are (1) the poorly structured, and mostly neither standardized nor machine-readable presentation of results and thus the more difficult subsequent use of the knowledge contained in the publications, (2) the, in many cases, not yet open direct provision of all content due to low dissemination of gold open access procedures, and (3) the mapping of the respective status quo, which is mostly already outdated by the time publications are disclosed. For some years now, journal publications have been supplemented in part by data publications (Figure , D), which increase the transparency of science and -if data provision is carried out in accordance with standards established in the community in subject repositories -enable faster and better structured availability of data. The structured, standardized, open provision of research data in repositories can be an essential pillar for making information available in the future. For the scientific contributions themselves, pre-print servers (Figure ) can be a solution for rapid publication, so that results are open and quickly accessible. To date, however, there are few processes that support the systematic provision of structured information through the currently established publication processes. Structured information can be obtained in some disciplines in the form of databases, but access to the contents of these databases often necessitates costly subscriptions. Typically, the content of the databases is collected by commercial providers reusing the information of scientific publications by extracting, curating, and processing the data contained in the publications for structured presentation (Figure ). The databases obtained by processing published data and information are of great importance for scientific work, especially in chemistry. They form the information backbone for searching scientific information. For chemical reactions, known chemical substances, and properties of substances, the databases SciFinder 5 and Reaxys 6 are an important part for literature and information search. However, the use of the commercial systems is associated with high costs and the provision of information takes place with a time delay, as the systematic investigation and re-use of non-structured information from the literature takes time, even for commercial providers. Efficient processes to gain structured information need either laborious manual extraction or software-supported analysis. Other disadvantages of providing structured information via commercial databases are the low influence of the chemical community on the presentation and curation of the information and the lack of means to efficiently provide overviews about subject-specific content. While formats such as Wikipedia can be used to provide and query general knowledge, there are no comparable platforms for current research content, and overviews for current research topics have to be generated manually from scratch in the form of review articles. However, these are quickly outdated and can only provide a static insight into the prepared topic. In the long run, AI based on large language models (LLMs) could probably serve as a valuable extension to gain state of the art insights and customized overviews on research topics, given that Large Language Models (LLMs) have shown robust capabilities for knowledge extraction across various domains . However, their performance on the current state of the research is not comparable with data collections that are curated by scientists.