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Agrimetrics is building a scalable computational platform for data integration and modelling, from a range of sources to enable smarter analytics, including mathematical and statistical modelling and visualisation, and to remove barriers to entry for businesses wishing to develop new data products and services.

Agrimetrics will also support businesses in developing their data science, data modelling and analytics capabilities by offering expertise, easier access to relevant data sets, data processing capability, consultancy, training and secondments to upskill the agri-food workforce.

A versatile data analysis platform

The Agrimetrics platform will use the latest technologies in data representation to aggregate and compartmentalise relevant public and private data. The integration of these data, according to stakeholders’ needs, will provide better insights and evidence base for more informed decision making.

A Semantic Web for the Agriculture-Food System

The Agrimetrics platform will integrate data using Semantic Web technology to facilitate the representation of information, draw inferences and derive novel insights.

To achieve this, it is important that the data are available in a standard format, reachable and manageable by Semantic Web tools. Using the Semantic web to link data is a growing trend and is already used in the pharmaceutical and medical community. Ultimately, this creates opportunities to integrate data across domains relevant to food and farming, such as health.

In this framework, meta-data is much more comprehensive and describes as many relationships between data as possible, which makes their role (and volume) significantly larger than in traditional databases, where they mainly describe what is recorded.

The other important key feature is that each individual pieces of data are given a Uniform Resource Indicator (URI), which enables it to be linked within web pages. The creation of this web of data makes relationships among data available to be explored and queried – this interrelation of datasets on the web is also referred to as Linked Data. By structuring data in this way, a query can extend to an external (and possibly unknown) data set through a web of data, which include those encoded in the layers of meta-data sitting above the real data.

This means that the different interconnected data of a complex system such as the agri-food system can be better managed and understood using the semantic web, by enabling their query and integration in new ways that could not otherwise be imagined, to generate valuable insights and new knowledge that can then be analysed using data sciences and analytics to support better informed and more effective decision-making.