Abstract
Purpose The quality of consumer-oriented health information on the web has been defined and evaluated in several studies. Usually it is based on evaluation criteria identified by the researchers and, so far, there is no agreed standard for the quality indicators to use. Based on such indicators, tools have been developed to evaluate the quality of web information. The HONcode is one of such tools. The purpose of this paper is to investigate the influence of web document features on their quality, using HONcode as ground truth, with the aim of finding whether it is possible to predict the quality of a document using its characteristics. Design/methodology/approach The present work uses a set of health documents and analyzes how their characteristics (e.g. web domain, last update, type, mention of places of treatment and prevention strategies) are associated with their quality. Based on these features, statistical models are built which predict whether health-related web documents have certification-level quality. Multivariate analysis is performed, using classification to estimate the probability of a document having quality given its characteristics. This approach tells us which predictors are important. Three types of full and reduced logistic regression models are built and evaluated. The first one includes every feature, without any exclusion, the second one disregards the Utilization Review Accreditation Commission variable, due to it being a quality indicator, and the third one excludes the variables related to the HONcode principles, which might also be indicators of quality. The reduced models were built with the aim to see whether they reach similar results with a smaller number of features. Findings The prediction models have high accuracy, even without including the characteristics of Health on the Net code principles in the models. The most informative prediction model considers characteristics that can be assessed automatically (e.g. split content, type, process of revision and place of treatment). It has an accuracy of 89 percent. Originality/value This paper proposes models that automatically predict whether a document has quality or not. Some of the used features (e.g. prevention, prognosis or treatment) have not yet been explicitly considered in this context. The findings of the present study may be used by search engines to promote high-quality documents. This will improve health information retrieval and may contribute to reduce the problems caused by inaccurate information.

Abstract
Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable. Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts. We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running.  The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.

Abstract
The diversity of research topics and resulting datasets in the field of Ecology (the scientific study of ecological systems and their biodiversity) has grown in parallel with developments in research data management. Based on a meta-analysis performed on 93 scientific references, this paper presents a comprehensive overview of the use of metadata tools in the Ecology domain through time. Overall, 40 metadata tools were found to be either referred or used by the research community from 1997 to 2018. In the same period, 50 different initiatives in ecology and biodiversity research were conceptualized and implemented to promote effective data sharing in the community. A relevant concern that stems from this analysis is the need to establish simple methods to promote data interoperability and reuse, so far limited by the production of metadata according to different standards. With this study, we also highlight challenges and perspectives in research data management in the domain of Ecology towards best practice guidelines.

Abstract
Researchers are currently encouraged by their institutions and the funding agencies to deposit data resulting from projects. Activities related to research data management, namely organization, description, and deposit, are not obvious for researchers due to the lack of knowledge on metadata and the limited data publication experience. Institutions are looking for solutions to help researchers organize their data and make them ready for publication. We consider here the deposit process for a CKAN-powered data repository managed as part of the IT services of a large research institute. A simplified data deposit process is illustrated here by means of a set of examples where researchers describe their data and complete the publication in the repository. The process is organised around a Dublin Core-based dataset deposit form, filled by the researchers as preparation for data deposit. The contacts with researchers provided the opportunity to gather feedback about the Dublin Core metadata and the overall experience. Reflections on the ongoing process highlight a few difficulties in data description, but also show that researchers are motivated to get involved in data publication activities.

Abstract
Because of terminology mismatches, health consumers frequently face difficulties while searching the Web for health information. Difficulties arise in query formulation but also in understanding the retrieved documents. In this work we analyze how documents' readability affects users' comprehension and how both affect the retrieval performance, measured in different ways. In addition, we analyze how performance measures relate with each other. For this purpose we have conducted a laboratory user study with 40 participants. We found that readability is essential for a document to be at least partially relevant and that it becomes even more important if the document has medico-scientific terminology. Moreover, the relevance of a document to a specific user highly depends on its comprehension. In lay queries we found the medical accuracy of users' answers is related to the session's relevance assessments. This shows that users can, at least in part, relate their relevance assessments with the medical accuracy of the documents. On the other hand, this relationship does not exist with medico-scientific queries.

Abstract
The current requirements for open data in the EU are increasing the awareness of researchers with respect to data management and data publication. Metadata is essential in research data management, namely on data discovery and reuse. Current practices tend to either leave metadata definition to researchers, or to assign their creation to curators. The former typically results in ad-hoc descriptors, while the latter follows standards but lacks specificity. In this exploratory study, we adopt a researcher-curator collaborative approach in five data publication cases, involving researchers in data description and discussing the use of both generic and domain-oriented metadata. The study shows that researchers working on familiar datasets can contribute effectively to the definition of metadata models, in addition to the actual metadata creation. The cases also provide preliminary evidence of cross-disciplinary descriptor use. Moreover, the interaction with curators highlights the advantages of data management, making researchers more open to participate in the corresponding tasks. © Springer Nature Switzerland AG 2019.