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description Publicationkeyboard_double_arrow_right Article 2023 ItalyPublisher:MDPI AG Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusAuthors: Simona Cicero; Massimo Guarascio; Antonio Guerrieri; Simone Mungari;Simona Cicero; Massimo Guarascio; Antonio Guerrieri; Simone Mungari;In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is the possibility of enhancing different crucial aspects of life within these buildings, including energy efficiency, safety, health, and occupant comfort. In particular, the fast progress in the field of the Internet of Things has yielded exponential growth in the number of connected smart devices and, consequently, increased the volume of data generated and exchanged. However, traditional Cloud-Computing platforms have exhibited limitations in their capacity to handle and process the continuous data exchange, leading to the rise of new computing paradigms, such as Edge Computing and Fog Computing. In this new complex scenario, advanced Artificial Intelligence and Machine Learning can play a key role in analyzing the generated data and predicting unexpected or anomalous events, allowing for quickly setting up effective responses against these unexpected events. To the best of our knowledge, current literature lacks Deep-Learning-based approaches specifically devised for guaranteeing safety in IoT-Based Smart Buildings. For this reason, we adopt an unsupervised neural architecture for detecting anomalies, such as faults, fires, theft attempts, and more, in such contexts. In more detail, in our proposal, data from a sensor network are processed by a Sparse U-Net neural model. The proposed approach is lightweight, making it suitable for deployment on the edge nodes of the network, and it does not require a pre-labeled training dataset. Experimental results conducted on a real-world case study demonstrate the effectiveness of the developed solution.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s23239331&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s23239331&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2023 ItalyPublisher:IEEE Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusKhan, Irfanullah; Delicato, Flavia C.; Greco, Emilio; Guarascio, Massimo; Guerrieri, Antonio; Spezzano, Giandomenico;Energy efficiency and energy saving have become crucial issues in the face of increasing energy demand, the need for sustainable solutions, and concerns about climate change. Buildings, as significant contributors to energy consumption and greenhouse gas emissions, require effective measures for energy optimization that can also be reached by predicting the usage of the building spaces. This paper introduces a data-driven approach combining Internet of Things sensors, Machine Learning, Edge computing, and Federated Learning to predict multi-occupancy in buildings. The proposed approach is used on real data from the ICAR-CNR IoT Laboratory in order to extract insights into occupancy patterns within a multi-occupant environment. Finally, a comparative analysis conducted by varying Federated Learning configurations demonstrates the robustness of the solution.
CNR ExploRA arrow_drop_down https://doi.org/10.1109/dasc/p...Conference object . 2023 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/dasc/picom/cbdcom/cy59711.2023.10361520&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert CNR ExploRA arrow_drop_down https://doi.org/10.1109/dasc/p...Conference object . 2023 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/dasc/picom/cbdcom/cy59711.2023.10361520&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object 2023 ItalyPublisher:Zenodo Funded by:EC | DESIRA, EC | SoBigData-PlusPlus, EC | EOSC-Pillar +1 projectsEC| DESIRA ,EC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| Blue CloudMorteza Arezoumandan; Leonardo Candela; Donatella Castelli; Ali Ghannadrad; Dario Mangione; Pasquale Pagano;Virtual Research Environments, Science Gateways and Virtual Laboratories are systems aiming at serving the needs of their designated communities of practice by providing them with a working environment for performing their tasks. These systems have been proposed and exploited in diverse application domains and scopes ranging from education to simulation, collaboration, and open science. This paper analyses the literature published from 2010 to start characterising this manifold family of systems. In particular, the study identified and analysed a corpus of 1167 research papers to highlight their distribution over time, the most frequent publication venues and the characterising topics.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7883103&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!visibility 40visibility views 40 download downloads 27 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7883103&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 EnglishPublisher:Zenodo Funded by:EC | Blue Cloud, EC | EOSC-Pillar, EC | SoBigData-PlusPlus +1 projectsEC| Blue Cloud ,EC| EOSC-Pillar ,EC| SoBigData-PlusPlus ,EC| DESIRAAuthors: Candela, Leonardo; Castelli, Donatella; Mangione, Dario;Candela, Leonardo; Castelli, Donatella; Mangione, Dario;Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows. The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘workflow ID’ and ‘URL’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively. The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified: 'used/open science infrastructure/virtual' If a workflow relies on a virtual open infrastructure (yes/no) 'used/open science infrastructure/physical' If a workflow relies on a physical open infrastructure (yes/no) 