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Towards a Data Science of Startups :

Datasets and methods

The rapid emergence of large datasets of startups, among which notably Crunchbase, Dealroom or Pitchbook, has fostered renewed academic investigations with regard to entrepreneurship and startups, mostly using quantitative techniques from or inspired by data science. These new datasets have indeed made widely available detailed information about close to a million startups from all over the world, ranging from the identity of founders to information concerning the startups’ funding rounds or else textual descriptions of their activities. Associated with the novel techniques from data science that have recently started to be used by the managerial, economic and social sciences, these datasets have therefore allowed researchers to address various relevant issues such as: automatically identifying competitors, predicting which startups will raise funds and analyzing the determinants of their success in doing so, analyzing the evolution of entrepreneurial ecosystems, etc.


The ambition of this track is to help foster the emergence of a community of researchers investigating startups using these novel techniques and associated datasets. We welcome contributions exploiting all kinds of methodologies, from econometrics to machine learning through network theory, etc. 


  • Jean-Michel Dalle  
  • Matthijs den Besten  

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