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

14 Feb 2020 12:00 am
Data skills

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.

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


Event resources

Presentation slides: Introduction, Andrew Engeli, Office for National Statistics and Louise Corti, UK Data Service| Presentation slides: The case for reproducibility, Paul Jackson, ADR-UK| Presentation slides: Reproducibility and granular data: What’s the issue?, Stefan Bender, Deutsche Bundesbank| Presentation slides: The Turing Way: Reproducible, Inclusive, Collaborative Data Science, Kirstie Whitaker, Turing Institute and The UK Reproducibility Network| Presentation slides: Quality Assurance of Administrative Data, Catherine Bromley, Office for Statistics Regulation| Presentation slides: The Challenges of Reproducible Research and Teaching it, Chris Playford, QStep, Exeter| Presentation slides: Transparency and reproducibility for linked administrative datasets, Katie Harron, Institute of Child Health, UCL| Presentation slides: Open analytics at the Health Foundation, Fiona Grimm, The Health Foundation| Presentation slides: Deriving variables: A Data Managers’ guide to DV best practice, Jess Bailey, NatCen| Presentation slides: Challenges of creating and documenting derived variables and harmonised datasets in longitudinal studies, Aida Sanchez, Centre for Longitudinal Studies and Dara O’Neill, CLOSER| Presentation slides: Keynote- Ed Humpherson, Office for Statistical Regulation| Link to presentation slides: What we do and what we need, Lars Vilhuber, American Economic Association| Presentation slides: How to reproduce: The Cascad Reproducibility Service, Christophe Perignon, Cascad| Presentation Slides: Breakout sessions