From Data to Credits: Using ReadMe, Markdown, and Dublin Core for Better Documentation

Effective documentation is crucial for good research data management (RDM) enabling reproducibility, data sharing, and collaboration. A common standard for information sharing in data publication is to write text files as generic “ReadMe”s. Using the markup language Markdown and the Dublin Core vocabulary allows for formatted and structured documentation. The enrichment of datasets with these metadata, including licenses such as Creative Commons, ensures findability, attribution, and reusability in open science publication, as well as easy-to-understand descriptions for research partners. Furthermore, utilizing parsers to extract and validate metadata can streamline the documentation process. I will provide some practical tips and examples for implementing these concepts establishing a robust data life cycle in your research and facilitating data sharing and collaboration. Link to README.md: https://doi.org/10.5281/zenodo.14848834Link to Markdown-to-JSON-Parser: https://doi.org/10.5281/zenodo.14942696 and https://github.com/Bondoki/ParsingMetadataMD2JSON