bibliometric studies, the recent surge in quantitative studies of science is characterized by a few distinct qualities: (i) They typically rely on large-scale datasets to study science, ranging from hundreds of thousands to millions of authors, papers and citations; and, (ii) Instead of only evaluating metrics, they also use models to more deeply probe the mechanisms driving science, from knowledge production to scientific and social impact, systematically distinguishing predictable from random patterns.