This post is about a little dashboard idea that aims at helping policy makers discover research relevant to the ‘zero hunger‘ topic, one of the themes of the Sustainable Development Goals program.

The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. They recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth – all while tackling climate change and working to preserve our oceans and forests.

For more background about this project, see also its wikipedia page https://en.wikipedia.org/wiki/Sustainable_Development_Goals

Springer Nature is among the many organizations who are taking an active role in developing scenarios and solutions to tackle these global challenges. A couple of months ago Springer Nature organized a hack day which brought together people with different backgrounds and expertise in order to come up with ideas and prototypes that could lead to further research. In particular, the focus of the hack day was on the ‘zero hunger’ theme.

The team I was working with developed a concept around the idea of an easy-to-use dashboard-like tool which could be used by busy policy makers in order to quickly gather infos about researchers or institutions they’d want to consult with.

In order to make this idea more tangible I ended up building a little prototype, which allows to scan scholarly documents in order to pull out information (potentially) related to the ‘zero hunger’ topic and sub-topics, essentially following the keywords-structure specified in the Sustainable Development Goals document.

The prototype is available here: http://hacks2019.michelepasin.org/zerohunger/

 

 

 

This experiment also gave me an opportunity to learn about the Dimensions.ai API, a domain specific language (DSL) which allows to query the Dimensions database, a state-of-the-art scholarly platform containing  millions of linked metadata records about publications, grants, patents, clinical trials and policy documents (for more background about Dimensions, see this blog post and this white paper).

The API itself is being a paywall, but if you are curious about it, the documentation is available online.

It’s a fantastic resource, intuitive and easy to use yet powerful and features-rich, so I am pretty sure I’ll be writing more about it.

Stay tuned for more!