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Driving Innovation through Data in Agriculture (DIDAg)

Organizing Committee || Agenda || Speaker Biographies || Registration || Location & Hotel Info || Poster Submission Info

Data management and publication for agricultural economics and dairy agroecosystems – a FACT Initiative workshop for researchers and information scientists

Maryland Holsteins

Public access to the results of federally funded research is not only an executive directive of the U.S. Government; it is fast becoming an expectation of the scholarly community. Access to peer-reviewed publications and the data that support them ensures that research is conducted to the highest standards of integrity and can drive future innovation. In most agricultural research communities, the cultural norms of data management and publication are underdeveloped. Additionally, there are many different agricultural research communities whose interests and methods overlap with other domains (e.g. biomedicine, biodiversity, ecology) but whose specific needs are not yet fully satisfied by the data-management norms of the allied disciplines. Coordination and leadership are needed to establish both the culture and the skill base to achieve an effective transformation.

DIDAg will be the first in what are anticipated to be a series of workshops in support of the USDA NIFA (National Institute of Food and Agriculture) FACT (Food and Agriculture Cyberinformatics and Tools) initiative. DIDAg, which will be held at the National Agricultural Library, will be used to address long-term goals and supporting objectives for two research domains -- dairy agroecosystems and agricultural economics:

  1. Shared understanding of existing policies and resources related to public access to data and agricultural data management.
  2. Clear expectations for research data management and publication in selected research domains, both so that researchers can plan for them and so that information professionals can support them.
  3. Improved cyberinfrastructure, training materials, and business models.
  4. Roadmap for supporting the next generation of data-intensive research in the chosen domains.

Row Crop Harvesting