Implementation of FAIR data management in Agro

In most projects, Data Management Plans are now created, which are no longer a tedious duty, but more of a living document that must support an efficient, structured, and transparent way of handling and documenting FAIR data, models and methods.

Recommendations from the committee to the AGRO management on the implementation of FAIR data management include these points

1. Implement FAIR data management services and support from DeiC, AU, Faculty and AGRO to all relevant staff in AGRO e.g. ERDA, SIF and Dataverse data platforms

2. Educate Super users in ERDA/SIF and Dataverse

3. Workshops and training for all staff

4. Employ more technical staff trained in Research Data management

5. Develop specific cross project and cross section IT infrastructures enabling the management and use of big data, integration of diverse data from different platforms, use of AI and Cyper-physical and Digital twin technologies

6. Engage in data science projects using AGRO topics as use cases – together with AU engineers  

The ministry has drawn up a strategy for data management based on the FAIR principles, which has given rise to a coordination group being set up under the AU Open Science Forum, which will contribute to the rollout of the FAIR principles at the AU level. To focus the efforts, AU has formulated seven strategic goals, which focus on the necessary changes to the researchers' practice in relation to data management and the necessary measures at university level. The goals are that all researchers at AU:

1. adheres to the FAIR principles for data as well as for other research outputs such as software-codes and methods

2. Integrates data management into the research processes and thereby ensures transparency and integrity of the research results

3. Contributes to good practice and clear standards for handling both data and metadata throughout the entire research life cycle, including e.g. data collection, curation and storage both during and after projects are completed, including choice of licenses and use of persistent identifiers.

Aarhus University supports these goals by ensuring that:

1. The necessary technical infrastructure is available (see recommendation 1.

2. The necessary expertise is available and that courses and continuing education courses are offered at relevant levels (see recommendation 2 and 3).

3. The work of sharing data and other relevant output is recognized as research-relevant activities.

4. Criteria are defined for the data's value in relation to reusability and long-term storage, and a strategy is formulated for the long-term preservation of data that has not been handed over in its entirety to the National Archives.

The work in the Data management coordination group focuses on points 1-4 and B.

We are just about to employ a new Data Steward in AGRO, and that person must contribute to the roll-out of the management agreements on the recommendations that have come from the committee. ERDA and SIF (replace O-drive) are now ready for use, but Dataverse will be available this summer. We will communicate a plan for workshops and support on utilizing these platforms as soon as possible when the new Data Steward starts.