2023
Journal article
Open Access
An exploratory approach to data driven knowledge creation
Thanos C., Meghini C., Bartalesi V., Coro G.
This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.Source: Journal of big data 10 (2023). doi:10.1186/s40537-023-00702-x
DOI: 10.1186/s40537-023-00702-x
Metrics:
Thanos C., Meghini C., Bartalesi V., Coro G.
This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.Source: Journal of big data 10 (2023). doi:10.1186/s40537-023-00702-x
DOI: 10.1186/s40537-023-00702-x
Metrics:
See at:
journalofbigdata.springeropen.com 
| ISTI Repository | CNR ExploRA
2023
Report
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
An exploratory approach to archaeological knowledge production
Thanos C., Meghini C., Bartalesi V., Coro G.
The current scientific context is characterized by intensive digitization of the research outcomes and by the creation of data infrastructures for the systematic publication of datasets and data services. Several relationships can exist among these outcomes. Some of them are explicit, e.g. the relationships of spatial or temporal similarity, whereas others are hidden, e.g. the relationship of causality. By materializing these hidden relationships through a linking mechanism, several patterns can be established. These knowledge patterns may lead to the discovery of information previously unknown. A new approach to knowledge production can emerge by following these patterns. This new approach is exploratory because by following these patterns, a researcher can get new insights into a research problem. In the paper, we report our effort to depict this new exploratory approach using Linked Data and Semantic Web technologies (RDF, OWL). As a use case, we apply our approach to the archaeological domain.Source: International journal on digital libraries (Internet) (2022). doi:10.1007/s00799-022-00324-3
DOI: 10.1007/s00799-022-00324-3
Metrics:
Thanos C., Meghini C., Bartalesi V., Coro G.
The current scientific context is characterized by intensive digitization of the research outcomes and by the creation of data infrastructures for the systematic publication of datasets and data services. Several relationships can exist among these outcomes. Some of them are explicit, e.g. the relationships of spatial or temporal similarity, whereas others are hidden, e.g. the relationship of causality. By materializing these hidden relationships through a linking mechanism, several patterns can be established. These knowledge patterns may lead to the discovery of information previously unknown. A new approach to knowledge production can emerge by following these patterns. This new approach is exploratory because by following these patterns, a researcher can get new insights into a research problem. In the paper, we report our effort to depict this new exploratory approach using Linked Data and Semantic Web technologies (RDF, OWL). As a use case, we apply our approach to the archaeological domain.Source: International journal on digital libraries (Internet) (2022). doi:10.1007/s00799-022-00324-3
DOI: 10.1007/s00799-022-00324-3
Metrics:
See at:
ISTI Repository | link.springer.com 
| CNR ExploRA
2022
Report
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2022 activities of the research group.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2022 activities of the research group.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.Source: ISTI Annual Report, ISTI-2021-AR/003, pp.1–34, 2021
DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.Source: ISTI Annual Report, ISTI-2021-AR/003, pp.1–34, 2021
DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Report
Open Access
AIMH research activities 2020
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.Source: ISTI Annual Report, ISTI-2020-AR/001, 2020
DOI: 10.32079/isti-ar-2020/001
Metrics:
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.Source: ISTI Annual Report, ISTI-2020-AR/001, 2020
DOI: 10.32079/isti-ar-2020/001
Metrics:
See at:
ISTI Repository | CNR ExploRA
2019
Journal article
Open Access
A data model-independent approach to big research data integration
Barralesi Lenzi V., Meghini C., Thanos C.
The paper addresses the data integration problem in the context of the scientific domain. The main characteristics of the big research data that make the traditional approach of data integration unfeasible are presented. Two new emerging practices, i.e. an exploratory approach to data seeking and an empiricist epistemological approach to knowledge creation, are discussed. Based on these considerations, we present a new paradigm of data integration and an application ontology that supports it. The ontology is based on five types of events and every event is extensionally modelled as an input/output operation on the involved data entity. The strong point of the ontology and of the whole approach to data integration is that no assumption is made on the data models in which the databases or the views are expressed. This provides a level of generality that successfully deals with the heterogeneity of the domain.Source: International journal of metadata, semantics and ontologies (Print) 13 (2019): 330–345. doi:10.1504/IJMSO.2019.102680
DOI: 10.1504/ijmso.2019.102680
DOI: 10.1504/ijmso.2019.10024347
Metrics:
Barralesi Lenzi V., Meghini C., Thanos C.
