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In 2020, the SHERPA project contributed to the debate on the Long-Term Vision for Rural Areas by presenting the key issues identified by the 20 regional and national SHERPA Multi-Actor Platforms (MAPs), and by the EU-level MAP. The MAPs identified their desired visions for 2040, the enabling factors to achieve those visions, the challenges to overcome and the opportunities to be seized. MAPs implemented a Delphi method, which was comprised of desk research and the use of quantitative data (e.g., development indicators, demography etc.), interviews with key informants, and the design, implementation and analysis of online surveys. The outputs of MAP cycle 1 are MAP Position Papers and one SHERPA Position Paper. This Discussion Paper provides a methodological guidance for the second MAP cycle on how to implement a foresight exercise, which is the second stage of the work on the vision. The objective of the foresight exercise is threefold: (i) testing a methodology for prompting more ambitious reflections on the future of their rural area/community and guiding strategic thinking among stakeholders involved, (ii) developing pathways of change which provide inspiration and a basis for decision-making and (iii) producing a MAP Position Paper that enables further analysis and aggregated policy recommendations by the SHERPA consortium. Results will inform SHERPA’s contribution to the EU LTVRA initiative and will be documented into MAP Position Papers as well as analysed in one single document, the SHERPA Position Paper. It will also continue feeding the debate on the long-term vision for rural areas initiated in September 2019, which will result in a Communication of the European Commission aimed to be published in summer 2021. The document presents the SHERPA approach, and logic of, the foresight exercise. The majority of the document provides the main information needed to prepare for and virtually run the workshops, broken down into five main steps.

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.

The paper set out to demonstrate the opportunities and critical issues arising from the dematerialisation of cultural heritage, also in the light of the novelties of PNRR

Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.

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.

The foresight exercise1 carried out by the Tuscany MAP represented a first attempt to align the work achieved in the MAP cycle with the policy cycle of the National Strategy for Inner Areas (SNAI). The SNAI is an integrated policy addressing issues related to depopulation and low access to basic services by enhancing the preconditions for territorial development and promoting local development projects. The rural area selected by the MAP for this task is the SNAI Pilot area of Garfagnana-Lunigiana-Media Valle del Serchio-Appennino Pistoiese. All four of the localities encompassed are, each to their specific extent, affected by remoteness, depopulation (especially of young people), ageing, loss of functions and lack of territorial control, with ensuing social and economic costs. Inaction in addressing these main challenges could potentially strengthen the vicious circle of marginalisation (Strategia d’Area, 2018).

La Quarta Rivoluzione Industriale ha annunciato una serie di trasformazioni culturali, economiche e sociali di profondo rilievo. L’ampia accelerazione tecnologica che stiamo vivendo, indubbiamente, crea un valore enorme, aumentando la produttività e facilitando il benessere collettivo. Tuttavia, la tecnica, oltre a stravolgere il mon- do del lavoro e della produzione, l'organizzazione del si- stema economico e, più in generale, la quotidianità, allo stesso tempo, opera una rivoluzione della coscienza uma- na, della comprensione del mondo interiore ed esteriore degli individui. Le tecnologie digitali non sono soltanto strumenti che si limitano a modificare il modo in cui inte- ragiamo con il mondo, ma sono, soprattutto, sistemi che influenzano sempre di più il modo in cui comprendiamo il mondo e ci rapportiamo ad esso, così come il modo in cui concepiamo noi stessi e interagiamo tra noi. Oggi, in maniera progressivamente accelerata, si dà atto di un’e- rosione sistematica dell’esperienza puramente umana da parte della tecnica, in particolar modo nell’ambito del nascere, vivere e morire. Seppur la tecnologia moltiplichi esponenzialmente le connessioni e le interazioni tra per- sone ed organizzazioni, tuttavia, non ha incoraggiato tendenze che vadano nella direzione del benessere collet- tivo. Inoltre, il processo di deterioramento del sistema va- loriale non ha risparmiato il diritto, fenomeno umano per eccellenza. Pertanto, un sistema che non registra, come in passato, un forte legame tra progresso tecnico-scientifico e creazione di valori che orientino al bene collettivo, ri- chiede l’innovazione parallela dei modelli di pensiero, nonché la ridefinizione delle categorie con le quali i sape- ri umanistici e scientifici approcciano la realtà. A partire dall’analisi di questo contesto, questo contributo afferma l’idea della necessarietà di etica e filosofia quali elemen- ti imprescindibili ai fini della sostenibilità della quarta rivoluzione industriale. I principi etici, invero, educano la collettività ad agire valutando gli effetti delle proprie azioni sulla natura e sugli altri, e nel lungo periodo. L’e- tica, infatti, quale filosofia morale o scienza del bene, stabilisce relazioni strutturali tra l’agire umano e i mezzi per conseguirlo, affinché si mantenga il “bene in sé” quale fine ultimo. The Fourth Industrial Revolution introduced a series of profound cultural, economic and social transformations. The wide technological accele- ration we are experiencing, undoubtedly, creates enormous value, increasing productivity and faci- litating collective well-being. However, technology, in addition to upsetting the world of work and production, the organization of the economic sy- stem and, more generally, everyday life, at the same time, brings a revolution in human consciousness, in the comprehension of the inner and outer world of individuals. Digital technologies are not just to- ols that only change the way we interact with the world, but they are, above all, systems that increa- singly influence the way we understand the world and relate to it, as well as the way we interact with it, the way we conceive of ourselves and interact with each other. Today, in a progressively accelera- ted manner, we face a systematic erosion of purely human experience by technology, especially in the context of being born, living and dying. In particu- lar, although technology exponentially multiplies the connections and interactions between people and organizations, however, it has not encouraged trends that go in the direction of collective well- being. Furthermore, the process of deterioration of the value system has not spared law, a human phe- nomenon par excellence. Therefore, a system that does not register, as in the past, a strong link betwe- en technical-scientific progress and the creation of values that orient towards the collective good, requires the parallel innovation of the models of thought, as well as the redefinition of the catego- ries with which humanities and sciences approach reality. Starting from the analysis of this context, this contribution affirms the idea of the necessity of ethics and philosophy as essential elements for the sustainability of the fourth industrial revolu- tion. Ethical principles, indeed, educate the com- munity to act by evaluating the effects of its actions on nature and on the others, and in the long term. Ethics, in fact, as a moral philosophy or science of good, establishes structural relationships between human action and the means to achieve it, so that "good in itself " is maintained as the ultimate goal.

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.