DMP Guide: MSCA for Engineering & Technology

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 Engineering & Technology, 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 Engineering & Technology, datasets typically range from raw observational measurements to curated computational models.

In the domain of computational **Engineering & Technology**, investigators must archive both the raw input datasets and the containerized software (Docker/Singularity) used for analysis. This ensures that **MSCA** compliance officers can verify and replicate pipeline executions.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Technology, Industry, Agriculture 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 W3C SSN (Semantic Sensor Network), Dublin Core, ISO 10303 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, Figshare, IEEE Dataport, and directory servers mapped in IEEE Xplore, Compendex & Scopus). Confirm that the repository supports persistent identifiers (handles/DOIs) and provides secure preservation.

Open Science Workflows, Data Curation & Repositories

To secure approval from Marie Skłodowska-Curie Actions, 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 Funding & Tenders Portal portal. 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. 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 Engineering & Technology. 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 Marie Skłodowska-Curie Actions targets. 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 Marie Skłodowska-Curie Actions ethical benchmarks. Implementing this storage layout satisfies compliance protocols overseen by the MSCA data audit team.

4. Frequently Asked Questions