Publication

Authored by: Njoku Ola Ama and Francis Nathan Okurut Abstract: This study explores the adoption, by micro, small, and medium-sized enterprises (MSMEs) in Botswana, of...

Published by Open AIRPublication Date: 2014Download: Optimising Benefits from Publicly Funded Research (323) This 2014 Briefing Note highlights Open AIR research findings on apparent disconnects between...

ACA2K network membersPublication Date: 2009 Download : WIPO Briefing Paper 3: Research Findings from Africa Relevant to WIPO SCCR 19 This December 2009 Briefing Paper by...

Jeremy de Beer, Chris Armstrong, Chidi Oguamanam et Tobias Schonwetter Date de publication : septembre 2017 Télécharger: Chapitre 16 : La propriété intellectuelle...

This paper positions and critiques the Open African Innovation Research (Open AIR) network as a unique cross-regional PPP research platform. It examines, on empirical and theoretical perspectives, the elements of the Open AIR project, including its core driving factors relevant to the development gap associated with IP and knowledge governance in Africa.

This paper reviews the methodologies of 16 indices in innovation, information and communication technologies, economic environment, governance, and development.

Authored by: Chidi Oguamanam. Over the years, the idea of traditional knowledge has progressively unraveled as a traction point for complex issue linkages between intellectual property and, for example, genetic resources, biodiversity conservation, the rights of Indigenous Peoples and Local Communities (ILCs), food, nutritional and environmental security.

First/Third World COVID-19 Vaccination Divide The COVID-19 pandemic has continued to disrupt Open AIR activities. Certain of our research activities have had to be postponed,...

نغم الحسامي ونجلاء رزق Authored by: Nagham El Houssamy and Dr. Nagla Rizk نسعى من خلال ورقة العمل هذه لتوضيح مخرجات...

Agricultural data is globally recognized for its importance in addressing food insecurity. We propose a ‘data commons’, formed through a licensing model that allows farmers to benefit from the datasets to which they contribute.