DMP Guide: ARC for Education Research

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 Education Research, 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 Education Research, datasets typically range from raw observational measurements to curated computational models.

Since studies in **Education Research** rely heavily on human survey panels and interviews, participant privacy is a paramount concern. The DMP must document explicit consent agreements, transcript scrubbing techniques, and firewalled database storage to meet **ARC** standards.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Education 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 DDI standard, Dublin Core Metadata Element Set 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 ERIC, ICPSR, Zenodo, Figshare, and directory servers mapped in ERIC (Education Resources Information Center)). 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. Investigators must outline procedures for post-collection data cleaning, strict audits of data integrity, and programmatic data wrangling to transform raw outputs into clean models. Furthermore, a descriptive data dictionary must be provided to define the database schema. 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. Upon completion, data will be submitted to the dryad data repository, published as figshare datasets, or preserved via a zenodo data upload to be registered in the global data citation index and satisfy nsf data management plan guidelines and regional ARC open-science rules. 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