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  • How is AgReFed different from other data discovery tools?

    This project is differentiated from other data initiatives by its primary focus on agriculture, its federated system architecture, the diverse sources of data, and in particular its social architecture. These aspects will ensure the benefits are achievable and sustainable, as each participant is being supported to make their data FAIR in a way that suits their needs as well as those of the community, rather than being asked to conform to a single solution.

  • What are the benefits?

    In the era of big data, finding and extracting meaning from agricultural data for decision making, discoveries and innovation is a challenge. One survey estimates that up to 80 percent of PhD and other researchers’ time in projects is dealing with discovering and reusing multiple data sources (Press, 2016).

    AgReFed enables participation and data discovery through improving the sharing and reuse of agricultural data with these benefits:

    • Adopting good data stewardship, including making data findable, accessible and re-usable by both humans and machines, avoids wasting time and money.
    • New, innovative findings become a possibility when users are able to search and combine your datasets with other datasets by parameters of interest, such as region, environmental variable, or experimental factor such as ‘crop type’.
    • Deep, integrative data-driven research for discovery and policy insights can be achieved when data can be searched and combined across sectors, such as agricultural, environmental and society.
    • Participation in agricultural data sharing, reuse and discovery can span beyond the traditional academic research setting, including across public and private sectors.
  • What constitutes ‘agricultural data’ and who can participate?

    Agriculture data covers a wide variety of data types (such as field, laboratory and sensor data), generated from production or research relevant to agriculture and can be collected and used by farmers, agronomists, researchers and industry. Participants can include individuals, organisations or communities with the vision of improving the sharing and reuse of agricultural data

  • Can our organisation participate but retain control over data access?

    Yes. Many research projects, facilities and organisations have existing policies about data reuse and access. Whilst the assignment of a standard "open" licence (e.g. Creative Commons 4.0) is encouraged for maximal data reuse of agricultural data through AgReFed, the ‘A’ in FAIR stands for ‘Accessible under well defined conditions’. AgReFed policy settings, based on FAIR guidelines, require that data is findable through AgReFed through a rich description in a machine-readable metadata record so that the existence of the data can be discovered, relevance to potential users evaluated, and that the conditions under which the data may be accessed, reused and cited are clear.

  • What is meant by ‘participation’ in the Agricultural Research Federation community?

    The Agricultural Research Federation is made up of providers (people and organisations) who contribute data and services to AgReFed. These providers have the opportunity to participate in the Federation, for example as a member of the Federation Council and/or the Technical Committee (see the Data Stewardship Framework for details). These are the bodies that set the policies for AgReFed, including data standards, technical architecture, and governance. See Get Involved.

  • I already submit my data to a repository - would I need to do it again?

    The AgReFed data ecosystem is not a ‘repository’ for data - you do not submit your data, and it does not hold your data. Rather, datasets or data services are made discoverable, accessible, and possibly visualised via the AgReFed portal. For example, data can be made accessible via AgReFed from your domain-specific, organisation or institutional database or repository via an API, and your metadata records made discoverable in AgReFed if already in a registry such as Research Data Australia or Government data portal.

  • Our organisation has its own data management practices - could this work with AgReFed?

    Yes. Data made available through AgReFed will be required to meet community standards for AgReFed FAIR and Trusted repositories to maximise data discovery and reuse. Yet the needs of agriculture data provider communities and their data management practices are diverse and this is recognised. AgReFed does not enforce one particular data management and delivery solution, but rather works together toward improving the sharing, reuse and value adding to providers’ agricultural data as part of the provider’s usual business.

  • Do I have to conform to a particular data ‘standard’

    The use standard and open-source data exchange standards, metadata standards and widely accepted controlled vocabularies are encouraged where possible. This helps to make agricultural data understandable by both humans and machines and aids seamless exchange and integration of datasets (interoperability, the “I” in FAIR). AgReFed does not enforce a particular standard but rather encourages the community to work together to adopt standards that are suitable within, and across, the different areas of agricultural research.

  • What does ‘machine-readable’ mean, and do I need to make my data fully machine-readable to participate?

    ‘Machine readability’ is the capability of data to be discovered, queried, understood and actioned by a machine for maximising efficiencies and insights in a data-rich world. Machine-readable metadata is the first major step towards becoming maximally FAIR for data discovery, access and reuse through AgReFed. Employment of machine-readable metadata, such as used to generate records in Research Data Australia, is becoming common practice of data brokers and institutions. Whilst making data fully ‘machine-readable’ can maximise reuse of the data, there can be circumstances where this is deemed not desirable for data use or beyond the existing capacity of the data provider.

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