How to increase the reusability of research outputs?
Sure, Open Science has gained some momentum during the last decade. The Horizon Europe programme supports a number of open infrastructure projects, one of them being AquaINFRA, and the next call is already out. The German National Research Data Infrastructure (NFDI) has just announced that funding will continue until 2038. And let’s not forget the local initiatives in universities, such as Open Science Communities, reproducibility networks, and research data management centers. We also talked about 52°North’s efforts in our blog post on Open Science Capacity Building last week. Ultimately, however, we haven’t yet tapped into the full potential when it comes to publishing research outputs. Let’s say we collect some data and analyze it in an R script. We then implement functions for data pre-processing, analysis, and visualization. The configuration of the function parameters is buried in the code and the computational environment is specified on our local computer. We write a paper and send it to a journal or conference for peer review. At best, the submission requirements ask us to publish all materials in a repository and paste the DOI into the article. While we should appreciate that this is more than it was a few years ago, we also need to acknowledge that this is not the most efficient way to share high-value and easily reusable research outputs such as data and source code. Others need to download the materials, install the right version of the libraries, understand the code, find out how to change the parameters and so on and so forth. Many people tend to avoid these efforts and instead develop something new from scratch. So the challenge we want to address in this week’s blog post is:
How to increase the reusability of research outputs?
