Data Management in AGRO
We generate data through research projects and advisory activities, and we also wish to incorporate these data into our educational programmes. The volume of data we produce has increased dramatically, and so have the requirements governing how data are managed, shared, stored and ultimately archived.
At the same time, we aim to strengthen collaboration across projects and locations, particularly where data generated in one project may be useful for others. Achieving this requires a more systematic approach to data management, which has proven to be a substantial undertaking. We work according to the principles of FAIR data management, meaning that data should be Findable, Accessible, Interoperable and Reusable. This is a significant challenge for an institute operating across many different disciplines, generating a wide variety of data types, and where individual researchers often depend heavily on - and understandably value - their own datasets. We are also facing increasing requirements from funding bodies to make research data publicly available. In addition, we have experienced difficulties in locating data generated by former colleagues.
Although we have already begun this work, we are not yet at our destination. It is a large and exciting task that we are also obliged to address. Fortunately, AGRO already has several platforms and databases where data fully or partially comply with the FAIR principles. These examples should be highlighted so others can see the benefits of well-organised data management. One such example is theSeed Production Database, established in 1999, which contains metadata and results from research on grass and clover seed production. The database enables results to be analysed across varieties, cultivation strategies and years, while also making data readily available for reuse by others.
High-Performance Computing (HPC) and statistical support are also becoming increasingly important areas within AGRO. As more researchers make use of these resources, a coordinated institutional effort is needed to ensure that our financial resources are used as efficiently as possible. Naturally, we work closely with other departments and the faculty, as AGRO is far from the only institute generating large volumes of data and working towards FAIR data management.