Datasets accompanying the paper “Virtual Research Environments Ethnography: a Preliminary Study”, a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories. While for legal reasons we can not share the original datasets obtained by querying the databases, since they include copyrighted data, we can share the two datasets derived from the query results and the two topic modelling datasets. The dataset “main_dataset.csv” consists of the merged query results from ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink databases. It is structured into six columns: (i) doi; (ii) title; (iii) content_type; (iv) publication year; (v) keyword_search; (vi) DB. The ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘content_type’ label refers to the different and normalised typologies of resources: (a) Article; (b) Book, (c) Book Chapter; (d) Chapter; (e) Chapter ReferenceWorkEntry; (f) Conference Paper; (g) Conference Review; (h) Early Access Articles; (i) Editorial; (j) Erratum; (k) Letter; (l) Magazines; (m) Masters Thesis; (n) Note; (o) Ph.D. Thesis; (p) Retracted; (q) Review; (r) Short Survey; (s) Standards. (c) and (d) refer to the same type of entry (they are used in different databases), while in the case of (e) we observed that it is used in the Springer database to refer mainly to encyclopaedic entries. The ‘keyword_search’ label is used for identifying the keyword group used for formulating the query: (a) science gateway | scientific gateway; (b) virtual laboratory | Vlab; or (c) virtual research environment. The ‘DB’ label indicates the provenance of the entries from one of the five databases we selected for our study: (a) ACM; (b) IEEE; (c) ScienceDirect; (d) scopus; and (e) Springer, identifying the ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink respectively. The dataset “filtered_dataset.csv” consists of the deduplicated and filtered entries (journal articles and conference papers from 2010 onward, with a DOI assigned) from the “main_dataset.csv” we used as the final dataset for answering our research questions. It is structured into ten columns: (i) doi; (ii) title; (iii) venue; (iv) publication_year; (v) content_type; (vi) abstract; (vii) keywords; (viii) science gateway | scientific gateway; (ix) virtual laboratory | Vlab; and (x) virtual research environment. As for the previous dataset, the ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘venue’ label is used for indicating the conference or the journal the entries refer to. The values derive from the original query results. The ‘abstract’ and ‘keyword’ labels are used for the abstracts and the keywords associated with the entries. The values are mainly derived from the original query results, as we integrated the missing ones by querying OpenAIRE. The ‘science gateway | scientific gateway’, ‘virtual laboratory | Vlab’ and ‘virtual research environment’ labels indicate the connection between the entries and the keyword group used for denoting them. The values are binary (1 if the keywords belong to the group, 0 if they do not). The datasets “sg_vlab_vre_topics_datasets.csv” and “sgvlabvre_topics_dataset.csv” consist of the three datasets and of the unique dataset resulting from topic modelling, the first (corpus divided into three datasets) and the second analysis (corpus as a whole) respectively. They share the same structure: (i) Topic; (ii) #studies; (iii) Representative word; (iv) Representative word weight. The ‘Topic’ label is used for the topic denomination and the values consist of an alphanumeric string indicating the dataset and the progressive topic number: (a) SG, for the scientific gateway dataset; (b) VRE, for the virtual research environment dataset; (c) VLAB, for the virtual laboratory dataset; and (d) A, for the corpus as a whole. The ‘#studies’ label indicates the number of studies contributing to each topic. The ‘Representative word’ and ‘Representative word weight’ labels are used for denoting the keywords describing each topic and their weights respectively.
Datasets accompanying the study “A Taxonomy of Tools and Approaches for FAIRification” on the tools and approaches emerging from stakeholders’ experiences adopting the FAIR principles in practice. Datasets: queryResults.csv Description The dataset consists of the query results returned by OpenAIRE Explore and defines the corpus at the base of our study. Structure 11 columns: Query Type of query entered FAIR, FAIRification (all fields) OpenAIRE subjects (subject) Result Type [OpenAIRE label] Type of the research output (publication|data|software|other) Title [OpenAIRE label] Authors [OpenAIRE label] Publication Year [OpenAIRE label] DOI [OpenAIRE label] Download from [OpenAIRE label] Type [OpenAIRE label] Subtype of the research output Journal [OpenAIRE label] Funder|Project Name (GA Number) [OpenAIRE label] Access [OpenAIRE label] Access rights publicationsTools.csv Description The dataset pairs the tools/services extracted from the corpus to their respective source. Structure 2 columns: source reference to the publication or software citation name name of the tool/service/technology toolsAll.csv Description The dataset lists all the unique tool/service entries, distinguishing between those that were considered relevant for the study (further categorised into tools, technologies or services) and those that were excluded. Structure 3 columns: entryType entry categorisation (tool|service|technology|excluded) name name of the tool/service/technology URL URL of the tool/service web page or description toolsType.csv Description Classification of the tools/services/technologies into the study-defined classes. Structure 19 columns: name name of the tool/service/technology URL URL of the tool/service web page or description GUPRI helper - GUPRI creation and management service ‘class - subclass’ of the tool/service/technology GUPRI helper - GUPRI Indexing and discovery service ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata editor ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata extractor ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata tracker ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata validator ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata assistant ‘class - subclass’ of the tool/service/technology Indexing and discovery service - registry ‘class - subclass’ of the tool/service/technology Indexing and discovery service - repository ‘class - subclass’ of the tool/service/technology Indexing and discovery service - Indexing and discovery service finder ‘class - subclass’ of the tool/service/technology Converter - metadata ‘class - subclass’ of the tool/service/technology Converter - data ‘class - subclass’ of the tool/service/technology Licence helper ‘class’ of the tool/service/technology Assessment tool - automated ‘class - subclass’ of the tool/service/technology Assessment tool - manual ‘class - subclass’ of the tool/service/technology Assessment tool - Assessment tool finder ‘class - subclass’ of the tool/service/technology DMP tool ‘class’ of the tool/service/technology toolsFAIR.csv Description The dataset relates the tool/service/technology to the FAIR principles it enables. Structure 12 columns: name name of the tool/service/technology URL URL of the tool/service web page or description F1 reference to the FAIR principle F2 reference to the FAIR principle F3 reference to the FAIR principle F4 reference to the FAIR principle A generic reference to the accessibility principles (see the paper) I1 reference to the FAIR principle I3 reference to the FAIR principle R1.1 reference to the FAIR principle R1.2 reference to the FAIR principle R1.3 reference to the FAIR principle toolsScope.csv Description Since the FAIR principles have been specified for different types of resources ((meta)data, semantic artefacts, software and workflows), the dataset correlates the tool/service/technology and the types of FAIR-specific resources it covers. Structure 6 columns: name name of the tool/service/technology URL URL of the tool/service web page or description (meta)data reference to the FAIR-specific resource semantic artefact reference to the FAIR-specific resource software reference to the FAIR-specific resource workflow reference to the FAIR-specific resource toolsDomain.csv Description Classification of the tools/services/technologies into the Frascati framework-defined domains. Structure 9 columns: name name of the tool/service/technology URL URL of the tool/service web page or description cross-domain domain Agricultural and veterinary sciences domain Engineering and technology domain Humanities and the arts domain Medical and health sciences domain Natural sciences domain Social sciences domain
https://www.eesc.europa.eu/sites/default/files/files/qe-01-21-440-en-n.pdf As the voice of organised civil society in Europe, the European Economic and Social Committee (EESC) brings together employers, trade unions and the diverse interests represented in the economic, civic, professional and cultural fields. By enabling civil-society organisations from the Member States to express their views at the European level, the EESC contributes to strengthening the democratic legitimacy and effectiveness of the European Union and helps to ensure that European policies and legislation are a better fit with the economic, social and civic circumstances on the ground. For more than 60 years, the Committee has fostered dialogue and consensus between the sectors that make up European society. EESC members represent a vast range of interests: community and youth organisations, consumer and professional associations, environmental campaigners, associations of disabled people, and many more. It was to recognise civil society’s best efforts towards European identity and citizenship that the EESC launched its annual Civil Society Prize back in 2006. The EUR-50 000 prize focuses on a different topic every year. This year’s theme is climate action, celebrating effective and creative initiatives that promote a just transition towards a low-carbon, climate-resilient economy. The EESC received over 50 entries from 24 EU Member States. They demonstrate value and creativity in a variety of areas, including reforestation, sustainable tourism, greening of industrial zones, youth activism, inclusion of people with disabilities, and cooling overheated cities. This brochure presents the five winning projects and gives an overview of the innovative approaches that civil society organisations are taking to tackle the climate emergency.
Project: EC | MOVING (862739), EC | Blue Cloud (862409)
In the context of the MOVING (MOuntain Valorisation through INterconnectedness and Green growth) project, we released an open-source software - the MOVING Story Map Building and Visualization Tool (SMBVT) - that allows users to create and visualise story maps within a collaborative environment and using a user-friendly Web interface. The tool uses Semantic Web technologies and the Narrative Ontology to represent the stories of the MOVING mountain Value Chains. The MOVING community access SMBVT through The MOVING story map Virtual Research Environment and creates the events of the story. For each event, the user can add: a title, a textual description, start and end dates, the geographic coordinates, a media object (i.e. a video or image), notes, and digital objects. The tool takes Wikidata as reference KB and assigns Wikidata Internationalized Resource Identifiers (IRIs) to the story components (i.e. the entities that take part in an event). All the knowledge collected by SMBVT is stored in a JSON Postgres DB. When a story is completed, the tool automatically creates the corresponding visualisation using StoryMapJS library and makes available a corresponding URL that can be freely shared. Finally, SMBVT saves the collected knowledge as a Web Ontology Language (OWL) graph and publishes it as a Linked Open Data.
