2017
Doctoral thesis
Open Access
Data Flow Quality Monitoring in Data Infrastructures
Mannocci A.
In the last decade, a lot of attention worldwide has been brought by researchers, organizations, and funders on the realization ofData Infrastructures (DIs), namely systems supporting researchers with the broad spectrum of resources they need to perform science. DIs are here intended as ICT (eco)systems offering data and processing components which can be combined into data flows so as to enable arbitrarily complex data manipulation actions serving the consumption needs of DI customers, be them humans or machines.Data resulting from the execution of data flows, represent an important asset both for the DI users, typically craving for the information they need, and for the organization (or community) operating the DI, whose existence and cost sustainability depends on the adoption and usefulness of the DI. On the other hand, when operating several data processing data flows over time, several issues, well-known to practitioners, may arise and compromise the behaviour of the DI, and therefore undermine its reliability and generate stakeholders dissatisfaction. Such issues span a plethora of causes, such as(i) the lack of any kind of guarantees (e.g. quality, stability, findability, etc.) from integrated external data sources, typically not under the jurisdiction of the DI; (ii) the occurrence at any abstraction level of subtle, unexpected errors in the data flows; and(iii) the nature in ever changing evolution of the DI, in terms of data flow composition and algorithms/configurations in use.The autonomy of DI components, their use across several data flows, the evolution of end-user requirements over time, make the one of DI data flows a critical environment, subject to the most subtle inconsistencies. Accordingly, DI users demand guarantees, while quality managers are called to provide them, on the "correctness" of the DI data flows behaviour over time, to be somehow quantified in terms of "data quality" and in terms of "processing quality". Monitoring the quality of data flows is therefore a key activity of paramount importance to ensure the up-taking and long term existence of a DI. Indeed, monitoring can detect or anticipate misbehaviours of DI's data flows, in order to prevent and adjust the errors, or at least "formally" justify to the stakeholders the underlying reasons, possibly not due to the DI, of such errors. Not only, monitoring can also be vital for DIs operation, as having hardware and software resources actively employed in processing low quality data can yield inefficient resource allocation and waste of time.However, data flow quality monitoring is further hindered by the "hybrid" nature of such infrastructures, which typically consist of a patchwork of individual components("system of systems") possibly developed by distinct stakeholders with possibly distinct life-cycles, evolving over time, whose interactions are regulated mainly by shared policies agreed at infrastructural level. Due to such heterogeneity, generally DIs are not equipped with built-in monitoring systems in this sense and to date DI quality managers are therefore bound to use combinations of existing tools - with non trivial integration efforts - or to develop and integrate ex-post their own ad-hoc solutions, at high cost of realization and maintenance.In this thesis, we introduce MoniQ, a general-purpose Data Flow Quality Monitoring system enabling the monitoring of critical data flow components, which are routinely checked during and after every run of the data flow against a set of user-defined quality control rules to make sure the data flow meets the expected behaviour and quality criteria over time, as established upfront by the quality manager. MoniQ introduces a monitoring description language capable of (i) describing the semantic and the time ordering of the observational intents and capture the essence of the DI data flows to be monitored; and (ii) describing monitoring intents over the monitoring flows in terms of metrics to be extracted and controls to be ensured. The novelty of the language is that it incorporates the essence of existing data quality monitoring approaches, identifies and captures process monitoring scenarios, and, above all, provides abstractions to represent monitoring scenarios that combine data and process quality monitoring within the scope of a data flow. The study is provided with an extensive analysis of two real-world use cases used as support and validation of the proposed approach, and discusses an implementation of MoniQ providing quality managers with high-level tools to integrate the solution in a DI in an easy, technology transparent and cost efficient way in order to start to get insight out data flows by visualizing the trends of the metrics defined and the outcome of the controls declared against them.Project(s): OPENAIRE
Mannocci A.
