A common observation of everyday life reveals the growing importance of data science methods, which are increasingly more and more important part of the mainstream of knowledge generation process. Digital technologies and their potential for data collection and data processing have initiated the birth of the fourth paradigm of science, based on Big Data. Key to these transformations is datafication and data mining that allow the discovery of knowledge from contaminated data. The main purpose of the considerations presented here is to describe the phenomena that make up these processes and indicate their possible epistemological consequences. It has been assumed that increasing datafication tendencies may result in the formation of a data- centric perception of all aspects of reality, making data and the methods of their processing a kind of higher instance shaping human thinking about the world. This research is theoretical in nature. Such issues as the process of datafication and data science have been analyzed with a focus on the areas that raise doubts about the validity of this form of cognition.

Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation.