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      Indicators on firm level innovation activities from web scraped data

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          Abstract

          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.

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          Most cited references11

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          Research, Innovation And Productivi[Ty: An Econometric Analysis At The Firm Level

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            Measuring innovative performance: is there an advantage in using multiple indicators?

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              Microsoft Academic Graph: When experts are not enough

              An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed.
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                Author and article information

                Contributors
                @ArhoSuominen @ArhoSuominen
                @Arash_Hajikhani
                @SchubertTorbenScott
                @cunningham_sw
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                06 May 2022
                June 2022
                06 May 2022
                : 42
                : 108246
                Affiliations
                [a ]Quantitative Science and Technology Studies, VTT Technical Research Centre of Finland, Tekniikantie 21, 02044 Espoo, Finland
                [b ]Industrial Engineering and Management, Tampere University, Korkeakoulunkatu 8. PL 541, 33014 Tampereen Yliopisto, Finland
                [c ]Public Policy and Management Institute, Gedimino pr. 50 LT-01110 Vilnius, Lithuania
                [d ]Fraunhofer Institute for Systems and Innovation Research ISI, Breslauer Str. 48, 76135 Karlsruhe, Germany
                [e ]CIRCLE - Centre for Innovation Research, Sölvegatan 16, 22100 Lund, Sweden
                [f ]UNU-MERIT UM – United Nations University Maastricht Economic and Social Research Insitute on Innovation and Technology, Maastricht University, Boschstraat 24, 6211 Maastricht, the Netherlands
                [g ]Section Economics of Technology and Innovation, Department of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, the Netherlands
                [h ]School of Government and Public Policy, University of Strathclyde, Glasgow G1 1XQ, United Kingdom
                [i ]LUT University, School of Business and Management (LBM), Yliopistonkatu 34, 53850 Lappeenranta, Finland
                Author notes
                Article
                S2352-3409(22)00448-6 108246
                10.1016/j.dib.2022.108246
                9120249
                a0f1495d-544f-4599-a3a6-5b1ad6048898
                © 2022 Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 3 March 2022
                : 19 April 2022
                : 2 May 2022
                Categories
                Data Article

                big data,web scraped data,text data,innovation,firm-level data

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