From unstructured texts to semantic story maps

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 expertfirst 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.

Building a European geothermal information network using a distributed e-Infrastructure

Geothermal data are published using different IT services,formats and content representations,and can refer to both regional and global scale information.Geothermal stakeholders search for information with different aims.E-Infrastructures are collaborative platforms that address this diversity of aims and data representations.In this paper,we present a prototype for a European Geothermal Information Platform that uses INSPIRE recommendations and an e-Infrastructure(D4Science)to collect,aggregate and share data sets from different European data contributors,thus enabling stakeholders to retrieve and process a large amount of data.Our system merges segmented and national realities into one common framework.We demonstrate our approach by describing a platform that collects data from Italian,French,Hungarian,Swiss and Icelandic geothermal data providers.

Detecting patterns of climate change in long-term forecasts of marine environmental parameters

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 crosscorrelation 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.