DMP Guide: NSERC for Psychology & Cognitive 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 Psychology & Cognitive 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 Psychology & Cognitive Science, datasets typically range from raw observational measurements to curated computational models.

Since studies in **Psychology & Cognitive Science** 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 **NSERC** standards.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Behavior and Behavior Mechanisms 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 DDI (Data Documentation Initiative), Dublin Core Metadata 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 ICPSR, Open Science Framework (OSF), Zenodo, and directory servers mapped in PsycINFO & PubMed). Confirm that the repository supports persistent identifiers (handles/DOIs) and provides secure preservation.

Open Science Workflows, Data Curation & Repositories

A compliant data management plan dmp for research projects in Psychology & Cognitive Science must detail the specific data collection methods and the precise data curation standards that govern the project. By configuring customizable dmptool workflows, research teams can seamlessly align their files with Natural Sciences and Engineering Research Council's schemas. Adhering to NSERC requirements means detailing how raw files undergo data cleaning, how researchers verify ongoing data integrity, and which tools handle automated data wrangling. Additionally, a standardized data dictionary must be compiled to guarantee metadata clarity. From a storage perspective, researchers must evaluate storing datasets in a highly indexed data warehouse versus a scalable data lake, analyzing the trade-offs of a data lake vs data warehouse architecture for downstream data analysis and exploratory data analysis inside the ResearchNet workspace. To ensure permanent access, datasets will be deposited in the dryad data repository, hosted as figshare datasets, or archived via a secure zenodo data upload, enabling inclusion in the data citation index and fulfilling standard nsf data management plan and local NSERC requirements. We will enforce structured data versioning protocols using the open science framework osf to streamline reproducible data sharing that is fully compliant with fair data principles examples. Additionally, all collaborative research must respect the care data principles and uphold indigenous data sovereignty care policies, keeping community interest central to the data lifecycle. This explicit lifecycle structure meets the standard pre-requisites issued under NSERC project management guidelines.

4. Frequently Asked Questions