The Oslo Manual describes how statistical data on innovation should be collected and interpreted. It is the de facto guideline for statistical offices and other organisations working with innovation data around the world. The Manual is closely related to the Community Innovation Survey that is administrated by Eurostat. It is run every two years by all EU statistical offices but is also used by many statistical offices outside Europe. The Oslo Manual is traditionally a co-production between OECD and Eurostat.

Since 2016, Dialogic has assisted Eurostat with the revision of the current (2005) 3rd edition from the Oslo Manual. As part of this project Dialogic, together with its partners Devstat (Valencia) and the Higher School of Economics (Moscow), published five working papers:

WP1 – DevStat – New methods for the quantitative measurement of innovation intensity
WP2 – HSE – Featuring open innovation implications for measurement
WP3 – Dialogic – Improving the measurements of innovation outcome
WP4 – Dialogic – Globalisation and MNEs. Implications for innovation measurement
WP5 – HSE – Measuring innovation in the business sector: beyond manufacturing and services

In the context of the project, Pim den Hertog also gave a presentation on the decennial Blue Sky conference in Ghent: Improving the measurements of innovation outcome.

The pending (2018) revision of the CIS was meant to built on the revised version of the Oslo Manual. Due to delay in the revision process of the Oslo Manual in 2017 Eurostat decided to decouple the CIS revision from the Oslo Manual Revision. Dialogic’s assignment was now refined as drafted an Implementation Guide for CIS2018. The primary aim of this document is to illustrative how CIS data can be used, or is already being used, in the practice of innovation research and policy analysis.

The full report can be found here: Putting the CIS2018 into context­

Robbin te Velde and José Cervera (Devstat) also gave presentations on respectively linking CIS data with other data sources and measuring innovation intensity.
A separate paper on the application of machine learning to profile enterprises has also been written by Robbin te Velde.