'used/open scientific knowledge/open source software' If a workflow relies on open source software (yes/no) 'used/open scientific knowledge/open hardware' If a workflow relies on open hardware (yes/no) 'used/open scientific knowledge/open research data' If a workflow (re)uses open research data (yes/no) 'used/open scientific knowledge/open educational resources' If a workflow (re)uses open educational resources (yes/no) 'produced/open scientific knowledge/(open access) scientific publication' If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no) 'produced/open scientific knowledge/open source software' If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no) 'produced/open scientific knowledge/open research data' If a workflow envisages the release of open research data (yes/no) 'produced/open scientific knowledge/open educational resources' If a workflow envisages the release of open educational resources (yes/no) 'transparency/transparency type' degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise) 'transparency/sharing type' workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.) 'collaboration/collaboration implementation' If a workflow implements collaborative practices (yes/no) 'collaboration/open engagement of societal actors/crowdfunding' If a workflow envisages crowdfunding (yes/no) 'collaboration/open engagement of societal actors/crowdsourcing' If a workflow envisages crowdsourcing (yes/no) 'collaboration/open engagement of societal actors/scientific volunteering' If a workflow envisages scientific volunteering (yes/no) 'collaboration/open engagement of societal actors/citizen and participatory science' If a workflow envisages citizen and participatory science (yes/no) 'collaboration/open dialogue with other knowledge systems/indigenous peoples' If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no) 'collaboration/open dialogue with other knowledge systems/marginalised scholars' If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no) 'collaboration/open dialogue with other knowledge systems/local communities' If a workflow envisages the establishment of a dialogue with local communities (yes/no) 'assessment' If a workflow implements assessment processes for the evaluation of the research products created (yes/no) 'automation' If a workflow includes automated processes (yes/no)
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7340704&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 31visibility views 31 download downloads 27 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7340704&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 ItalyPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | TEACHING, EC | SoBigData-PlusPlus, EC | MARVEL +1 projectsEC| TEACHING ,EC| SoBigData-PlusPlus ,EC| MARVEL ,EC| HumanE-AI-NetAuthors: Pietro Cassara; Alberto Gotta; Lorenzo Valerio;Pietro Cassara; Alberto Gotta; Lorenzo Valerio;Autonomous vehicles (AVs) generate a massive amount of multi-modal data that, once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, through two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data. This work is partially supported by the MIUR PON project OK-INSAID (GA #ARS01_00917) and by the H2020 projects TEACHING (GA #871385), HumanAI-Net, (GA #952026), MARVEL (GA #957337), and So-BigData++ (GA #871042).
IEEE Transactions on... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tvt.2022.3178612&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 81visibility views 81 download downloads 83 Powered bymore_vert IEEE Transactions on... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tvt.2022.3178612&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 Italy EnglishPublisher:Zenodo Funded by:EC | Blue Cloud, EC | SoBigData-PlusPlus, EC | EOSC-Pillar +1 projectsEC| Blue Cloud ,EC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| DESIRAArezoumandan, M.; Candela, L.; Castelli, D.; Ghannadrad, A.; Mangione, D.; Pagano, P.;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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6481182&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 137visibility views 137 download downloads 76 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6481182&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 ItalyPublisher:Wiley Funded by:EC | SoBigData-PlusPlus, EC | EOSC-Pillar, EC | AGINFRA PLUS +1 projectsEC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| AGINFRA PLUS ,EC| Blue CloudAuthors: Assante, Massimiliano; Candela, Leonardo; Castelli, Donatella; Cirillo, Roberto; +10 AuthorsAssante, Massimiliano; Candela, Leonardo; Castelli, Donatella; Cirillo, Roberto; Coro, Gianpaolo; Dell'Amico, Andrea; Frosini, Luca; Lelii, Lucio; Lettere, Marco; Mangiacrapa, Francesco; Pagano, Pasquale; Panichi, Giancarlo; Piccioli, Tommaso; Sinibaldi, Fabio;doi: 10.1002/cpe.6925
AbstractVirtual research environments are systems called to serve the needs of their designated communities of practice. Every community of practice is a group of people dynamically aggregated by the willingness to collaborate to address a given research question. The virtual research environment provides its users with seamless access to the resources of interest (namely, data and services) no matter what and where they are. Developing a virtual research environment thus to guarantee its uptake from the community of practice is a challenging task. In this article, we advocate how the co‐creation driven approach promoted by D4Science has proven to be effective. In particular, we present the co‐creation options supported, discuss how diverse communities of practice have exploited these options, and give some usage indicators on the created VREs.