The paper addresses the data integration problem in the context of the scientific domain. The main characteristics of the big research data that make the traditional approach of data integration unfeasible are presented. Two new emerging practices, i.e. an exploratory approach to data seeking and an empiricist epistemological approach to knowledge creation, are discussed. Based on these considerations, we present a new paradigm of data integration and an application ontology that supports it. The ontology is based on five types of events and every event is extensionally modelled as an input/output operation on the involved data entity. The strong point of the ontology and of the whole approach to data integration is that no assumption is made on the data models in which the databases or the views are expressed. This provides a level of generality that successfully deals with the heterogeneity of the domain.Source: International journal of metadata, semantics and ontologies (Print) 13 (2019): 330–345. doi:10.1504/IJMSO.2019.102680
DOI: 10.1504/ijmso.2019.102680
DOI: 10.1504/ijmso.2019.10024347
Metrics:
See at:
ISTI Repository | International Journal of Metadata Semantics and Ontologies | International Journal of Metadata Semantics and Ontologies | www.inderscienceonline.com | CNR ExploRA
2019
Conference article
Open Access
Big research data integration
Bartalesi Lenzi V., Meghini C., Thanos C.
The paper presents a vision about a new paradigm of data integration in the context of the scientific world, where data integration is instrumental in exploratory studies carried out by research teams. It briefly overviews the technological challenges to be faced in order to successfully carry out the traditional approach to data integration. Then, three important application scenarios are described in terms of their main characteristics that heavily influence the data integration process. The first application scenario is characterized by the need of large enterprises to combine information from a variety of heterogeneous data sets developed autonomously, managed and maintained independently from the others in the enterprises. The second application scenario is characterized by the need of many organizations to combine information from a large number of data sets dynamically created, distributed worldwide and available on the Web. The third application scenario is characterized by the need of scientists and researchers to connect each others research data as new insight is revealed by connections between diverse research data sets. The paper highlights the fact that the characteristics of the second and third application scenarios make unfeasible the traditional approach to data integration, i.e., the design of a global schema and mappings between the local schemata and the global schema. The focus of the paper is on the data integration problem in the context of the third application scenario. A new paradigm of data integration is proposed based on the emerging new empiricist scientific method, i.e., data driven research and the new data seeking paradigm, i.e., data exploration. Finally, a generic scientific application scenario is presented for the purpose of better illustrating the new data integration paradigm, and a concise list of actions that must be performed in order to successfully carry out the new paradigm of big research data integration is described.Source: International Workshop on Information Search, Integration, and Personalization, pp. 23–37, Fukuoka, Japan, 14/05/ 2018, 15/05/2018
DOI: 10.1007/978-3-030-30284-9_2
Metrics:
Bartalesi Lenzi V., Meghini C., Thanos C.
The paper presents a vision about a new paradigm of data integration in the context of the scientific world, where data integration is instrumental in exploratory studies carried out by research teams. It briefly overviews the technological challenges to be faced in order to successfully carry out the traditional approach to data integration. Then, three important application scenarios are described in terms of their main characteristics that heavily influence the data integration process. The first application scenario is characterized by the need of large enterprises to combine information from a variety of heterogeneous data sets developed autonomously, managed and maintained independently from the others in the enterprises. The second application scenario is characterized by the need of many organizations to combine information from a large number of data sets dynamically created, distributed worldwide and available on the Web. The third application scenario is characterized by the need of scientists and researchers to connect each others research data as new insight is revealed by connections between diverse research data sets. The paper highlights the fact that the characteristics of the second and third application scenarios make unfeasible the traditional approach to data integration, i.e., the design of a global schema and mappings between the local schemata and the global schema. The focus of the paper is on the data integration problem in the context of the third application scenario. A new paradigm of data integration is proposed based on the emerging new empiricist scientific method, i.e., data driven research and the new data seeking paradigm, i.e., data exploration. Finally, a generic scientific application scenario is presented for the purpose of better illustrating the new data integration paradigm, and a concise list of actions that must be performed in order to successfully carry out the new paradigm of big research data integration is described.Source: International Workshop on Information Search, Integration, and Personalization, pp. 23–37, Fukuoka, Japan, 14/05/ 2018, 15/05/2018
DOI: 10.1007/978-3-030-30284-9_2
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2017
Journal article
Open Access
White paper on research data service discoverability
Thanos C., Klan F., Kriticos K., Candela L.
