DMP Guide: NSERC for Agriculture & Food Science

1. Funder Policy & Open Data Compliance

In alignment with international open-science mandates, Natural Sciences and Engineering 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 ResearchNet 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.

Data outputs in **Agriculture & Food Science** typically consist of historical records, gridded data, or structured text documents. DMPs must outline plans to archive these files in open, non-proprietary formats to avoid software lock-in under **NSERC** projects.

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 NSERC 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

When drafting a data management plan dmp to satisfy NSERC guidelines, defining systematic data collection methods and formal data curation standards is vital. Utilizing institutional dmptool workflows ensures that these administrative requirements are built-in from the outset of the study. 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. Architecturally, teams can configure either a secure relational data warehouse or a cost-effective cloud-based data lake, evaluating how this data lake vs data warehouse setup supports formal data analysis and immediate exploratory data analysis under NSERC guidelines. 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 Natural Sciences and Engineering Research Council targets. The study will document clear data versioning protocols hosted on the open science framework osf to enable reproducible data sharing matching top fair data principles examples. Furthermore, any community-engaged data must respect the care data principles and support indigenous data sovereignty care standards to ensure local governance of shared knowledge under NSERC audits. This explicit lifecycle structure meets the standard pre-requisites issued under NSERC project management guidelines.

4. Frequently Asked Questions