This contribution was delivered on 5 May 2022 on the occasion of the hybrid 2022 edition of EUI State of the Union on ‘A Europe fit for the next generation?' The European economy faces three major challenges. It must achieve a transition to a digital economy, to a low-carbon and sustainable economy, and to a more inclusive economy. Making Europe’s economy more resilient has become even more important with the Russian war on Ukraine. We already know that the next phase of transformation will be felt even more acutely by citizens. We also know that the social impact of these transformations will vary across European countries and regions. Too great a disparity in economic opportunities and development possibilities jeopardizes the attractiveness of the European Union and its ability to act – both internally and externally. In the past, the European Single Market has made a decisive contribution to the development of the member states and the well-being of Europeans. But the benefits of the single market are not evenly distributed among the countries and regions of the EU. The post-pandemic economic recovery will not even out the disparities by itself. And while the overall economic fallout from Russia’s war on Ukraine is still difficult to predict, the impact will be different for different EU member states and regions. What are the likely effects of European initiatives and policies to green and digitize the European economy on regional development and cohesion in Europe? And what can European policymakers do to strengthen European cohesion along the way.
Pastoralism in Asia features a variety of agro-ecological and socio-cultural settings. From Russian Siberia to Indian drylands, the continent is home to large and diverse pastoral territories and communities. Policies and legislation regulating rangeland governance and livestock production are of great concern in the region, as they affect the livelihoods of significant parts of the population. Herding communities across the continent are also highly heterogeneous in their historical trajectories, and socio-political institutions; during the twentieth century, Asian rangelands underwent important political reconfigurations that brought specific consequences for the territories and lives of pastoralists. The Socialist and the capital-intensive Green revolutions that have characterised the recent history of different portions of the region with the goal of modernising agricultural systems have generated significant and differentiated forms of uncertainty for most rural communities. Agrarian reforms, large-scale infrastructure, subsidy and loan schemes, along with integration into market dynamics, have been instrumental in supporting the stabilization of livestock production and the sedentarisation of herding communities, as part of their broader incorporation into the global economic and political arena. The overall impact has been one of widespread dispossession, dislocation, and marginalization, forcing pastoralists to reconfigure herd management and mobility strategies, and to constantly negotiate their access to grazing resources, market options, and income opportunities, including through land use conversion and migration. This review of past and evolving policy frameworks in different parts of Asia shows that, despite contrasting differences in ideological perspectives and development trajectories, the dismantling of pastoral resource management has always been purported as a prerequisite for modernisation, through the multiple and divergent agendas of increasing livestock production, preserving rangeland ecosystems and improving local welfare. However, the engagement with State- and market-driven dynamics has rarely been favourable to pastoralists. The political and institutional uncertainty resulting from these approaches has contributed substantially to altering patterns of resource governance for local communities, who have been seldom invited to participate in policy planning and societal debates, even though their livelihoods, land and livestock are often the primary focus of development programmes and modernisation strategies.
This contribution was delivered on 5 May 2022 on the occasion of the hybrid 2022 edition of EUI State of the Union on ‘A Europe fit for the next generation?' Digitalisation offers small firms in Europe and the rest of the world the prospect of engaging in international trade, either as part of a global value chain (B2B) or providing services to consumers directly (B2C). Different models of regulation of digital trade have emerged around the world, resulting in a fragmentation of rules which can potentially impact costs and the ability of firms and consumers to sell (and buy) digital products and online services across borders. The session will draw on the ongoing Digital Trade Integration Project, a CIVICA research project led by the EUI, that collects information on digital trade regulation, exploring options to overcome regulatory fragmentation, including negotiation of digital partnership agreements and regulatory equivalence mechanisms.
The rangelands of West Asia and North Africa (WANA) region - which includes the Maghreb and Mashreq, Turkey and other countries of the Arabian Peninsula - are conducive to different patterns of pastoral resource management, due to the prevailing arid and mountainous conditions. Environmental change in the region is quite intense, resulting from population growth, shifts in land use and climate dynamics, and is one of the main drivers of socio-economic and political transformation in the region. In most WANA countries livestock rearing is a primary source of livelihood for a large segment ofthe population, and the governance of rangeland management and livestock trade are high priority issues for the national and regional political economy. Despite a fragmented and conflicting political setup that affects regional economic integration and the establishment of a common institutional framework, development trajectories regarding agriculture and food security have converged over time. Throughout the region, there have been repeated attempts to convert herding communities into stable and controllable producers through their incorporation into state and market mechanisms. Patterns of herd management and livestock mobility have been profoundly reconfigured, and while the movement of animals is increasingly restricted as feed and water are brought to them, the mobility of rural dwellers has intensified, through intense migration flows that are contributing to major transformations in local societies. Over time, development approaches, institutional arrangements and market dynamics have proven inconsistent in addressing the long-term needs of rural producers and ecosystems. Particularly in the arid and remote pastoral regions, local livelihoods have significantly deteriorated in recent decades, and are now increasingly shaped by processes that take place outside the realm of livestock production and very often beyond regional boundaries. The reconfiguration of land, livestock and labour regimes has generated tensions and risks that have weakened the capacity of pastoralist communities to deal with evolving uncertainties. The recent history of WANA drylands is one of strained economic development, stressed community networks and degraded ecosystems; the broader implications of the political and economic marginalisation of drylands have significant impacts for the entire WANA region and society.
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