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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

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.

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?' EU Member States have adopted several initiatives to establish a legal and technical framework for digital identity. The European Commission has facilitated this development by offering guidance and promoting interoperable solutions through frameworks such as eIDAS and solutions developed within the European Interoperability Framework. At the same time, two years of COVID-19 pandemic have led at once to an acceleration of digital identity projects, and mounting concerns that widespread data collection and availability can lead to the risk of privacy violations, citizen profiling and mass surveillance. This session will explore the opportunities and challenges of emerging digital identity and digital payments, including the privacy, security concerns as well as the outstanding opportunities for inclusive growth, resilient and sustainable solutions for the society of the future. The discussion will also cover emerging attempts to develop joint European solutions for digital identity, including the recent joint declaration between the governments of Finland and Germany to support the progress of the proposed regulation on European digital identity, and to accelerate the development of joint European solutions based on digital identity.

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.

Developing long-term visions through participatory approaches can be very useful to explore different possible scenarios and pathways to reach desirable futures. This brief report describes a participatory process conducted in the Emilia-Romagna region (Italy) to develop a long-term vision for rural areas of 2040. This approach consisted in: (i) interviews and a focus group conducted with a Multi-actor Platform (MAP) composed of experts from science-society-policy sectors, (ii) an on-line questionnaire addressing a larger number of rural stakeholders of the region. Mixing expert-based consultation through the MAP with a more inclusive consultation approach, resulted to be an effective method to build long-term visions in the very heterogeneous rural context of the Emilia-Romagna. However, this study only constitutes a preliminary step into a more elaborated backcasting approach

The CWASI database is a unique and harmonized database of water footprint (WF) and virtual water trade (VWT) data in the period 1961-2016 (detailed trade starts in 1986). The database includes 370 agricultural products among which are crops, crop-based and animal-based products, both edible and non-edible. The database has been developed within the EU-funded CWASI project. {"references": ["Reference paper is: Tamea, S., Tuninetti, M., Soligno, I., and Laio, F (2021, IN PRESS), Earth Syst. Sci. Data, 13, 1\u201327, https://doi.org/10.5194/essd-13-1-2021"]}

The CWASI database is a unique and harmonized database of water footprint (WF) and virtual water trade (VWT) data in the period 1961-2016. The database includes hundreds of agricultural products among which are crops, crop-based and animal-based products, both edible and non-edible. The database has been developed within the EU-funded CWASI project.

NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine. This is the data to the same-titled Data paper, which can be found at https://doi.org/10.3390/data5010001. Along with auxilary files for the cloudremoval package, and example scripts on how to access chunks of the data. The files contain: python-cloudremoval-aux-data.tar.gz : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal python-example-data-access.html : Example script how to access parts of the data using python R-example-data-access.html : Example script how to access parts of the data using R zenodo_01_original.tar.gz : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (https://doi.org/10.3390/data5010001), and for algorithm at https://doi.org/10.3390/rs5010110. zenodo_02_cloudremoval.tar.gz : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper. The maps are GeoTIFF with integer based values: 0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata Version history: 1.0.0 : initial upload 1.0.1 : changes after revision of Data paper 1.0.2 : added example scripts