In the last decade, a lot of attention worldwide has been brought by researchers, organizations, and funders on the realization ofData Infrastructures (DIs), namely systems supporting researchers with the broad spectrum of resources they need to perform science. DIs are here intended as ICT (eco)systems offering data and processing components which can be combined into data flows so as to enable arbitrarily complex data manipulation actions serving the consumption needs of DI customers, be them humans or machines.Data resulting from the execution of data flows, represent an important asset both for the DI users, typically craving for the information they need, and for the organization (or community) operating the DI, whose existence and cost sustainability depends on the adoption and usefulness of the DI. On the other hand, when operating several data processing data flows over time, several issues, well-known to practitioners, may arise and compromise the behaviour of the DI, and therefore undermine its reliability and generate stakeholders dissatisfaction. Such issues span a plethora of causes, such as(i) the lack of any kind of guarantees (e.g. quality, stability, findability, etc.) from integrated external data sources, typically not under the jurisdiction of the DI; (ii) the occurrence at any abstraction level of subtle, unexpected errors in the data flows; and(iii) the nature in ever changing evolution of the DI, in terms of data flow composition and algorithms/configurations in use.The autonomy of DI components, their use across several data flows, the evolution of end-user requirements over time, make the one of DI data flows a critical environment, subject to the most subtle inconsistencies. Accordingly, DI users demand guarantees, while quality managers are called to provide them, on the "correctness" of the DI data flows behaviour over time, to be somehow quantified in terms of "data quality" and in terms of "processing quality". Monitoring the quality of data flows is therefore a key activity of paramount importance to ensure the up-taking and long term existence of a DI. Indeed, monitoring can detect or anticipate misbehaviours of DI's data flows, in order to prevent and adjust the errors, or at least "formally" justify to the stakeholders the underlying reasons, possibly not due to the DI, of such errors. Not only, monitoring can also be vital for DIs operation, as having hardware and software resources actively employed in processing low quality data can yield inefficient resource allocation and waste of time.However, data flow quality monitoring is further hindered by the "hybrid" nature of such infrastructures, which typically consist of a patchwork of individual components("system of systems") possibly developed by distinct stakeholders with possibly distinct life-cycles, evolving over time, whose interactions are regulated mainly by shared policies agreed at infrastructural level. Due to such heterogeneity, generally DIs are not equipped with built-in monitoring systems in this sense and to date DI quality managers are therefore bound to use combinations of existing tools - with non trivial integration efforts - or to develop and integrate ex-post their own ad-hoc solutions, at high cost of realization and maintenance.In this thesis, we introduce MoniQ, a general-purpose Data Flow Quality Monitoring system enabling the monitoring of critical data flow components, which are routinely checked during and after every run of the data flow against a set of user-defined quality control rules to make sure the data flow meets the expected behaviour and quality criteria over time, as established upfront by the quality manager. MoniQ introduces a monitoring description language capable of (i) describing the semantic and the time ordering of the observational intents and capture the essence of the DI data flows to be monitored; and (ii) describing monitoring intents over the monitoring flows in terms of metrics to be extracted and controls to be ensured. The novelty of the language is that it incorporates the essence of existing data quality monitoring approaches, identifies and captures process monitoring scenarios, and, above all, provides abstractions to represent monitoring scenarios that combine data and process quality monitoring within the scope of a data flow. The study is provided with an extensive analysis of two real-world use cases used as support and validation of the proposed approach, and discusses an implementation of MoniQ providing quality managers with high-level tools to integrate the solution in a DI in an easy, technology transparent and cost efficient way in order to start to get insight out data flows by visualizing the trends of the metrics defined and the outcome of the controls declared against them.Project(s): OPENAIRE
See at:
etd.adm.unipi.it 
| ISTI Repository | CNR ExploRA
2021
Contribution to book
Open Access
Detection, analysis, and prediction of research topics with scientific knowledge graphs
Salatino A., Mannocci A., Osborne F.
Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption. In the last years, we saw the emergence of several publicly-available and large-scale Scientific Knowledge Graphs fostering the development of many data-driven approaches for performing quantitative analyses of research trends. This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology. We discuss the advantages of a solution based on a formal representation of topics and describe how it was applied to produce bibliometric studies and innovative tools for analysing and predicting research dynamics.Source: Predicting the Dynamics of Research Impact, edited by Manolopoulos Y., Vergoulis T., pp. 225–252, 2021
DOI: 10.1007/978-3-030-86668-6_11
Metrics:
Salatino A., Mannocci A., Osborne F.
Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption. In the last years, we saw the emergence of several publicly-available and large-scale Scientific Knowledge Graphs fostering the development of many data-driven approaches for performing quantitative analyses of research trends. This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology. We discuss the advantages of a solution based on a formal representation of topics and describe how it was applied to produce bibliometric studies and innovative tools for analysing and predicting research dynamics.Source: Predicting the Dynamics of Research Impact, edited by Manolopoulos Y., Vergoulis T., pp. 225–252, 2021
DOI: 10.1007/978-3-030-86668-6_11
Metrics:
See at:
arxiv.org | doi.org 
| CNR ExploRA
2013
Conference article
Restricted
Data searchery: preliminary analysis of data sources interlinking
Manghi P., Mannocci A.
The novel e-Science's data-centric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, re-use, and validation of research activities more effective. Scholarly Communication Infrastructures are advocated for bridging such data sources, by offering tools for identification, creation, and navigation of relationships. Since realization and maintenance of such infrastructures is expensive, in this demo we propose a lightweight approach for "preliminary analysis of data source interlinking" to help practitioners at evaluating whether and to what extent realizing them can be effective. We present Data Searchery, a congurable tool enabling users to easily plug-in data sources from different realms with the purpose of cross-relating their objects, be them publications or research data, by identifying relationships between their metadata descriptions.Source: TPDL 2013 - Research and Advanced Technology for Digital Libraries. International Conference on Theory and Practice of Digital Libraries, pp. 458–461, Valletta, Malta, 22-26 September 2013
DOI: 10.1007/978-3-642-40501-3_60
Project(s): RDA EUROPE
Metrics:
Manghi P., Mannocci A.
The novel e-Science's data-centric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, re-use, and validation of research activities more effective. Scholarly Communication Infrastructures are advocated for bridging such data sources, by offering tools for identification, creation, and navigation of relationships. Since realization and maintenance of such infrastructures is expensive, in this demo we propose a lightweight approach for "preliminary analysis of data source interlinking" to help practitioners at evaluating whether and to what extent realizing them can be effective. We present Data Searchery, a congurable tool enabling users to easily plug-in data sources from different realms with the purpose of cross-relating their objects, be them publications or research data, by identifying relationships between their metadata descriptions.Source: TPDL 2013 - Research and Advanced Technology for Digital Libraries. International Conference on Theory and Practice of Digital Libraries, pp. 458–461, Valletta, Malta, 22-26 September 2013
DOI: 10.1007/978-3-642-40501-3_60
Project(s): RDA EUROPE
Metrics:
See at:
link.springer.com | CNR ExploRA
2014
Contribution to book
Restricted
Preliminary analysis of data sources interlinking
Mannocci A. K, Manghi P.
The novel e-Science's data-centric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, re-use, and validation of research activities more effective. Scholarly Communication Infrastructures (SCIs) are advocated for bridging such data sources by offering an overlay of services for identification, creation, and navigation of relationships among objects of different nature. Since realization and maintenance of such infrastructures is in general very cost-consuming, in this paper we propose a lightweight approach for "preliminary analysis of data source interlinking" to help practitioners at evaluating whether and to what extent realizing them can be effective. We present Data Searchery, a configurable tool delivering a service for relating objects across data sources, be them publications or research data, by identifying relationships between their metadata descriptions in real-time.Source: Theory and Practice of Digital Libraries -- TPDL 2013 Selected Workshops, edited by ?ukasz Bolikowski, Vittore Casarosa, Paula Goodale, Nikos Houssos, Paolo Manghi, Jochen Schirrwagen, pp. 53–64. Berlin: Springer, 2014
DOI: 10.1007/978-3-319-08425-1_6
DOI: 10.1007/978-3-319-14226-5_6
Metrics:
Mannocci A. K, Manghi P.
The novel e-Science's data-centric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, re-use, and validation of research activities more effective. Scholarly Communication Infrastructures (SCIs) are advocated for bridging such data sources by offering an overlay of services for identification, creation, and navigation of relationships among objects of different nature. Since realization and maintenance of such infrastructures is in general very cost-consuming, in this paper we propose a lightweight approach for "preliminary analysis of data source interlinking" to help practitioners at evaluating whether and to what extent realizing them can be effective. We present Data Searchery, a configurable tool delivering a service for relating objects across data sources, be them publications or research data, by identifying relationships between their metadata descriptions in real-time.Source: Theory and Practice of Digital Libraries -- TPDL 2013 Selected Workshops, edited by ?ukasz Bolikowski, Vittore Casarosa, Paula Goodale, Nikos Houssos, Paolo Manghi, Jochen Schirrwagen, pp. 53–64. Berlin: Springer, 2014
DOI: 10.1007/978-3-319-08425-1_6
DOI: 10.1007/978-3-319-14226-5_6
Metrics:
See at:
biblioproxy.cnr.it | doi.org | doi.org | CNR ExploRA
2016
Conference article
Open Access
DataQ: a data flow quality monitoring system for aggregative data infrastructures
Mannocci A., Manghi P.
Aggregative Data Infrastructures (ADIs) are information systems offering services to integrate content collected from data sources so as to form uniform and richer information spaces and support communities of users with enhanced access services to such content. The resulting information spaces are an important asset for the target communities, whose services demand for guarantees on their "correctness" and "quality" over time, in terms of the expected content (structure and semantics) and of the processes generating such content. Application-level continuous monitoring of ADIs becomes therefore crucial to ensure validation of quality. However, ADIs are in most of the cases the result of patchworks of software components and services, in some cases developed independently, built over time to address evolving requirements. As such they are not generally equipped with embedded monitoring components and ADI admins must rely on third-party monitoring systems. In this paper we describe DataQ, a general-purpose system for exible and cost-effective data fow quality monitoring in ADIs. DataQ supports ADI admins with a framework where they can (i) represent ADIs data fows and the relative monitoring specification, and (ii) be instructed on how to meet such specification on the ADI side to implement their monitoring functionality.Source: TPDL 2016 - Theory and Practice of Digital Libraries. 20th International Conference, pp. 357–369, Hannover, Germany, September 5-9, 2016
DOI: 10.1007/978-3-319-43997-6_28
Project(s): OpenAIRE2020
Metrics:
Mannocci A., Manghi P.
Aggregative Data Infrastructures (ADIs) are information systems offering services to integrate content collected from data sources so as to form uniform and richer information spaces and support communities of users with enhanced access services to such content. The resulting information spaces are an important asset for the target communities, whose services demand for guarantees on their "correctness" and "quality" over time, in terms of the expected content (structure and semantics) and of the processes generating such content. Application-level continuous monitoring of ADIs becomes therefore crucial to ensure validation of quality. However, ADIs are in most of the cases the result of patchworks of software components and services, in some cases developed independently, built over time to address evolving requirements. As such they are not generally equipped with embedded monitoring components and ADI admins must rely on third-party monitoring systems. In this paper we describe DataQ, a general-purpose system for exible and cost-effective data fow quality monitoring in ADIs. DataQ supports ADI admins with a framework where they can (i) represent ADIs data fows and the relative monitoring specification, and (ii) be instructed on how to meet such specification on the ADI side to implement their monitoring functionality.Source: TPDL 2016 - Theory and Practice of Digital Libraries. 20th International Conference, pp. 357–369, Hannover, Germany, September 5-9, 2016
DOI: 10.1007/978-3-319-43997-6_28
Project(s): OpenAIRE2020
Metrics:
See at:
OpenAIRE | doi.org | link.springer.com | CNR ExploRA
2020
Conference article
Open Access
Open Science Graphs must interoperate!
Aryani A., Fenner M., Manghi P., Mannocci A., Stocker M.
Open Science Graphs (OSGs) are Scientific Knowledge Graphs whose intent is to improve the overall FAIRness of science, by enabling open access to graph representations of metadata about people, artefacts, institutions involved in the research lifecycle, as well as the relationships between these entities, in order to support stakeholder needs, such as discovery, reuse, reproducibility, statistics, trends, monitoring, impact, validation, and assessment. The represented information may span across entities such as research artefacts (e.g. publications, data, software, samples, instruments) and items of their content (e.g. statistical hypothesis tests reported in publications), research organisations, researchers, services, projects, and funders. OSGs include relationships between such entities and sometimes formalised (semantic) concepts characterising them, such as machine-readable concept descriptions for advanced discoverability, interoperability, and reuse. OSGs are generally valuable individually, but would greatly benefit from information exchange across their collections, thereby improving their efficacy to serve stakeholder needs. They could, therefore, reuse and exploit the data aggregation and added value that characterise each OSG, decentralising the effort and capitalising on synergies, as no one-size-fits-all solution exists. The RDA IG on Open Science Graphs for FAIR Data is investigating the motivation and challenges underpinning the realisation of an Interoperability Framework for OSGs. This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) the definition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.Source: International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, pp. 195–206, Lyon, France, 25-27/08/2020
DOI: 10.1007/978-3-030-55814-7_16
Project(s): OpenAIRE-Advance
Metrics:
Aryani A., Fenner M., Manghi P., Mannocci A., Stocker M.
