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Data used in this study are: (1) 24 hour averaged surface NO2 data for March-August for 2019 & 2020; (2) a total of about 328 AQS sites that have no data gaps; (3) a trend analysis for a subset of those AQS sites that extends back to 2005; and (4) OMI (2005-2020) and TROPOMI (2018-2020) satellite-based data for NO2 tropospheric columns. Note that OMI and TROPOMI make swath measurements from low Earth sun-synchronous polar orbit (i.e., global coverage each day at approximately 1 PM local standard time). For data associated with this paper, please contact the corresponding author Zhen Qu at zhenqu@g.harvard.edu. This dataset is not publicly accessible because: Role in this research effort as limited advisor / manuscript co-author on non-EPA and publicly available AQS datasets. It can be accessed through the following means: For data associated with this paper, contact the primary author Zhen Qu at zhenqu@g.harvard.edu. Format: N/A. This dataset is associated with the following publication: Qu, Z., D. Jacob, R. Silvern, V. Shah, P.C. Campbell, L. Valin, and L. Murray. US COVID-19 Shutdown Demonstrates Importance of Background NO2 in Inferring NOx Emissions From Satellite NO2 Observations. GEOPHYSICAL RESEARCH LETTERS. American Geophysical Union, Washington, DC, USA, 48(10): e2021GL092783, (2021).

In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to “improve the efficiency of all social systems,” “create new industries,” and “increase intellectual productivity.” For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea. Methods We used the bibliographic data of Web of Science (WoS) core collection, one of the biggest bibliographic databases from 2000 to 2018. The analysis of the visualization organizational diversity used data from 2018; studies on AI, big data, and IoT have been continuously increasing, reaching 3,133, 5,155, and 4,662 related papers in 2018, respectively. The 23 Essential Science Indicators Subject Areas in the Web of Science Core Collection were used for the article specialties. This data was generated from the "Web of Science Categories” using a conversion table (Thomson Reuters Community, 2012).