Love Your Code: Building common standards for reproducibility with administrative and survey data

Sharing code is good research practice and helps actors in the
research domain to be reproducible. Data producers benefit from
including syntax in data documentation to demonstrate how derived
variables were constructed; data users benefit from publishing code that
underpins research findings; and data users can helpfully share back
value-added work they have done in the course of their analysis so that
ne users can benefit.

A number of leading journals now require
code and syntax to be uploaded and some rerun this code to ensure that
results in an article are indeed replicable. ‘Showing the code’ can help
demonstrate trust in published work, but how far should we go to
validate code or enforce that it is reproducible? Can we define a
baseline best practice recommendations for publishing code in the social
and behavioural sciences? How far might Reproducibility Services go
towards demonstrating robustness in research findings?

Come and join us for a knowledge exchange day with experts in the field to discuss and debate these issues.

Prerequisites:
data depositors, advanced statistics/secure lab data users, data or
peer reviewers curators with statistical knowledge and experience

This event supports the 2020 Love Data week

#LoveYourCode