Result for: Crops: Alfalfa Barley Beans (dry) Canola Corn for grain Corn for silage Cotton Hay Hops Potato Rice Rye Ryegrass Sorghum Soybeans Winter wheat Wheat 4R Practices: Metadata Project
4R Research Fund Repository
Dr. Sylvie Brouder
Dr. Sylvie Brouder
Start Date: 2015
End Date: ongoing
- Purdue University Research Repository
The objective of this proposal is to develop a standard data repository and preservation framework for 4R Fund projects. The research repository (RR) and framework will ensure data and metadata are standardized across projects, widely accessible, adhere to emerging open access data principles, and archived for long-term preservation and reuse. We will model the 4R Fund RR on the framework already developed for curation and preservation of data from the Purdue University Water Quality Field Station (WQFS). The WQFS includes a wide variety of data that are similar to those being collected in 4R Fund projects. The WQFS core database is housed within the Purdue University Research Repository (PURR), managed by Purdue Libraries; WQFS researchers have collaborated with PURR and Purdue Libraries to develop workflows, procedures and policies for the curation, preservation and publication of agricultural datasets that meet or exceed emerging requirements for “open access” to data as a public good. Faculty from Purdue Libraries and the Department of Agronomy will collaborate with the research teams of 4R Fund projects to 1) describe, annotate and otherwise prepare existing datasets from 4R Fund projects for open access and publication , 2) assess and, where necessary, improve the data collection workflows and annotation practices of new 4R Fund projects such that completed datasets are “publication ready,” 3) create policy and protocols for a 4R Fund RR that meet grantor and grantee needs for open access, privacy, and embargoing, and 4) create guides and self-help tools for future 4R Fund researchers to ensure compliance with 4R Fund RR policy for data standardization, interoperability, open access, and other “best practices” in data management.