DMP Guide: DFG for Linguistics & Cognitive Language

1. Funder Policy & Open Data Compliance

In alignment with international open-science mandates, Deutsche Forschungsgemeinschaft (German Research Foundation) 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 elan 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 Linguistics & Cognitive Language, datasets typically range from raw observational measurements to curated computational models.

For qualitative and archival files in **Linguistics & Cognitive Language**, 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 **DFG** digital preservation criteria.

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 DFG 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 Deutsche Forschungsgemeinschaft (German Research Foundation), 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 elan 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 Linguistics & Cognitive Language. 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 Deutsche Forschungsgemeinschaft (German Research Foundation) 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 Deutsche Forschungsgemeinschaft (German Research Foundation) ethical benchmarks. This explicit lifecycle structure meets the standard pre-requisites issued under DFG project management guidelines.

4. Frequently Asked Questions