## 1 Introduction  
The latest advances in technologies such as natural language processing [1], image recognition [2], neutral networks [3], etc., have extensively enriched the theory and methods of information retrieval. More specifically, large language models such as BERT [4, 5], BARD [6], Llama [7, 8], GPT-x [9], and RoBERTa [10] are now being fine-tuned to perform traditional natural language processing tasks such as information extraction, name entity recognition, text summarization, text classification, and, in most cases, building question/answer systems. However, the need of providing precise retrieval systems remains challenging to fulfill since these large language models can hardly trace the results of a user query to a data source. In the context of scientific documentation, this fact is even more relevant when someone is looking for specific research artifacts such as an equation,a model, a table, etc. That is why traditional retrieval systems are still relevant in the field of scientific documentation. In this context, the process of extracting information from documents hosted in scientific repositories on a specific subject is partially efficient [11] due to the massive volumes of documents and their structural differences. It is not easy to efficiently extract and retrieve information from these documents due to the variety and diversity of the formats in which documents are published, which implies the necessity of employing information extraction techniques for each distinct format, resulting in complications during their processing. Hence, the automatic extraction of content and metadata from scientific repositories remains a challenge for researchers [12]. This challenge also implies issues regarding document classification and machine-processing quality, meeting information needs, and providing precise query results. Additionally, there is a need for metrics to measure the quality of text-based contents and other research artifacts, as well as specific metrics tailored to scientific documentation to ensure that the Open Science principles are fulfilled. Models for predicting or measuring quality are required, alongside considerations of accessibility and usability to ensure reproducibility. The fields of application of information retrieval are increasingly extensive, due to changes in behavior and dissemination of science [2], making it difficult to search and access due to the heterogeneity of the information source such as tables, equations, and algorithms, among others. The quality, visibility, and accessibility [13; 14] of scientific documents also have impact in the satisfaction of the user's need, because several objects may coincide with the query with different degrees of relevance [15]. Information retrieval methods all have in common features to query and classify unstructured data (documents) through the use of computational tools, so that the documents of interest to users can be quickly and accurately located, thus achieving greater efficiency and effectiveness [1], which are usually measured using the metrics of precision and recall. In this sense, the focus of information retrieval is to evaluate the sensitivity and importance of the keywords or concepts within contents, analyze the potential search intent and the user's target range, and accurately feed the results back [16]. On the other hand, the Open Science initiative is looking for bringing research closer to society through a set of good research practices such as open access to data, results, and scientific articles with the objective of making research properly available for validation, reuse, etc. [17]. Through these practices, the contribution of researchers becomes more visible by applying the principle of sharing academic information. That is why researchers are increasingly seeking more forms of collaboration, which has led to the practices related to Open Science being used more frequently.