This White Paper reports the outcome of a Workshop on "Research Data Service Discoverability" held in the island of Santorini (GR) on 21-22 April 2016 and organized in the context of the EU funded Project "RDA-E3". The Workshop addressed the main technical problems that hamper an efficient and effective discovery of Research Data Services (RDSs) based on appropriate semantic descriptions of their functional and non-functional aspects. In the context of this White Paper, by RDSs are meant those data services that manipulate/transform research datasets for the purpose of gaining insight into complicated issues. In this White Paper, the main concepts involved in the discovery process of RDSs are defined; the RDS discovery process is illustrated; the main technologies that enable the discovery of RDSs are described; and a number of recommendations are formulated for indicating future research directions and making an automatic RDS discovery feasible.Source: Publications 5 (2017). doi:10.3390/publications5010001
DOI: 10.3390/publications5010001
Project(s): RDA Europe
Metrics:
Thanos C., Klan F., Kriticos K., Candela L.
This White Paper reports the outcome of a Workshop on "Research Data Service Discoverability" held in the island of Santorini (GR) on 21-22 April 2016 and organized in the context of the EU funded Project "RDA-E3". The Workshop addressed the main technical problems that hamper an efficient and effective discovery of Research Data Services (RDSs) based on appropriate semantic descriptions of their functional and non-functional aspects. In the context of this White Paper, by RDSs are meant those data services that manipulate/transform research datasets for the purpose of gaining insight into complicated issues. In this White Paper, the main concepts involved in the discovery process of RDSs are defined; the RDS discovery process is illustrated; the main technologies that enable the discovery of RDSs are described; and a number of recommendations are formulated for indicating future research directions and making an automatic RDS discovery feasible.Source: Publications 5 (2017). doi:10.3390/publications5010001
DOI: 10.3390/publications5010001
Project(s): RDA Europe
Metrics:
See at:
Publications | ISTI Repository | Publications | Publications | doaj.org | CNR ExploRA
2017
Journal article
Open Access
Research data reusability: conceptual foundations, barriers and enabling technologies
Thanos C.
High-throughput scientific instruments are generating massive amounts of data. Today, one of the main challenges faced by researchers is to make the best use of the world's growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice. By data (re)usability, we mean the ease of using data for legitimate scientific research by one or more communities of research (consumer communities) that is produced by other communities of research (producer communities). Data (re)usability allows the reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. It has four main dimensions: policy, legal, economic and technological. The paper addresses the technological dimension of data reusability. The conceptual foundations of data reuse as well as the barriers that hamper data reuse are presented and discussed. The data publication process is proposed as a bridge between the data author and user and the relevant technologies enabling this process are presented.Source: Publications 5 (2017). doi:10.3390/publications5010002
DOI: 10.3390/publications5010002
Metrics:
Thanos C.
High-throughput scientific instruments are generating massive amounts of data. Today, one of the main challenges faced by researchers is to make the best use of the world's growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice. By data (re)usability, we mean the ease of using data for legitimate scientific research by one or more communities of research (consumer communities) that is produced by other communities of research (producer communities). Data (re)usability allows the reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. It has four main dimensions: policy, legal, economic and technological. The paper addresses the technological dimension of data reusability. The conceptual foundations of data reuse as well as the barriers that hamper data reuse are presented and discussed. The data publication process is proposed as a bridge between the data author and user and the relevant technologies enabling this process are presented.Source: Publications 5 (2017). doi:10.3390/publications5010002
DOI: 10.3390/publications5010002
Metrics:
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Publications | ISTI Repository | Publications | Publications | CNR ExploRA
2017
Journal article
Open Access
The Key Role of the DELOS Network of Excellence in Establishing Digital Libraries as a Research Field in Europe
Thanos C., Casarosa V.
It was only in the mid-1990s that Digital Libraries became a research topic in Europe. The European Union was then instrumental in supporting and promoting the subject. One key element of that support was the establishment of the DELOS Network of Excellence. Its central role in the development of digital libraries in the period 1997-2007 in Europe and its contributions areoutlined in this paper.Source: LIBER quarterly 26 (2017): 296–307. doi:10.18352/lq.10165
DOI: 10.18352/lq.10165
Metrics:
Thanos C., Casarosa V.
It was only in the mid-1990s that Digital Libraries became a research topic in Europe. The European Union was then instrumental in supporting and promoting the subject. One key element of that support was the establishment of the DELOS Network of Excellence. Its central role in the development of digital libraries in the period 1997-2007 in Europe and its contributions areoutlined in this paper.Source: LIBER quarterly 26 (2017): 296–307. doi:10.18352/lq.10165
DOI: 10.18352/lq.10165
Metrics:
See at:
Liber Quarterly: The Journal of European Research Libraries | liberquarterly.eu | DOAJ-Articles | ISTI Repository | DOAJ-Articles | DOAJ-Articles | CNR ExploRA
2016
Journal article
Open Access
A vision for open cyber-scholarly infrastructures
Thanos C.
The characteristics of modern science, i.e., data-intensive, multidisciplinary, open, and heavily dependent on Internet technologies, entail the creation of a linked scholarly record that is online and open. Instrumental in making this vision happen is the development of the next generation of Open Cyber-Scholarly Infrastructures (OCIs), i.e., enablers of an open, evolvable, and extensible scholarly ecosystem. The paper delineates the evolving scenario of the modern scholarly record and describes the functionality of future OCIs as well as the radical changes in scholarly practices including new reading, learning, and information-seeking practices enabled by OCIs.Source: Publications 4 (2016). doi:10.3390/publications4020013
DOI: 10.3390/publications4020013
Metrics:
Thanos C.
The characteristics of modern science, i.e., data-intensive, multidisciplinary, open, and heavily dependent on Internet technologies, entail the creation of a linked scholarly record that is online and open. Instrumental in making this vision happen is the development of the next generation of Open Cyber-Scholarly Infrastructures (OCIs), i.e., enablers of an open, evolvable, and extensible scholarly ecosystem. The paper delineates the evolving scenario of the modern scholarly record and describes the functionality of future OCIs as well as the radical changes in scholarly practices including new reading, learning, and information-seeking practices enabled by OCIs.Source: Publications 4 (2016). doi:10.3390/publications4020013
DOI: 10.3390/publications4020013
Metrics:
See at:
Publications | Publications | Publications | CNR ExploRA
2016
Report
Open Access
RDA Europe - Planning and activities report
Thanos C., Zoppi F.
This WP4 Planning and Activities Report describes the actions that are intended within WP4 in the coming 12 months to engage with the practitioners and to organize uptake. It is the second report of this type describing what has been done and what is being planned.Source: Project report, RDA Europa, Deliverable D4.6, 2016
Project(s): RDA Europe
Thanos C., Zoppi F.
This WP4 Planning and Activities Report describes the actions that are intended within WP4 in the coming 12 months to engage with the practitioners and to organize uptake. It is the second report of this type describing what has been done and what is being planned.Source: Project report, RDA Europa, Deliverable D4.6, 2016
Project(s): RDA Europe
See at:
ISTI Repository | CNR ExploRA
2016
Report
Open Access
RDA Europe - Planning and activities report 3
Thanos C., Zoppi F.
This WP4 Planning and Activities Report describes the actions that are intended within WP4 in the coming 12 months to engage with the practitioners and to organize uptake. It is the second report of this type describing what has been done and what is being planned.Source: Project report, RDA Europa, Deliverable D4.7, 2016
Project(s): RDA Europe
Thanos C., Zoppi F.
This WP4 Planning and Activities Report describes the actions that are intended within WP4 in the coming 12 months to engage with the practitioners and to organize uptake. It is the second report of this type describing what has been done and what is being planned.Source: Project report, RDA Europa, Deliverable D4.7, 2016
Project(s): RDA Europe
See at:
ISTI Repository | CNR ExploRA
2015
Contribution to journal
Open Access
Scientific data sharing and re-use
Thanos C., Rauber A.
