Reusable methods for Open Science
This
is the second post of this week, so we're discussing how reporting of methodology came to be regarded as an asset to the reproducibility initiative.
Stick around and follow daily!
If you want to read why the scientific method was developed, check it here.
If you want to know why you should care, read on.
Promoting Reusable and Open Methods and Protocols (PRO-MaP) is a set of recommendations aimed at improving the reporting of protocols in the life sciences.
I first heard of this initiative through my work at Bio-protocol. This is an online peer-reviewed protocol journal that curates and hosts high quality, free access, step-by-step protocols across the life sciences. Although relatively recent, it's by no means unique.
The expansion of protocol sharing led to the establishment of detailed guidelines for preprints, and validation sections were introduced in protocol articles. The detailed features of these protocols, structured in a step-by-step format, make them reusable.
PRO-MaP also addresses other stakeholders but those recommendations aimed at researchers are:
Document, share, and follow protocols within your research group, such as maintaining online lab books, with referenced and updated methods.
Follow relevant study design and reporting guidelines when designing and conducting your study and writing your methods. Examples are the PREPARE and MDAR frameworks.
Describe methods in enough detail to allow others to reproduce the experiment. Report research resource identifiers (RRIDs) to unambiguously identify cell lines, antibodies, model organisms, plasmids, software, and tools.
Strengthen static methods sections by linking out to protocols posted on dynamic
platforms, such as protocol repositories that are open access, DOI citable, and that allow versioning and forking.Use methodological shortcut citations responsibly.
Include methods and materials availability statements in papers and publications.
Ensure that methods sections are clearly formatted and user friendly, and make it easy to connect data to specific methods used to generate that data.
Advocate for or offer training in open and reproducible methods and protocols.
Support a culture that rewards and incentivizes method development and protocol sharing.
This is a lot to take in... so I'll go through the intricacies of what makes a good protocol:
Title
Abstract that specifies what the protocol produces
List of required items
Safety information
Chronological step-by-step instructions
Troubleshooting
Protocol limitations and assumptions
References
BONUS:
Keywords
Data analysis
Validation
Graphical abstract
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