Building sustainable information systems and transformer models on demand

The growing practice of archiving research data in repositories reflects an upward trend. However, storing data in an RDR (Research Data Repository) does not guarantee that the archived data will always be readily reusable, even if this fulfils the FAIR (Findable, Accessible, Interoperable Reusable) principles. To ensure sustainable RDM (Research Data Management), archiving must consider the future potential for data reuse in a low-threshold fashion. In this article, we demonstrate the utilisation of straightforward methods to implement a so-called warm or hot archiving for research data within an RDR, as opposed to the conventional cold archiving approach. We explore the additional value of using research data in the humanities, emphasising the advantages of maintaining data accessibility and relevance over time. In the humanities, evaluating numerous data sets efficiently is crucial for current and future projects. Reviewing and evaluating relevance is important, particularly when dealing with a substantial number of data sets. Rapid evaluation facilitates profound decisions on the utility of the data for one’s ongoing or upcoming projects. For hot archiving, this means that in addition to the research data, the data should be available in a human-friendly way, i.e., a viewer application to visualise the data should be easily accessible. However, as rapid developments in the IT sector mean that after a few years, it cannot be guaranteed that these viewers or other tools will work, we also show how data can be viewed in a user-specific way via the RDR and how sustainable viewing can be integrated into the RDR. This article presents a generic approach to building sustainable viewers, which we call information systems, or transformer models on demand using data from pre-modern Arabic. In addition, we show that the easy-to-use chatbot ChatGPT can alternatively be context-specifically prepared to deliver more precise results and associated resources in the field of humanities. On the one hand, we have achieved a substantial reduction in the development time of an information system, from months to seconds, as well as the ability to fine-tune BERT (Bidirectional Encoder Representations from Transformers) models without specific knowledge in selecting models or tools. On the other hand, we have developed a chatbot that not only provides project-specific responses but also references the sources.