Source: ERCIM news online edition 100 (2015): 13–13.
Thanos C., Rauber A.
Source: ERCIM news online edition 100 (2015): 13–13.
See at:
ercim-news.ercim.eu | CNR ExploRA
2015
Contribution to conference
Unknown
Scientific Data Reusability: Concepts, Impediments and Enabling Technologies
Thanos C.
High-throughput scientific instruments are generating massive amounts of data. Today one of the main challenges faced by researchers is to make the best use of the world's growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice, as it allows reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. The paper addresses the technological dimension of data reusability: the scientific data universe, the impediments of data (re)reuse; the data publication process as a bridge between data author and user and the relevant technologies enabling this process.Source: Digital Presentation and Preservation of Cultural and Scientific Heritage International Conference, Veliko Tarnovo, Bulgaria, 28-30/09/2015
Thanos C.
High-throughput scientific instruments are generating massive amounts of data. Today one of the main challenges faced by researchers is to make the best use of the world's growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice, as it allows reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. The paper addresses the technological dimension of data reusability: the scientific data universe, the impediments of data (re)reuse; the data publication process as a bridge between data author and user and the relevant technologies enabling this process.Source: Digital Presentation and Preservation of Cultural and Scientific Heritage International Conference, Veliko Tarnovo, Bulgaria, 28-30/09/2015
See at: CNR ExploRA
2015
Report
Open Access
RDA Europe - Planning and activities report 1
Thanos C., Castelli D.
This WP4 Planning and Activities Report describes the actions that are intended within WP4 in the coming 12 months to engage with the practitioners and to organize uptake. It is the first report of this type and an update will be written before the end of this first 12 month period.Project(s): RDA EUROPE
Thanos C., Castelli D.
This WP4 Planning and Activities Report describes the actions that are intended within WP4 in the coming 12 months to engage with the practitioners and to organize uptake. It is the first report of this type and an update will be written before the end of this first 12 month period.Project(s): RDA EUROPE
See at:
ISTI Repository | CNR ExploRA
2014
Journal article
Open Access
Mediation: The technological foundation of modern science
Thanos C.
Modern science is increasingly data-intensive, multidisciplinary, and network-centric. There is an emerging consensus among the members of the academic research community that the practices of this new science paradigm should be congruent with "open science". This entails that the bonanza of research data, the wide availability of algorithms, data tools, and data services produced by the members of the research community must be discoverable, understandable, and usable by overcoming all kinds of heterogeneity and logical inconsistencies. The main concept for coping with the many dimensions of heterogeneity and logical inconsistency is mediation. Mediation is achieved by mediators or brokers. These are software modules that exploit encoded knowledge about certain datasets, data services, and user needs in order to implement an intermediary service. A mediating environment is an environment that provides a core set of intermediary services. Mediation should be a distinct functionality of future research data infrastructures. This paper surveys the different levels of interoperability, i.e., exchangeability, compatibility, and usability, their properties and relationships, mediation concepts, functions, and intermediary services. The current interoperability landscape is also illustrated. Finally, the paper advocates the need for mediating environments to be supported by future research data infrastructures and envisions that one of the most important features of future research data infrastructures will be mediation software.Source: Data science journal 13 (2014): 88–105.
Thanos C.
Modern science is increasingly data-intensive, multidisciplinary, and network-centric. There is an emerging consensus among the members of the academic research community that the practices of this new science paradigm should be congruent with "open science". This entails that the bonanza of research data, the wide availability of algorithms, data tools, and data services produced by the members of the research community must be discoverable, understandable, and usable by overcoming all kinds of heterogeneity and logical inconsistencies. The main concept for coping with the many dimensions of heterogeneity and logical inconsistency is mediation. Mediation is achieved by mediators or brokers. These are software modules that exploit encoded knowledge about certain datasets, data services, and user needs in order to implement an intermediary service. A mediating environment is an environment that provides a core set of intermediary services. Mediation should be a distinct functionality of future research data infrastructures. This paper surveys the different levels of interoperability, i.e., exchangeability, compatibility, and usability, their properties and relationships, mediation concepts, functions, and intermediary services. The current interoperability landscape is also illustrated. Finally, the paper advocates the need for mediating environments to be supported by future research data infrastructures and envisions that one of the most important features of future research data infrastructures will be mediation software.Source: Data science journal 13 (2014): 88–105.
