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Title: MEDFORD: A human- and machine-readable metadata markup language
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 MEtaData Format for Open Reef Data (MEDFORD), in which scientists can record all details relevant to their research. Being 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.  more » « less
Award ID(s):
1940169 1939263 1939795
PAR ID:
10374277
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Database
Volume:
2022
ISSN:
1758-0463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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