Abstract
Academia uses methods and techniques that are cutting edge and constantly evolving, while the underlying cultures and working practices remain rooted in the 19th-century model of the independent scientist. Standardization in processes and data standards—delivered via foundational and ongoing training—could ensure a common minimum standard, increase interoperability across the sector, and drive improvements in research quality. But change will require a coordinated approach that recognizes the systems nature of the challenge.
Highlights
At the same time, our underlying cultures and working practices remain rooted in the 19th-century model of the independent scientist
How might we provide incentives to systematizing effective practice? The conventional approach of ‘‘quality through evaluation’’ could play a central role, but this would require that that any evaluation be richer, deeper, and timelier than is currently the case
An alternative to ‘‘quality through evaluation’’ is ‘‘quality by design.’’ If an institution or research group has robust systems in place to manage research quality, it seems likely that quality will improve more rapidly and more sustainably than research quality for an institution or research group that does not
Summary
Systematizing Effective Practice What skills are lacking, or exist but are not standardized across the sector? Delivering training in basic skills—from file-naming conventions to data curation (including formatting of data files, the use of data dictionaries and codebooks, the need for metadata, the importance of using non-proprietary formats, and other principles of FAIR data,[5] etc.) could go a long way to creating common, sector-wide standards in quantitative disciplines. This might include training in more discipline-specific methodological skills, and again this would benefit from a degree of standardization of expectations.
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