ISTI Open Portal arrow_drop_down Concurrency and Computation Practice and ExperienceArticle . 2022 . Peer-reviewedLicense: Wiley Online Library User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/cpe.6925&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!visibility 169visibility views 169 download downloads 49 Powered bymore_vert ISTI Open Portal arrow_drop_down Concurrency and Computation Practice and ExperienceArticle . 2022 . Peer-reviewedLicense: Wiley Online Library User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/cpe.6925&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 EnglishPublisher:Zenodo Funded by:EC | Blue Cloud, EC | SoBigData-PlusPlus, EC | EOSC-Pillar +1 projectsEC| Blue Cloud ,EC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| DESIRAAuthors: Mangione, Dario; Candela, Leonardo; Castelli, Donatella;Mangione, Dario; Candela, Leonardo; Castelli, Donatella;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
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6037508&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 176visibility views 176 download downloads 156 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6037508&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2022 Italy English Funded by:EC | SoBigData-PlusPlus, EC | Blue Cloud, EC | EOSC-Pillar +1 projectsEC| SoBigData-PlusPlus ,EC| Blue Cloud ,EC| EOSC-Pillar ,EC| DESIRAAuthors: Candela L.; Castelli D.; Mangione D.;Candela L.; Castelli D.; Mangione D.;Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.
All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::21c556dc23165c662b9265f3d533d65d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::21c556dc23165c662b9265f3d533d65d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2022 Italy English Funded by:EC | SoBigData-PlusPlus, EC | DESIRA, EC | EOSC-Pillar +1 projectsEC| SoBigData-PlusPlus ,EC| DESIRA ,EC| EOSC-Pillar ,EC| Blue CloudAuthors: Mangione D.; Candela L.; Castelli D.;Mangione D.; Candela L.; Castelli D.;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.
All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::3f92563b4a472fab526db151b6df61ac&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::3f92563b4a472fab526db151b6df61ac&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article 2023 ItalyPublisher:MDPI AG Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusAuthors: Simona Cicero; Massimo Guarascio; Antonio Guerrieri; Simone Mungari;Simona Cicero; Massimo Guarascio; Antonio Guerrieri; Simone Mungari;In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is the possibility of enhancing different crucial aspects of life within these buildings, including energy efficiency, safety, health, and occupant comfort. In particular, the fast progress in the field of the Internet of Things has yielded exponential growth in the number of connected smart devices and, consequently, increased the volume of data generated and exchanged. However, traditional Cloud-Computing platforms have exhibited limitations in their capacity to handle and process the continuous data exchange, leading to the rise of new computing paradigms, such as Edge Computing and Fog Computing. In this new complex scenario, advanced Artificial Intelligence and Machine Learning can play a key role in analyzing the generated data and predicting unexpected or anomalous events, allowing for quickly setting up effective responses against these unexpected events. To the best of our knowledge, current literature lacks Deep-Learning-based approaches specifically devised for guaranteeing safety in IoT-Based Smart Buildings. For this reason, we adopt an unsupervised neural architecture for detecting anomalies, such as faults, fires, theft attempts, and more, in such contexts. In more detail, in our proposal, data from a sensor network are processed by a Sparse U-Net neural model. The proposed approach is lightweight, making it suitable for deployment on the edge nodes of the network, and it does not require a pre-labeled training dataset. Experimental results conducted on a real-world case study demonstrate the effectiveness of the developed solution.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s23239331&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s23239331&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2023 ItalyPublisher:IEEE Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusKhan, Irfanullah; Delicato, Flavia C.; Greco, Emilio; Guarascio, Massimo; Guerrieri, Antonio; Spezzano, Giandomenico;Energy efficiency and energy saving have become crucial issues in the face of increasing energy demand, the need for sustainable solutions, and concerns about climate change. Buildings, as significant contributors to energy consumption and greenhouse gas emissions, require effective measures for energy optimization that can also be reached by predicting the usage of the building spaces. This paper introduces a data-driven approach combining Internet of Things sensors, Machine Learning, Edge computing, and Federated Learning to predict multi-occupancy in buildings. The proposed approach is used on real data from the ICAR-CNR IoT Laboratory in order to extract insights into occupancy patterns within a multi-occupant environment. Finally, a comparative analysis conducted by varying Federated Learning configurations demonstrates the robustness of the solution.
