Survey data on growth companies' and control group's perceptions on innovative behavior.

The data set contains data collected using a paper format and an online survey. The data includes a sample of 120 Finnish companies. The survey, structured into six sections focuses on the firms' growth outlooks and the underlying management practices and principles. The data reports the perceptions of top managers on growth, innovativeness, and the ability for renewal. The majority of the data comprises Likert scale questions on respondents' agreement or disagreement on innovation behaviors. This is complemented with micro-level data based indicator if a company is a growth company, the companies' growth expectations as percentage, and if COVID-19 has had business impacts. The data description is associated with a codebook and survey form. The data can be used to better understand growth and non-growth companies behaviours and outcomes. The associated material offers an opportunity to replicate the study in different regions.

Indicators on firm level innovation activities from web scraped data.

This article presents data on companies' innovative behavior measured at the firm-level based on web scraped firm-level data derived from medium-high and high-technology companies in the European Union and the United Kingdom. The data are retrieved from individual company websites and contains in total data on 96,921 companies. The data provide information on various aspects of innovation, most significantly the research and development orientation of the company at the company and product level, the company's collaborative activities, company's products, and use of standards. In addition to the web scraped data, the dataset aggregates a variety firm-level indicators including patenting activities. In total, the dataset includes 21 variables with unique identifiers which enables connecting to other databases such as financial data.

Connecting firm's web scraped textual content to body of science: Utilizing microsoft academic graph hierarchical topic modeling.

This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method illustrates the benefit of Natural Language Processing (NLP) in creating links between established economic classification systems with novel and agile constructs that new data sources enable. Therefore, we experimented on the European classification of economic activities (known as NACE) on sectoral and company levels. We established a connection with Microsoft Academic Graph hierarchical topic modeling based on companies' website content. Central to the operationalization of our method are a web scraping process, NLP and a data transformation/linkage procedure. The method contains three main steps: data source identification, raw data retrieval, and data preparation and transformation. These steps are applied to two distinct data sources.

Dataset on researcher's mobility by utilising bibliometric data: Case of Finland-based scholars for the duration of 2008-2018.

Scientific discoveries are the result of global collaboration and often the multidisciplinary nature of collaborations. A core element of these successful collaborations will materialise through a researcher's mobility in location and disciplinary focus. Researchers experience numerous opportunities to practice locational mobility throughout their careers as well as by conducting multidisciplinary research. Both changes have short- and long-term impacts on individual researchers and science, technology, and innovation systems that have an immediate interest for the public and private research and development funding mechanisms. With the advancement in data science tools and increasing computational capacities, we can use bibliometric data for calculating a researcher's mobility on location and a disciplinary focus over time. We looked at Finland as a case, and by incorporating analytical procedures, the processed data is capable of delivering insights on researcher mobility between cities over time as well as disciplinary change over time. This dataset can reveal hidden dynamics in the scholar's career progress. If combined with funding information and mission-oriented policies, the dataset can evaluate the long-lasting effect of instruments in mobilising researchers, steering research agendas, and consequently the resulting impacts.

The global inventor gap: distribution and equality of world-wide inventive effort, 1990-2010.

Applying distance-to-frontier analysis, we have used 2.9 million patents and population data to assess whether the relative capacity of world countries and major regions to create new knowledge and technology has become globally more equal or less equal between 1990 and 2010. We show with the Gini coefficient that the global distribution of inventors has become more equal between major countries and regions. However, this trend has been largely due to the improved performance of only two major countries, China and India. The worst performing regions, totalling a population of almost 2 billion, are actually falling behind. Our results suggest that substantial parts of the global population have fallen further behind countries at the global frontier in their ability to create new knowledge and inventions, and that the catch-up among the least developed and middle-income countries is highly uneven, prompting questions about the nature and future of the global knowledge economy.