Panel Study on Transparency and Reproducibility for NCSES Statistics and a Workshop on the Implications of Convergence for Measuring the S&E Workforce and the S&E Enterpris

国家科学与工程统计中心是全国领先的美国科学与工程统计数据提供商(S与工程)，包括研究与开发、S与工程工作人员、科学、技术、工程与数学(STEM)教育的条件和进展以及美国在这些领域的竞争力的数据。NCSES进行了大量调查以衡量这些特征；然而，社会和技术变化，如调查答复率下降、预算和人力资源有限，以及来自非政府来源的替代数据来源，正在使调查的进行成本更高，用处更小。美国国家科学院、工程院和医学院的国家统计委员会(CNSTAT)将为NCSES开展两个项目，以提高其统计方案的相关性和有用性，并帮助其应对不断变化的世界。第一个项目的重点是更好地衡量科学工作。由于当今科学和社会面临的许多最紧迫的问题--从细胞研究和纳米技术到环境研究等领域--都是巨大而复杂的问题，本质上是跨学科的，它们需要使用不同学科的多种方法和途径进行合作研究。这被称为趋同研究，NCSES能够准确地衡量趋同研究，以便向科学政策提供关于这项研究的模式及其如何与科学进步相联系的信息，这一点至关重要。然而，衡量这项工作是困难的，因为没有一个被接受的单一定义。为了帮助NCSES能够收集关于科学培训和研究的融合程度的有用数据，国家科技统计中心将召开一次研讨会，以确定和讨论在衡量融合方面的问题及其对科学和工程(S)劳动力和更广泛的S企业的影响。第二个项目的重点是扩大NCSES用于其统计计划的数据类型，潜在地纳入行政记录和其他数据集的使用，以产生对科学政策和研究界相关、及时和高质量的统计数据。然而，使用这些数据来源需要在来源、数据质量和使用的方法方面具有透明度，以便用户能够评估所产生的统计数据是否符合其预期目的，并与其他机构和国家的结果进行比较。2017年6月，国家科学基金会支持了国家统计委员会的一个讲习班，该讲习班审查了联邦统计机构数据产品的透明度和可重复性的各个方面，显然目前的透明度和可重复性的方法可以改进。其他国家的发言强调了各种工具的开发，这些工具可能有助于提高美国的透明度，支持更多地使用和再利用美国的统计数据。CNSTAT将进行一项共识研究，对这些工具和NCSES的潜在好处进行彻底评估，并就制定NCSES统计数据的透明度和重复性标准提出建议，以帮助数据用户并最大化NCSES统计数据的公共利益。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The National Center for Science and Engineering Statistics (NCSES) is the nation's leading provider of statistical data on U.S. Science & Engineering (S&E), including data on research and development, the S&E workforce, the condition and progress of Science, Technology, Engineering, and Mathematics (STEM) education, and U.S. competitiveness in these areas. NCSES conducts a large number of surveys to measure these characteristics; however, social and technological changes, such as declining survey response rates, constrained budget and staff resources, and alternative sources of data from non-government sources, are making surveys more expensive to conduct and less useful. The Committee on National Statistics (CNSTAT) of the National Academies of Sciences, Engineering, and Medicine will conduct two projects for NCSES to improve the relevance and usefulness of its statistical programs and help it address a changing world. The first project is focused on better measurement of scientific work. Because many of the most pressing questions confronting science and society today -- in fields ranging from cell research and nanotechnology to environmental studies and beyond -- are large and complex problems which are inherently interdisciplinary in character, they require collaborative research using multiple methods and approaches from different disciplines. This has been referred to as convergent research, and it is vital that NCSES be able to accurately measure convergent research to inform science policy about the patterns of this research and how it links to scientific advances. However, measurement of this work is difficult because there is no single accepted definition. To assist NCSES in being able to gather useful data on the extent of convergence in scientific training and research, CNSTAT will convene a workshop to identify and discuss issues in measuring convergence and its implications for the science and engineering (S&E) workforce and the S&E enterprise more broadly. The second project is focused on expanding the kinds of data NCSES uses for its statistical program, to potentially incorporate the use of administrative records and other data sets to produce statistics that are relevant, timely, and of high quality for the science policy and research communities. However, using these data sources requires transparency in the sources, quality of data, and methods used so users are able to assess whether the statistics produced are fit for their intended purposes, and for comparison with results from other agencies and countries. In June, 2017, NSF supported a CNSTAT workshop that examined various aspects of transparency and reproducibility of data products from federal statistical agencies, and it was clear that the current approaches to transparency and reproducibility could be improved. Presentations from other countries highlighted the development of a variety of tools, which could be useful for achieving greater transparency and supporting greater use and reuse of statistical data in the United States. CNSTAT will conduct a consensus study that will provide a thorough assessment of these tools and potential benefits to NCSES, and provide recommendations on developing standards for transparency and reproducibility of NCSES statistics to help data users and maximize the public benefits from NCSES statistical data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.