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www.scopus.com | CNR ExploRA
2014
Journal article
Open Access
The future of digital scholarship
Thanos C.
This paper advocates that connectivity is the technological foundation of digital scholarship and argues that the characteristics of modern science, i.e. data-centric, multidisciplinary, open, network-centric and heavily dependent on internet technologies entail the creation of a linked, semantically enhanced scholarly record composed of interconnected discipline-specific literature and scientific, social, and humanities data spaces. The changing scenario of the scholarly record is illustrated by describing the principal transformations now being enabled by advanced linking and semantic technologies. The main functionality of a cyberscholarship infrastructure is described, i.e. the ability to effectively and efficiently support a linking environment. (C) 2014 The Authors. Published by Elsevier B.V.Source: Procedia computer science 38 (2014): 22–27. doi:10.1016/j.procs.2014.10.005
DOI: 10.1016/j.procs.2014.10.005
Metrics:
Thanos C.
This paper advocates that connectivity is the technological foundation of digital scholarship and argues that the characteristics of modern science, i.e. data-centric, multidisciplinary, open, network-centric and heavily dependent on internet technologies entail the creation of a linked, semantically enhanced scholarly record composed of interconnected discipline-specific literature and scientific, social, and humanities data spaces. The changing scenario of the scholarly record is illustrated by describing the principal transformations now being enabled by advanced linking and semantic technologies. The main functionality of a cyberscholarship infrastructure is described, i.e. the ability to effectively and efficiently support a linking environment. (C) 2014 The Authors. Published by Elsevier B.V.Source: Procedia computer science 38 (2014): 22–27. doi:10.1016/j.procs.2014.10.005
DOI: 10.1016/j.procs.2014.10.005
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Procedia Computer Science | www.sciencedirect.com | CNR ExploRA
2013
Journal article
Open Access
A vision towards Scientific Communication Infrastructures
Castelli D., Manghi P., Thanos C.
The two pillars of the modern scientic communication are Data Centers and Research Digital Libraries, whose technologies and admin staff support researchers at storing, curating, sharing, and discovering the data and the publications they produce. Being realized to maintain and give access to the results of complementary phases of the scientificc research process, such systems are poorly integrated with one another and generally do not rely on the strengths of theother. Today, such a gap hampers achieving the objectives of the modern scientic communication, that is,publishing, interlinking, and discovery of all outcomesof the research process, from the experimental and observational datasets to the final paper. In this work, we envision that instrumental to bridge the gap is the construction of "Scientic Communication Infrastructures". The main goal of these infrastructures is to facilitate interoperability between Data Centers and Research Digital Libraries and to provide services that simplify the implementation of the large variety of modern scientific communication patterns.Source: International journal on digital libraries (Print) 13 (2013): 165–169. doi:10.1007/s00799-013-0106-7
DOI: 10.1007/s00799-013-0106-7
Project(s): OPENAIREPLUS
Metrics:
Castelli D., Manghi P., Thanos C.
The two pillars of the modern scientic communication are Data Centers and Research Digital Libraries, whose technologies and admin staff support researchers at storing, curating, sharing, and discovering the data and the publications they produce. Being realized to maintain and give access to the results of complementary phases of the scientificc research process, such systems are poorly integrated with one another and generally do not rely on the strengths of theother. Today, such a gap hampers achieving the objectives of the modern scientic communication, that is,publishing, interlinking, and discovery of all outcomesof the research process, from the experimental and observational datasets to the final paper. In this work, we envision that instrumental to bridge the gap is the construction of "Scientic Communication Infrastructures". The main goal of these infrastructures is to facilitate interoperability between Data Centers and Research Digital Libraries and to provide services that simplify the implementation of the large variety of modern scientific communication patterns.Source: International journal on digital libraries (Print) 13 (2013): 165–169. doi:10.1007/s00799-013-0106-7
DOI: 10.1007/s00799-013-0106-7
Project(s): OPENAIREPLUS
Metrics:
See at:
link.springer.com | International Journal on Digital Libraries | CNR ExploRA