Document Type
Article
Date of Original Version
6-17-2022
Department
Computer Science and Statistics
Abstract
Reproducibility of research is essential for science. However, in the way modern computational biology research is done, it is easy to lose track of small, but extremely critical, details. Key details, such as the specific version of a software used or iteration of a genome can easily be lost in the shuffle, or perhaps not noted at all. Much work is being done on the database and storage side of things, ensuring that there exists a space to store experiment-specific details, but current mechanisms for recording details are cumbersome for scientists to use. We propose a new metadata description language, named MEDFORD, in which scientists can record all details relevant to their research. Human-readable, easily-editable, and templatable, MEDFORD serves as a collection point for all notes that a researcher could find relevant to their research, be it for internal use or for future replication. MEDFORD has been applied to coral research, documenting research from RNA-seq analyses to photo collections.
Publication Title, e.g., Journal
Database
Volume
2022
Issue
2022
Citation/Publisher Attribution
Shpilker, Polina, John Freeman, Hailey McKelvie, Jill Ashey, Jay-Miguel Fonticella, Hollie Putnam, Jane Greenberg, Lenore J. Cowen, Alva Couch, and Noah M. Daniels. "MEDFORD: A HUMAN AND MACHINE READABLE METADATA MARKUP LANGUAGE." Database 2022, 2022 (2022). doi: 10.1093/database/baac065.
Author Manuscript
This is a pre-publication author manuscript of the final, published article.
Terms of Use
This article is made available under the terms and conditions applicable
towards Open Access Policy Articles, as set forth in our Terms of Use.