Sharing of Materials, Data, and Code

Sharing of research materials, research data, and analytical code, is a fundamental prerequisite to ensure that any research finding can be independently reproduced and verified, possibly with additional robustness tests, to enable independent replications with new samples, facilitate methodological research and meta-analyses, allow exploration and alternative analyses (and different interpretations) leading to new insights. It accelerates the interchange of ideas, fosters collaboration, assists with education and training, and increases the sustainability of research funding.

Learn more about the reasons to share research materials, which materials should be shared (and which should not), how to document them, and which sharing tools/platforms are available to you.

Learn more about the ‘FAIR Guiding Principles for scientific data management and stewardship’ (Wilkinson et al., 2016) to improve the findability, accessibility, interoperability, and reuse of digital research assets. 

Open Materials means making research materials (stimuli, procedures, instruments, code, instructions, tests) freely available on the public internet permitting any user to download, copy, analyse, re-process, pass them to software or use them for any other purpose without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.

Here you will find information about: 

• Basics
• Licensing
• Research Data Management and Law
• Tools

Open Data means making data freely available on the public internet permitting any user to download, copy, analyse, re-process, pass them to software or use them for any other purpose without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.

Here you will find information about:

• ethical barriers
• rights/duties of authors and secondary users
  

Open analysis code means providing the analysis code along with the belonging open data and thereby making the open data repeatable and reusable. At the best, this includes all scripts used for data processing as well as statistical analyses and other codes (simulations, etc.).

In cases of non-open access data (e.g., ethical barriers), related open analysis code makes conducted analyses still more transparent.

Here you will find information about:

• content of open analysis code
  
• aspects to create reproduceable code
• check your code for reproducibility
• examples