CNR ExploRA arrow_drop_down https://doi.org/10.1109/dasc/p...Conference object . 2023 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/dasc/picom/cbdcom/cy59711.2023.10361520&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert CNR ExploRA arrow_drop_down https://doi.org/10.1109/dasc/p...Conference object . 2023 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/dasc/picom/cbdcom/cy59711.2023.10361520&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object 2023 ItalyPublisher:Zenodo Funded by:EC | DESIRA, EC | SoBigData-PlusPlus, EC | EOSC-Pillar +1 projectsEC| DESIRA ,EC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| Blue CloudMorteza Arezoumandan; Leonardo Candela; Donatella Castelli; Ali Ghannadrad; Dario Mangione; Pasquale Pagano;Virtual Research Environments, Science Gateways and Virtual Laboratories are systems aiming at serving the needs of their designated communities of practice by providing them with a working environment for performing their tasks. These systems have been proposed and exploited in diverse application domains and scopes ranging from education to simulation, collaboration, and open science. This paper analyses the literature published from 2010 to start characterising this manifold family of systems. In particular, the study identified and analysed a corpus of 1167 research papers to highlight their distribution over time, the most frequent publication venues and the characterising topics.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7883103&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!visibility 40visibility views 40 download downloads 27 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7883103&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 EnglishPublisher:Zenodo Funded by:EC | Blue Cloud, EC | EOSC-Pillar, EC | SoBigData-PlusPlus +1 projectsEC| Blue Cloud ,EC| EOSC-Pillar ,EC| SoBigData-PlusPlus ,EC| DESIRAAuthors: Candela, Leonardo; Castelli, Donatella; Mangione, Dario;Candela, Leonardo; Castelli, Donatella; Mangione, Dario;Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows. The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘workflow ID’ and ‘URL’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively. The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified: 'used/open science infrastructure/virtual' If a workflow relies on a virtual open infrastructure (yes/no) 'used/open science infrastructure/physical' If a workflow relies on a physical open infrastructure (yes/no) 'used/open scientific knowledge/open source software' If a workflow relies on open source software (yes/no) 'used/open scientific knowledge/open hardware' If a workflow relies on open hardware (yes/no) 'used/open scientific knowledge/open research data' If a workflow (re)uses open research data (yes/no) 'used/open scientific knowledge/open educational resources' If a workflow (re)uses open educational resources (yes/no) 'produced/open scientific knowledge/(open access) scientific publication' If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no) 'produced/open scientific knowledge/open source software' If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no) 'produced/open scientific knowledge/open research data' If a workflow envisages the release of open research data (yes/no) 'produced/open scientific knowledge/open educational resources' If a workflow envisages the release of open educational resources (yes/no) 'transparency/transparency type' degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise) 'transparency/sharing type' workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.) 'collaboration/collaboration implementation' If a workflow implements collaborative practices (yes/no) 'collaboration/open engagement of societal actors/crowdfunding' If a workflow envisages crowdfunding (yes/no) 'collaboration/open engagement of societal actors/crowdsourcing' If a workflow envisages crowdsourcing (yes/no) 'collaboration/open engagement of societal actors/scientific volunteering' If a workflow envisages scientific volunteering (yes/no) 'collaboration/open engagement of societal actors/citizen and participatory science' If a workflow envisages citizen and participatory science (yes/no) 'collaboration/open dialogue with other knowledge systems/indigenous peoples' If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no) 'collaboration/open dialogue with other knowledge systems/marginalised scholars' If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no) 'collaboration/open dialogue with other knowledge systems/local communities' If a workflow envisages the establishment of a dialogue with local communities (yes/no) 'assessment' If a workflow implements assessment processes for the evaluation of the research products created (yes/no) 'automation' If a workflow includes automated processes (yes/no)
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7340704&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 31visibility views 31 download downloads 27 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7340704&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 ItalyPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | TEACHING, EC | SoBigData-PlusPlus, EC | MARVEL +1 projectsEC| TEACHING ,EC| SoBigData-PlusPlus ,EC| MARVEL ,EC| HumanE-AI-NetAuthors: Pietro Cassara; Alberto Gotta; Lorenzo Valerio;Pietro Cassara; Alberto Gotta; Lorenzo Valerio;Autonomous vehicles (AVs) generate a massive amount of multi-modal data that, once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, through two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data. This work is partially supported by the MIUR PON project OK-INSAID (GA #ARS01_00917) and by the H2020 projects TEACHING (GA #871385), HumanAI-Net, (GA #952026), MARVEL (GA #957337), and So-BigData++ (GA #871042).
