This blog post provides a copy of my recent talk at the Royal Microscopical Society’s Virtual Data Analysis in Atomic Force Microscopy Meeting (Event Page): How can we make AFM data analysis more open and reproducible?
I’ve also put together what I hope will be some useful links:
Collaborate with or get help from Research Software Engineers at the University of Sheffield:
Work with Research Librarians on data management:
Get a wide range of IT Support services:
Put your data on Figshare:
Advice on intellectual property (from a commercialisation prespective):
A readable survey report on reproducibility:
The UK government roadmap (Summer 2020):
International views on digital skills training for reproducibility:
Detailled help with reproducible data science:
Tools for Open Science using the R programming language:
A key article on what constitutes reproducible research:
Making data available and accessible to people and computers:
Repository for a wide range of data types:
Repositories for specific data types in biomedicine:
Help with finding the right license for your work:
“Nature”’s rules for code:
Recognising more diverse research outputs:
RSE Communities:
Organisation supporting better research software:
Applying FAIR-like priniciples to code:
Example completed software checklist:
Leading organisation for software and data science training courses:
Blog post on ways of getting into python: