Indicators on firm level innovation activities from web scraped data

Sajad Ashouri; Arho Suominen; Arash Hajikhani; Lukas Pukelis; Torben Schubert; Serdar Türkeli; Cees Van Beers; Scott Cunningham

doi:10.1016/j.dib.2022.108246

Indicators on firm level innovation activities from web scraped data

Sajad Ashouri
, Arho Suominen^*
, Arash Hajikhani
, Lukas Pukelis
, Torben Schubert
, Serdar Türkeli
, Cees Van Beers
, Scott Cunningham

^*Corresponding author for this work

Tampere University
Lappeenranta-Lahti University of Technology LUT
Public Policy and Management Institute (PPMI)
Fraunhofer-Institut für System- und Innovationsforschung ISI
Lund University
Maastricht University
Delft University of Technology
University of Strathclyde

Research output: Contribution to journal › Article › Scientific › peer-review

11 Citations (Scopus)

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.

Original language	English
Article number	108246
Journal	Data in Brief
Volume	42
DOIs	https://doi.org/10.1016/j.dib.2022.108246
Publication status	Published - Jun 2022
MoE publication type	A1 Journal article-refereed

Funding

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 870822.

Keywords

Big data
Firm-level data
Innovation
Text data
Web scraped data

Access to Document

10.1016/j.dib.2022.108246Licence: CC BY

Cite this

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title = "Indicators on firm level innovation activities from web scraped data",

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.",

keywords = "Big data, Firm-level data, Innovation, Text data, Web scraped data",

author = "Sajad Ashouri and Arho Suominen and Arash Hajikhani and Lukas Pukelis and Torben Schubert and Serdar T{\"u}rkeli and \{Van Beers\}, Cees and Scott Cunningham",

note = "{\textcopyright} 2022 Published by Elsevier Inc.",

year = "2022",

month = jun,

doi = "10.1016/j.dib.2022.108246",

language = "English",

volume = "42",

journal = "Data in Brief",

issn = "2352-3409",

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TY - JOUR

T1 - Indicators on firm level innovation activities from web scraped data

AU - Ashouri, Sajad

AU - Suominen, Arho

AU - Hajikhani, Arash

AU - Pukelis, Lukas

AU - Schubert, Torben

AU - Türkeli, Serdar

AU - Van Beers, Cees

AU - Cunningham, Scott

PY - 2022/6

Y1 - 2022/6

N2 - 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.

AB - 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.

KW - Big data

KW - Firm-level data

KW - Innovation

KW - Text data

KW - Web scraped data

UR - https://www.scopus.com/pages/publications/85130323992

U2 - 10.1016/j.dib.2022.108246

DO - 10.1016/j.dib.2022.108246

M3 - Article

C2 - 35599825

AN - SCOPUS:85130323992

SN - 2352-3409

VL - 42

JO - Data in Brief

JF - Data in Brief

M1 - 108246

ER -

Indicators on firm level innovation activities from web scraped data

Abstract

Funding

Keywords

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BIGPROD: Addressing productivity paradox with big data: implications to policy making

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Indicators on firm level innovation activities from web scraped data

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

BIGPROD: Addressing productivity paradox with big data: implications to policy making

Cite this