IEEE Transactions on... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tvt.2022.3178612&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 81visibility views 81 download downloads 83 Powered bymore_vert IEEE Transactions on... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tvt.2022.3178612&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 Italy EnglishPublisher:Zenodo Funded by:EC | Blue Cloud, EC | SoBigData-PlusPlus, EC | EOSC-Pillar +1 projectsEC| Blue Cloud ,EC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| DESIRAArezoumandan, M.; Candela, L.; Castelli, D.; Ghannadrad, A.; Mangione, D.; Pagano, P.;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.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6481182&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 137visibility views 137 download downloads 76 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6481182&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 ItalyPublisher:Wiley Funded by:EC | SoBigData-PlusPlus, EC | EOSC-Pillar, EC | AGINFRA PLUS +1 projectsEC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| AGINFRA PLUS ,EC| Blue CloudAuthors: Assante, Massimiliano; Candela, Leonardo; Castelli, Donatella; Cirillo, Roberto; +10 AuthorsAssante, Massimiliano; Candela, Leonardo; Castelli, Donatella; Cirillo, Roberto; Coro, Gianpaolo; Dell'Amico, Andrea; Frosini, Luca; Lelii, Lucio; Lettere, Marco; Mangiacrapa, Francesco; Pagano, Pasquale; Panichi, Giancarlo; Piccioli, Tommaso; Sinibaldi, Fabio;doi: 10.1002/cpe.6925
AbstractVirtual research environments are systems called to serve the needs of their designated communities of practice. Every community of practice is a group of people dynamically aggregated by the willingness to collaborate to address a given research question. The virtual research environment provides its users with seamless access to the resources of interest (namely, data and services) no matter what and where they are. Developing a virtual research environment thus to guarantee its uptake from the community of practice is a challenging task. In this article, we advocate how the co‐creation driven approach promoted by D4Science has proven to be effective. In particular, we present the co‐creation options supported, discuss how diverse communities of practice have exploited these options, and give some usage indicators on the created VREs.
ISTI Open Portal arrow_drop_down Concurrency and Computation Practice and ExperienceArticle . 2022 . Peer-reviewedLicense: Wiley Online Library User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/cpe.6925&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!visibility 169visibility views 169 download downloads 49 Powered bymore_vert ISTI Open Portal arrow_drop_down Concurrency and Computation Practice and ExperienceArticle . 2022 . Peer-reviewedLicense: Wiley Online Library User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/cpe.6925&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 EnglishPublisher:Zenodo Funded by:EC | Blue Cloud, EC | SoBigData-PlusPlus, EC | EOSC-Pillar +1 projectsEC| Blue Cloud ,EC| SoBigData-PlusPlus ,EC| EOSC-Pillar ,EC| DESIRAAuthors: Mangione, Dario; Candela, Leonardo; Castelli, Donatella;Mangione, Dario; Candela, Leonardo; Castelli, Donatella;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
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6037508&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 176visibility views 176 download downloads 156 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.6037508&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2022 Italy English Funded by:EC | SoBigData-PlusPlus, EC | Blue Cloud, EC | EOSC-Pillar +1 projectsEC| SoBigData-PlusPlus ,EC| Blue Cloud ,EC| EOSC-Pillar ,EC| DESIRAAuthors: Candela L.; Castelli D.; Mangione D.;Candela L.; Castelli D.; Mangione D.;Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.
All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::21c556dc23165c662b9265f3d533d65d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::21c556dc23165c662b9265f3d533d65d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2022 Italy English Funded by:EC | SoBigData-PlusPlus, EC | DESIRA, EC | EOSC-Pillar +1 projectsEC| SoBigData-PlusPlus ,EC| DESIRA ,EC| EOSC-Pillar ,EC| Blue CloudAuthors: Mangione D.; Candela L.; Castelli D.;Mangione D.; Candela L.; Castelli D.;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.
All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::3f92563b4a472fab526db151b6df61ac&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=cnr_________::3f92563b4a472fab526db151b6df61ac&type=result"></script>'); --> </script>
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