DMP Guide: ARC for Agriculture & Food Science

1. Funder Policy & Open Data Compliance

In alignment with international open-science mandates, Australian Research Council requires all principal investigators to submit a comprehensive Data Management Plan (DMP) with their grant application. A robust DMP details how research data will be collected, processed, documented, stored, shared, and preserved both during and after the project.

For projects in the field of Agriculture & Food Science, managing data correctly is essential not only for compliance, but also to support peer-review validation and reproducibility. All DMPs must be submitted through the RMS portal, using standard institutional guidelines.

2. Data Types, Formats, and Metadata Standards

A high-quality DMP must explicitly identify the types of data that will be generated and specify open, non-proprietary file formats to ensure long-term usability. For Agriculture & Food Science, datasets typically range from raw observational measurements to curated computational models.

Research outputs for **Agriculture & Food Science** generally comprise historic scans, tabular registers, or structured texts. The DMP should describe plans to store all digital files in open, non-proprietary formats, protecting project deliverables from vendor lock-in under **ARC** awards.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Agriculture branch of scholarly vocabularies. This ensures indexers and crawlers can crawl and identify research outputs accurately.

3. Step-by-Step DMP Construction Protocol

When preparing your DMP for a ARC proposal, structure your document around these core sections:

1. Data Collection and Generation:
   Describe the methodology, instrumentation, or software used to collect or generate new data. Detail quality assurance and quality control measures implemented at your facility.
2. Documentation and Metadata:
   Explain how the data will be documented, including accompanying read-me files, data dictionaries, and laboratory notebooks. Specify the metadata standards to be utilized (using AgMes (Agricultural Metadata Element Set), Dublin Core, EML as standard).
3. Ethics, Intellectual Property, and Consent:
   Address how sensitive or confidential datasets will be handled. Detail anonymisation processes, access controls, and compliance with institutional ethics boards.
4. Storage, Backups, and Security:
   State where data will be stored during active research. Detail automated backup schedules, server redundancies, and access authorisation protocols.
5. Long-Term Preservation and Archiving:
   Select the digital repository for post-project archiving (such as Zenodo, Pangaea, Dryad, Figshare, and directory servers mapped in CAB Direct & Agricola). Confirm that the repository supports persistent identifiers (handles/DOIs) and provides secure preservation.

Open Science Workflows, Data Curation & Repositories

To secure approval from Australian Research Council, the investigator's data management plan dmp must clearly justify chosen data collection methods and adhere to active data curation standards. Integrating digital dmptool workflows helps automate compliance reporting via the RMS portal. This includes describing protocols for data cleaning, validating data integrity via checksums, and conducting secure data wrangling on raw source files. Each output dataset must be documented with an explanatory data dictionary mapping key metadata fields. The DMP must justify whether files are catalogued in a structured data warehouse or kept as raw files in a flexible data lake, discussing how a data lake vs data warehouse decision impacts subsequent data analysis and programmatic exploratory data analysis for Agriculture & Food Science. PIs will facilitate public sharing by leveraging the dryad data repository, creating searchable figshare datasets, or completing a zenodo data upload, ensuring tracking through the data citation index in compliance with nsf data management plan protocols and Australian Research Council targets. Researchers are required to publish systematic data versioning protocols through the open science framework osf to facilitate long-term reproducible data sharing in line with fair data principles examples. If data is collected from specialized regions, the plan must comply with the care data principles and respect indigenous data sovereignty care rights to meet Australian Research Council ethical benchmarks. Aligning the archiving schedule directly with ARC open-access metrics protects the project's funding cycles.

4. Frequently Asked Questions