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

Abstract
In a world increasingly present online, people are leaving a digital footprint, with valuable information scattered on the Web, in an unstructured manner, beholden to the websites that keep it. While there are potential harms in being able to access this information readily, such as enabling corporate surveillance, there are also significant benefits when used, for example, in journalism or investigations into Human Trafficking. This paper presents an approach for retrieving domain-specific information present on the Web using Social Media platforms as a gateway to other content existing on any website. It begins by identifying relevant profiles, then collecting links shared in posts to webpages related to them, and lastly, extracting and indexing the information gathered. The tool developed based on this approach was tested for a case study in the domain of Human Trafficking, more specifically in sexual exploitation, showing promising results and potential to be applied in a real-world scenario.

Abstract
Research data management (RDM) includes people with different needs, specific scientific contexts, and diverse requirements. The description is a big challenge in the domain of RDM. Metadata plays an essential role, allowing the inclusion of essential information for the interpretation of data, enhances the reuse of data and its preservation. The establishment of metadata models can facilitate the process of description and contribute to an improvement in the quality of metadata. When we talk about image data, the task is even more difficult, as there are no explicit recommendations to guide image management. In this work, we present a proposal for a metadata model for image description. To validate the model, we followed an experiment of data description, where eleven participants described images from their research projects, using a metadata model proposed. The experiment shows that participants do not have formal practices for describing their imagery data. Yet, they provided valuable contributions and recommendations to the final definition of a metadata model for image description, to date nonexistent. We also developed controlled vocabularies for some descriptors. These vocabularies aim to improve the image description process, facilitate metadata model interpretation, and reduce the time and effort devoted to data description. © 2022 Joana Rodrigues and Carla Teixeira Lopes Published with license by Taylor & Francis Group, LLC.