Abstract
Research data management is an essential process in scientific research activities. It includes monitoring data from the moment it is created until it is deposited in a repository so that later it can be accessed and reused by others. Sharing and reuse are the last steps in this process. It is essential to ensure that the data stored in digital repositories is well preserved in the long term and that its adequate interpretation and future reuse is guaranteed. Following this debate, questions arise related to the interoperability of systems and the suitability of platforms. In this study, we study how data management platforms can solve the problems associated with description, preservation, and access in digital media, making their usefulness evident. We identify some of the most relevant repository platforms in the scope of research data management, offering the scientific community an aggregating view of the various solutions and their main characteristics, thus aiming at a better understanding of them for their appropriate choice.

Abstract
Linked Data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and promote findability. The required detail in manual descriptions of cultural heritage objects can be taxing and time-consuming. Given this, in EPISA, a research project on this topic, we propose to use the contents of the digital representations associated with the objects to assist archivists in their description tasks. More specifically, to extract information from the digital representations useful for an initial ontology population that should be validated or edited by the archivist. We apply optical character recognition in an initial stage to convert the digital representation to a machine-readable format. We then use ontology-oriented programming to identify and instantiate ontology concepts using neural networks and contextual embeddings. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Abstract
[No abstract available]

Abstract
Electronic Health Records store extensive patient health data, playing a crucial role in healthcare management. Extracting information from these text-heavy records is difficult due to their domain-specific vocabulary, which challenges applying general-domain techniques. Recent advancements in Large Language Models (LLMs) and an increasing interest in the field have sparked considerable progress in solving Clinical Information Extraction (IE) tasks. We review these applications in Clinical IE, highlighting the most common tasks, most successful methods, and most used datasets and evaluation criteria. Examining 85 studies, we synthesize and organize the current research trends, highlighting common points between papers. The presence of LLMs can be felt in the most common tasks, with novel approaches being attempted and showing promising results. However, breakthroughs are still necessary in designing reliable end-to-end systems that can perform all the Clinical IE tasks within a single system.

Abstract
This study examines the role of online health communities in Brazil dedicated to cannabis treatments for chronic diseases as platforms for evidence-based activism. Using a mixed-methods approach, the research combines qualitative analysis with computational techniques, including Latent Dirichlet Allocation (LDA) topic modeling, to analyze six online groups from WhatsApp and Facebook. Key themes emerging from the analysis include treatment per pathology, treatment effects, access barriers, peer support, and advocacy efforts. The findings reveal how these communities act as epistemic networks, where patients and caregivers co-produce knowledge by sharing personal experiences and engaging in dialogue with healthcare professionals. This study highlights how online health communities transform experience sharing into structured evidence, enabling collective action to address barriers such as limited access to cannabis-based treatments. It underscores the potential of digital platforms to empower patients, foster collaboration with healthcare professionals, and influence health governance.

Abstract
Online health communities enable patients and caregivers to share experiences, seek advice, and collaboratively generate knowledge about treatments and condition. However, accessing relevant information often proves challenging due to platform limitations like insufficient search functionalities. A previous study identified key topics discussed in Brazilian online health groups centered on cannabis treatments for chronic diseases. Building on these findings, this study introduces a proof-of-concept chatbot designed to enhance access to the collective knowledge within these communities. The chatbot prototype, built using Google Dialogflow, was tailored to provide contextually relevant, accurate, and user-friendly responses. A user study involving 38 participants evaluated its performance, showing high user satisfaction, task completion rates, and trust in the information provided. The results highlight the chatbot’s potential enhance knowledge accessibility, promote patient engagement, and support evidence-based activism by organizing and disseminating community-generated content effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.