Open Science Graphs (OSGs) are Scientific Knowledge Graphs whose intent is to improve the overall FAIRness of science, by enabling open access to graph representations of metadata about people, artefacts, institutions involved in the research lifecycle, as well as the relationships between these entities, in order to support stakeholder needs, such as discovery, reuse, reproducibility, statistics, trends, monitoring, impact, validation, and assessment. The represented information may span across entities such as research artefacts (e.g. publications, data, software, samples, instruments) and items of their content (e.g. statistical hypothesis tests reported in publications), research organisations, researchers, services, projects, and funders. OSGs include relationships between such entities and sometimes formalised (semantic) concepts characterising them, such as machine-readable concept descriptions for advanced discoverability, interoperability, and reuse. OSGs are generally valuable individually, but would greatly benefit from information exchange across their collections, thereby improving their efficacy to serve stakeholder needs. They could, therefore, reuse and exploit the data aggregation and added value that characterise each OSG, decentralising the effort and capitalising on synergies, as no one-size-fits-all solution exists. The RDA IG on Open Science Graphs for FAIR Data is investigating the motivation and challenges underpinning the realisation of an Interoperability Framework for OSGs. This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) the definition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.Source: International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, pp. 195–206, Lyon, France, 25-27/08/2020
DOI: 10.1007/978-3-030-55814-7_16
Project(s): OpenAIRE-Advance
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2022
Contribution to journal
Open Access
New trends in scientific knowledge graphs and research impact assessment
Manghi P., Mannocci A., Osborne F., Sacharidis D., Salatino A., Vergoulis T.
Source: Quantitative Science Studies 2 (2022): 1296–1300. doi:10.1162/qss_e_00160
DOI: 10.1162/qss_e_00160
Metrics:
Manghi P., Mannocci A., Osborne F., Sacharidis D., Salatino A., Vergoulis T.
Source: Quantitative Science Studies 2 (2022): 1296–1300. doi:10.1162/qss_e_00160
DOI: 10.1162/qss_e_00160
Metrics:
See at:
direct.mit.edu | Quantitative Science Studies | ISTI Repository | CORE (RIOXX-UK Aggregator) | Quantitative Science Studies | CNR ExploRA
2022
Conference article
Open Access
BIP! scholar: a service to facilitate fair researcher assessment
Vergoulis T., Chatzopoulos S., Vichos K., Kanellos I., Mannocci A., Manola N., Manghi P.
In recent years, assessing the performance of researchers has become a burden due to the extensive volume of the existing research output. As a result, evaluators often end up relying heavily on a selection of performance indicators like the h-index. However, over-reliance on such indicators may result in reinforcing dubious research practices, while overlooking important aspects of a researcher's career, such as their exact role in the production of particular research works or their contribution to other important types of academic or research activities (e.g., production of datasets, peer reviewing). In response, a number of initiatives that attempt to provide guidelines towards fairer research assessment frameworks have been established. In this work, we present BIP! Scholar, a Web-based service that offers researchers the opportunity to set up profiles that summarise their research careers taking into consideration well-established guidelines for fair research assessment, facilitating the work of evaluators who want to be more compliant with the respective practices.Source: JCDL'22 - 22nd ACM/IEEE Joint Conference on Digital Libraries, Cologne, Germany, 20-24/06/2022
DOI: 10.1145/3529372.3533296
DOI: 10.48550/arxiv.2205.03152
Project(s): OpenAIRE Nexus
Metrics:
Vergoulis T., Chatzopoulos S., Vichos K., Kanellos I., Mannocci A., Manola N., Manghi P.
