Abstract
Web Search Engine Results Pages (SERP) are one of the most well-known and used web pages. These pages have started as simple "10 blue links"pages, but the information in SERP currently goes way beyond these links. Several features have been included in these pages to complement organic and sponsored results and attempt to provide answers to the query instead of just pointing to websites that might deliver that information. In this work, we analyze the appearance and evolution of SERP features in the two leading web search engines, Google Search and Microsoft Bing. Using a sample of SERP from the Internet Archive, we analyzed the appearance and evolution of these features. We found that SERP are becoming more diverse in terms of elements, aggregating content from different verticals and including more features that provide direct answers.

Abstract

Abstract

Abstract
Indispensable in many contexts, images are fundamental in the tasks of representation and transmission of information. In the scientific context, images can be tools for researchers seeking to see their data properly managed. Research data management guides in this direction as it determines necessary phases in the life cycle of projects. The description phase is fundamental as it is an essential means for data context, safeguarding, and reuse. The description often occurs through metadata models composed of descriptors capable of attributing context. However, there is one common aspect: the values associated with these descriptors are always textual or numeric. Through studies and work developed over the last few years, we propose a new approach to description, where images can have a preponderant role in the description of data, assuming the role of metadata. We present several pieces of evidence, point out their challenges and determine the opportunities this new perspective can have in the research. Images have specific characteristics that can be leveraged in improving data description. Historical evidence establish that images have always been used and produced in research, yet their representational ability has never been harnessed to describe data and give more context to the scientific process. ©, Joana Rodrigues and Carla Teixeira Lopes. Published with license by Taylor & Francis Group, LLC.

Abstract
Wikipedia is the world's largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality metrics, and used datasets, examining 149 distinct studies, and exploring commonalities and gaps in them. The literature is extensive, and the approaches follow past technological trends. However, machine learning is still not widely used by Wikipedia, and we hope that our analysis helps future researchers change that reality.

Abstract
Archives preserve materials that allow us to understand and interpret the past and think about the future. With the evolution of the information society, archives must take advantage of technological innovations and adapt to changes in the kind and volume of the information created. Semantic Web representations are appropriate for structuring archival data and linking them to external sources, allowing versatile access by multiple applications. ArchOnto is a new Linked Data Model based on CIDOC CRM to describe archival objects. ArchOnto combines specific aspects of archiving with the CIDOC CRM standard. In this work, we analyze the ArchOnto representation of a set of archival records from the Portuguese National Archives and compare it to their CIDOC CRM representation. As a result of ArchOnto's representation, we observe an increase in the number of classes used, from 20 in CIDOC CRM to 28 in ArchOnto, and in the number of properties, from 25 in CIDOC CRM to 28 in ArchOnto. This growth stems from the refinement of object types and their relationships, favouring the use of controlled vocabularies. ArchOnto provides higher readability for the information in archival records, keeping it in line with current standards.