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Abstract The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas’ cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas’ environmental, societal, territory, and economic aspects that could help stem depopulation.

Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders.

Abstract NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project’s goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.

Forecasting environmental parameters in the distant future requires complex modelling and large computational resources. Due to the sensitivity and complexity of forecast models, long-term parameter forecasts (e.g. up to 2100) are uncommon and only produced by a few organisations, in heterogeneous formats and based on different assumptions of greenhouse gases emissions. However, data mining techniques can be used to coerce the data to a uniform time and spatial representation, which facilitates their use in many applications. In this paper, streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to (i) standardise and harmonise the data representations, (ii) produce intermediate scenarios and new informative parameters, and (iii) align all sets on a common time and spatial resolution. Time series cross-correlation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas. Our results highlight that (i) the Mediterranean Sea may have a standalone ‘response’ to climate change with respect to other areas, (ii) the Poles are most representative of global forecasted change, and (iii) the trends are generally alarming for most oceans.

Many research communities working in biology and related fields are deeply interested in having a wide collection of environmental and species distribution data. Obviously, for these communities to be able to carry out their studies in a fast and efficient manner, these data need to be well organized, meticulously described and possibly represented in a standard format that allows for a direct usage. In fact, being the final goal of these communities to extract information from the data and applying some kind of data processing workflows, cutting down the data preparation and preprocessing time is key. This is the main reason that triggered the activity presented in this paper that aimed at converting the whole collection of 10,385 species distribution models published by the Aquamaps consortium (Aquamaps Native Distributions) into NetCDF files, creating a re-usable, portable and self-describing collection of native habitat datasets.

In the context of scientific investigations, data have acquired an ever growing leading role while their large scale, cross-community and cross-domain sharing have concurred to identify new investigation paradigms (Hey, Tansley, & Tolle, 2009). Unfortunately, data interoperability – a mandatory prerequisite for achieving the above scenarios – is still a difficult open research challenge. Both the “data” and “interoperability” concepts are difficult to be fully perceived and actually lead to different perceptions in diverse communities. This problem is further amplified when considered in the context of (global) research data infrastructures that are expected to serve a plethora of communities of practice (Lave & Wenger, 1991) potentially involved in very diverse application scenarios, each characterised by a specific sharing problem.

Virtual Research Environments are innovative, web-based, community-oriented, comprehensive, flexible, and secure working environments conceived to serve the needs of modern science. We overview the existing initiatives developing these environments by highlighting the major distinguishing features. We envisage a future where regardless of geographical location, scientists will be able to use their Web browsers to seamlessly access data, software, and processing resources that are managed by diverse systems in separate administration domains via Virtual Research Environments. We identify and discuss the major challenges that should be resolved to fully achieve the proposed vision, i.e., large-scale integration and interoperability, sustainability, and adoption.