DMP Guide: ARC for Nursing & Allied Health

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 Nursing & Allied Health, 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 Nursing & Allied Health, datasets typically range from raw observational measurements to curated computational models.

Biological data streams for **Nursing & Allied Health** focus on cellular imaging and experimental protocols. The DMP specifies long-term archival plans, structured indexing mapped to the **Nursing** terminology, and secure patient anonymisation to satisfy **ARC** auditors.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Nursing 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 Dublin Core, CINAHL schema, CDISC standard 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, Dryad, ICPSR, and directory servers mapped in CINAHL & PubMed). 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 ARC 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 ARC 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 Australian 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 ARC audits. Aligning the archiving schedule directly with ARC open-access metrics protects the project's funding cycles.

4. Frequently Asked Questions