Исследовательские Данные

The continuing explosion of digital content (data revolution)  within the global research landscape and the increased interest in sharing and curating research data has triggered the emergence of research data management (RDM). RDM has already earned recognition as an autonomous discipline and as a service area supporting researchers in different organizations worldwide.

The ‘European Open Science Cloud’ report (2016) earmarks for active progression in alignment and sharing of trusted and FAIR (Findable, Accessible, Interoperable and Re-usable data) research data in all research domains globally. AIMS community is campaigning for encouraging (agricultural) research community to publish, manage, share and reuse research data according to FAIR principles and in a responsible manner, considering that this brings significant benefits to researchers.

To approach the research data complexity and ensure higher quality data for sharing and, therefore, research excellence, it is crucial to continue building and improving cross-disciplinary data bridges. This ongoing challenge requires:

  • enhancing and expanding cross-domain research collaborations,
  • building innovative research capacity in the long run, and

  • fostering awareness about the available technologies, standards, tools, methods, recommendations and services in support of the global research data infrastructures.

What does Research Data and Research Data Management mean? 

(Image credit: Data4lifesciences

The European Commission defines research data as data available in digital form and “underlying publications, curated data and/or raw data […] Users can normally access, mine, exploit, reproduce and disseminate openly accessible research data free of charge” (Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020).

Research data management refers to “the storage, access and preservation of data produced from a given investigation. Data management practices cover the entire lifecycle of the data, from planning the investigation to conducting it, and from backing up data as it is created and used to long term preservation of data deliverables after the research investigation has concluded” (CASRAI).  “Good research data management is not a goal in itself, but rather the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse” (Guidelines on FAIR Data Management in Horizon 2020).

Opening, sharing and reusing research data greatly enhance their value in knowledge exchange that leads to new research ideas and outputs. To enable open exchange of data in a structured and aligned manner, “the notions of ‘building blocks’ of common data infrastructures and building specific ‘data bridges’ are becoming accepted metaphors for approaching the data complexity and enable data sharing” (Research Data Alliance, RDA). Agricultural investment, innovation and policy strategies, rural development, agricultural production, food security, nutrition, natural resources management, regional food systems - all benefit from the efforts to make agricultural data, information and knowledge more accessible, sharable and reusable for the long term.

AIMS co-chairs the RDA Interest Group on Agricultural Data  (IGAD) and is part of the Global Open Data for Agriculture and Nutrition (GODAN) community of practice to support and provide more visibility to initiatives on open research data and open models in the agri-food sector.  

A number of groups under the IGAD and GODAN umbrella are working to align partner activities related to open global agriculture data. In particular, these groups produce and disseminate good practices about and precise proposals for (solutions in specific areas) data management planning, including data publishing and sharing, data interoperability and other related aspects in the agriculture and nutrition sectors. 

Click on the links below to learn more about several RDA-IGAD and GODAN Working Groups (WGs) engaged in developing and disseminating methods, policies and good practices for opening, connecting and sharing agricultural research data: