Abstract
Dublin Core descriptors capture metadata in most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the requirements of different communities with the so-called Dublin Core Application Profiles that rely on the agreement within user communities, taking into account their evolving needs. In this paper, we propose an automated process to help curators and users discover the descriptors that best suit the needs of a specific research group in the task of describing and depositing datasets. Our approach is supported on Dendro, a prototype research data management platform, where an experimental method is used to rank and present DC Terms descriptors to the users based on their usage patterns. User interaction is recorded and used to score descriptors. In a controlled experiment, we gathered the interactions of two groups as they used Dendro to describe datasets from selected sources. One of the groups viewed descriptors according to the ranking, while the other had the same list of descriptors throughout the experiment. Preliminary results show that (1) some DC Terms are filled in more often than others, with different distribution in the two groups, (2) descriptors in higher ranks were increasingly accepted by users in detriment of manual selection, (3) users were satisfied with the performance of the platform, and (4) the quality of description was not hindered by descriptor ranking. © 2018 Springer-Verlag GmbH Germany, part of Springer Nature

Abstract
The increasing prevalence of Open Science has brought reproducibility to the center of discussion of the scientific community as a requirement for ensuring the transparency and correctness of a research workflow. The current publishing landscape is evolving, as shown by the emergence of notebook technologies powering a new generation of interactive Web Journals. These use state-of-the-art interactive graphical visualizations and on-demand data processing to research papers, allowing readers to trace every step of the process, from raw data to the finalized visualization. Since there are many Research Notebook technologies and interactive graphical visualization solutions to choose from, we present a summary comparative overview of Web Journals and the Notebook engines that power the interactive, data driven visualizations inside their publications. Given our focus on visualization, our metrics are the support for the most advanced, popular and widely adopted data visualization frameworks. We conclude that Jupyter Notebook is currently the best alternative for the average user, given its popularity and support, combined with broad support for powerful and high-level interactive visualization grammars. © 2019 AISTI.

Abstract
The increasing prevalence of Open Science has brought reproducibility to the center of discussion of the scientific community as a requirement for ensuring the transparency and correctness of a research workflow. The current publishing landscape is evolving, as shown by the emergence of notebook technologies powering a new generation of interactive Web Journals. These use state-of-the-art interactive graphical visualizations and on-demand data processing to research papers, allowing readers to trace every step of the process, from raw data to the finalized visualization. Since there are many Research Notebook technologies and interactive graphical visualization solutions to choose from, we present a summary comparative overview of Web Journals and the Notebook engines that power the interactive, data driven visualizations inside their publications. Given our focus on visualization, our metrics are the support for the most advanced, popular and widely adopted data visualization frameworks. We conclude that Jupyter Notebook is currently the best alternative for the average user, given its popularity and support, combined with broad support for powerful and high-level interactive visualization grammars.

Abstract
Archives have well-established description standards, namely the ISAD(G) and ISAAR(CPF) with a hierarchical structure adapted to the nature of archival assets. However, as archives connect to a growing diversity of data, they aim to make their representations more apt to the so-called linked data cloud. The corresponding move from hierarchical, ISAD-conforming descriptions to graph counterparts requires state-of-the-art technologies, data models and vocabularies. Our approach addresses this problem from two perspectives. The first concerns the data model and description vocabularies, as we adopt and build upon the CIDOC-CRM standard. The second is the choice of technologies to support a knowledge graph, including a graph database and an Object Graph Mapping library. The case study is the Portuguese National Archives, Torre do Tombo, and the overall goal is to build a CIDOC-CRM-compliant system for document description and retrieval, to be used by professionals and the public. The early stages described here include the design of the core data model for archival records represented as the ArchOnto ontology and its embodiment in the ArchGraph knowledge graph. The goal of a semantic archival information system will be pursued in the migration of existing records to the richer representation and the development of applications supported on the graph. © Springer Nature Switzerland AG 2019.

Abstract
The importance of research data management is widely recognized. Dendro is an ontology-based platform that allows researchers to describe datasets using generic and domain-specific descriptors from ontologies. Selecting or building the right ontologies for each research domain or group requires meetings between curators and researchers in order to capture the main concepts of their research. Envisioning a tool to assist curators through the automatic extraction of key concepts from research documents, we propose 2 concept extraction methods and compare them with a term extraction method. To compare the three approaches, we use as ground truth an ontology previously created by human curators. © 2018, Springer Nature Switzerland AG.