The increasingly digital workflow in science has made it possible to share almost all aspects of the research cycle, from pre-registered analysis plans and study materials to the data and analysis code that produce the reported results. Although the growing availability of research output is a positive development, most of this digital information is in a format that makes it difficult to find, access, and reuse. A major barrier is the lack of a framework to concisely describe every component of research in a machine-readable format: A grammar of science.
Lakens, D., & DeBruine, L. M. (2021). Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920970949
Popular repositories Loading
-
-
-
-
machine_readable_FAIR_data_and_code machine_readable_FAIR_data_and_code Public
Manuscript on Machine readable FAIR data and code
Lua 1
Repositories
- talks Public
- papers Public
a repository of open access papers converted to metacheck paperlist objects in .rds format
- metacheck_docker_reproducibility Public
Docker images and build tooling for metacheck's reproducibility_check(sandbox='docker') execution backend
Top languages
Loading…
Most used topics
Loading…