The NutriNet Knowledgebase for Synthesis of North American Tile Drainage Research Team
Purdue University, Iowa State University, and the University of Illinois Urbana-Champaign
The value of data sharing, reuse and the synthesis of existing historic, disparate and “small” data into big data is widely considered an important and largely untapped resource for advancing evidence-based agricultural science in a changing climate. With an AFRI NIFA Foundational Program award, we are creating a knowledgebase for legacy data from long-term tile drainage research that will permit better use of research results to make informed decisions regarding farm management and natural resource stewardship. We seek a Postdoctoral Research Scientist to assist in ongoing efforts to create novel data infrastructure and to provide leadership in demonstrating the utility of legacy data in testing new hypotheses and generating new knowledge via rigorous statistical synthesis. The project focuses on 11 long-term field experiments in the Midwest US and Canada all with detailed, high-value historic data identified as at moderate to extremely high risk of loss due to lack of curation. The varied data structures, drainage system designs, crop managements and experimental designs create opportunity for novel approaches to exploration of persistent knowledge gaps regarding field-scale N balances and the covariates that drive system-specific differences in N efficiencies. The successful candidate will assist a team of crop, soil and water scientists in prioritizing the research foci, collaborate with data scientists and informaticians to facilitate recovery of the most impactful data into FAIR formats, and lead data visualization and analysis as proof-of-concept for grand challenge research.
Assist in ongoing data recovery into formats compliant with FAIR (Findable, Accessible, Interoperable, Reusable) principles;
Depending on the prioritized hypothesis, develop automated workflows and processes to verify data and meta-data accuracy design/implement best practices to gap fill highly variable data (e.g. drainage flux, greenhouse gas emission, etc.) in order to enhance data quality and reusability.
Lead a team exploration of one or more high-priority N-cycle knowledge gaps and collaborate on additional synthesis efforts undertaken by other team members.
Present at professional meetings and publish results of new syntheses and participate in additional team publications as appropriate including on legacy data recovery processes, lessons learned and best practices.
Position is funded via Purdue University but relocation to the Purdue Campus is not necessary. Location near one of the historic field experiments is desirable (e.g. near any of the cooperating university campuses). Some short term travel to field locations is anticipated.