DMP Guide: MSCA for Public Health & Epidemiology

1. Funder Policy & Open Data Compliance

In alignment with international open-science mandates, Marie Skłodowska-Curie Actions 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 Public Health & Epidemiology, 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 Funding & Tenders Portal 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 Public Health & Epidemiology, datasets typically range from raw observational measurements to curated computational models.

Social science research in **Public Health & Epidemiology** frequently gathers sensitive human opinions. The DMP must detail explicit participant consent forms, verbatim transcript pseudonymisation protocols, and secure restricted-access storage required by **MSCA**.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Epidemiologic Methods 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 MSCA 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, CDISC ODM, SDMX standards 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, WHO Data Platform, ICPSR, and directory servers mapped in PubMed, Embase & Global Health). 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 Public Health & Epidemiology 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 Marie Skłodowska-Curie Actions's schemas. Adhering to MSCA 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 Funding & Tenders Portal 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 MSCA 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. Implementing this storage layout satisfies compliance protocols overseen by the MSCA data audit team.

4. Frequently Asked Questions