DMP Guide: CNPq for Political Science & Public Policy

1. Funder Policy & Open Data Compliance

In alignment with international open-science mandates, Conselho Nacional de Desenvolvimento Científico e Tecnológico 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 Political Science & Public Policy, 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 Plataforma Lattes 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 Political Science & Public Policy, datasets typically range from raw observational measurements to curated computational models.

For qualitative and archival files in **Political Science & Public Policy**, data plans focus on digitised materials, text corpora, and spreadsheets. To ensure durability, the DMP mandates saving all documents in non-proprietary formats, satisfying standard **CNPq** digital preservation criteria.

To guarantee discoverability, datasets should be documented using standardised metadata schemas that map to the Social Sciences 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 CNPq 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 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, Harvard Dataverse, UK Data Service, and directory servers mapped in Worldwide Political Science Abstracts). 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 Political Science & Public Policy 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 Conselho Nacional de Desenvolvimento Científico e Tecnológico's schemas. Our project methodology mandates systematic data cleaning and continuous verification of data integrity to support reproducible data wrangling pipelines. To aid secondary usage, a comprehensive data dictionary will accompany every published record. 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 Plataforma Lattes workspace. Open-access guidelines require teams to push finalised files to the dryad data repository, configure shared figshare datasets, or initiate a zenodo data upload, securing a permanent slot in the data citation index that aligns with nsf data management plan directives. 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 CNPq project management guidelines.

4. Frequently Asked Questions