Growing attention has been devoted to innovation and the commercialisation of innovation, both in academia as well as in practice. Many individuals and startups have taken an approach, based on open innovation, crowdsourcing and co-creation principles, to reduce cost and improve productivity by taking advantage of distributed knowledge, talent, and resources on the Web. On the Web they can find collaborators and partners, co-create ideas and prototypes, utilize the wisdom of the crowd to assess the value of the project idea and/or prototype, share and find business and technical information, knowledge on start-up related topics and online tools, as well as access to crowd capital.
A large amount of datasets and analytics have been derived from the aforementioned activities, which can be utilised to study innovation on the Web from varying perspectives. In this project we want to explore the potential of data sources that are related to innovation on the Web. With access to a selection of financial, governmental, demographic datasets, you are expected to work in groups to :
- Identify questions regarding technological, social, economical, political, legal aspects of innovation on the Web, or tackle open challenges and issues of innovation on the Web. Your argument should be backed by data and analytics.
- Implement your analytics as proof-of-concept Web Observatory applications. Your application may take a selection of (two or more) datasets to provide non-trivial insights, and should be made publicly available on the Web.
2 x Presentations
- Initial stating the problem and what they intend to do
- Final, including their findings, wider implications, and then a walk through of their application and visualisation
- Code on Github or similar platforms
- Working via the Web Observatory API
- Document your application to enable reproduction
- Applications registered to the Web Observatory with the “innovation on the web” keyword for easy searching
Trends of investment on innovation of certain areas and implications.
Metrics to measure and/or predicate the performance of certain innovation activities, and evaluation of the metrics.
Surveys and statistics of innovation on the Web which can be published as high quality innovation datasets.
Teams are encouraged to try and find a balance of technical computer science (e.g. programming, database experience) and social science (e.g. economics, startup experience) skill sets. Recommend skills and knowledge include but are not limited to:
- Database, data integration skills e.g. MongoDB, SQL, ETL.
- Data analysis
- Social science, financial and economics knowledge.
- Modeling open questions so they can be explained by data.
- Storytelling and presentation skills.
- Design and visualisation skills.