DMP Guide: RGC for Linguistics & Cognitive Language

1. Funder Policy & Open Data Compliance

In alignment with international open-science mandates, Research Grants 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 Linguistics & Cognitive Language, 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 RGC Electronic System 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 Linguistics & Cognitive Language, datasets typically range from raw observational measurements to curated computational models.

Data outputs in **Linguistics & Cognitive Language** 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 **RGC** projects.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Language 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 RGC 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 OLAC (Open Language Archives Community), Dublin Core 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 TLA (The Language Archive), CLARIN, Zenodo, and directory servers mapped in LLBA (Linguistics and Language Behavior Abstracts)). Confirm that the repository supports persistent identifiers (handles/DOIs) and provides secure preservation.

Open Science Workflows, Data Curation & Repositories

To secure approval from Research Grants 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 RGC Electronic System portal. 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. 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 Linguistics & Cognitive Language. 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 RGC open-science rules. 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 Research Grants Council ethical benchmarks. Aligning the archiving schedule directly with RGC open-access metrics protects the project's funding cycles.

4. Frequently Asked Questions