In recent years, assessing the performance of researchers has become a burden due to the extensive volume of the existing research output. As a result, evaluators often end up relying heavily on a selection of performance indicators like the h-index. However, over-reliance on such indicators may result in reinforcing dubious research practices, while overlooking important aspects of a researcher's career, such as their exact role in the production of particular research works or their contribution to other important types of academic or research activities (e.g., production of datasets, peer reviewing). In response, a number of initiatives that attempt to provide guidelines towards fairer research assessment frameworks have been established. In this work, we present BIP! Scholar, a Web-based service that offers researchers the opportunity to set up profiles that summarise their research careers taking into consideration well-established guidelines for fair research assessment, facilitating the work of evaluators who want to be more compliant with the respective practices.Source: JCDL'22 - 22nd ACM/IEEE Joint Conference on Digital Libraries, Cologne, Germany, 20-24/06/2022
DOI: 10.1145/3529372.3533296
DOI: 10.48550/arxiv.2205.03152
Project(s): OpenAIRE Nexus
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | dl.acm.org | doi.org | doi.org | CNR ExploRA
2022
Conference article
Closed Access
Sci-K 2022 - International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment
Manghi P., Mannocci A., Osborne F., Sacharidis D., Salatino A., Vergoulis T.
In this paper we present the 2nd edition of the Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2022) workshop. Sci-K aims to explore innovative solutions and ideas for the generation of approaches, data models, and infrastructures (e.g., knowledge graphs) for supporting, directing, monitoring and assessing the scientific knowledge and progress. This edition is also a reflection point as the community is seeking alternative solutions to the now-defunct Microsoft Academic Graph (MAG).Source: WWW 2022 - The ACM Web Conference 2022, pp. 735–738, Lyon, France (Online), 25-29/04/2022
DOI: 10.1145/3487553.3524883
Metrics:
Manghi P., Mannocci A., Osborne F., Sacharidis D., Salatino A., Vergoulis T.
In this paper we present the 2nd edition of the Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2022) workshop. Sci-K aims to explore innovative solutions and ideas for the generation of approaches, data models, and infrastructures (e.g., knowledge graphs) for supporting, directing, monitoring and assessing the scientific knowledge and progress. This edition is also a reflection point as the community is seeking alternative solutions to the now-defunct Microsoft Academic Graph (MAG).Source: WWW 2022 - The ACM Web Conference 2022, pp. 735–738, Lyon, France (Online), 25-29/04/2022
DOI: 10.1145/3487553.3524883
Metrics:
See at:
dl.acm.org | CNR ExploRA
2022
Conference article
Open Access
Open Science and authorship of supplementary material. Evidence from a research community
Mannocci A., Irrera O., Manghi P.
While, in early science, most of the papers were authored by a handful of scientists, modern science is characterised by more extensive collaborations, and the average number of authors per article has increased across many disciplines (Baethge, 2008; Cronin, 2001; Fernandes & Monteiro, 2017; Frandsen & Nicolaisen, 2010; Wren et al., 2007). Indeed, in some fields of science (e.g., High Energy Physics), it is not infrequent to encounter hundreds or thousands of authors co-participating in the same piece of research. Such intricate collaboration patterns make it difficult to establish a correct relationship between contributor and scientific contribution and hence get an accurate and fair reward during research evaluation (Brand, Allen, Altman, Hlava, & Scott, 2015; Vasilevsky et al., 2021; Vergoulis et al., 2022). Thus, as widely known, scientific authorship tends to be a rather hot-button topic in academia, as roughly one-fifth of academic disputes among authors stem from this (Dance, 2012). Open Science, however, has the potential to disrupt such traditional mechanisms by injecting into the "academic market" new kinds of "currency" for credit attribution, merit and impact assessment (Mooney & Newton, 2012; Silvello, 2018). To this end, the new practices of supplementary research data (and software) deposition and citation could be perceived as an opportunity to diversify the attribution portfolio and eventually give credit to the different contributors involved in the diverse phases of the lifecycle within the same research endeavour (Bierer, Crosas, & Pierce, 2017; Brand et al., 2015). While, on the one hand, it is known that authors' ordering tells little or nothing about authors' roles and contributions (Kosmulski, 2012), on the other hand, we argue that variations of any kind in author sets of paired publications and supplementary material can be indicative. Despite being unclear the actual reason behind such a variation, the presence of a fracture between the publication and research data realms might suggest once more that current practices for research assessment and reward should be revised and updated to capture such peculiarities as well. In (Mannocci, Irrera, & Manghi, 2022), we argue that modern Open Science Graphs (OSGs) can be used to analyse whether this is the case or not and understand if the opportunity has been seized already. By offering extensive metadata descriptions of both literature, research data, software, and their semantic relations, OSGs constitute a fertile ground to analyse this phenomenon computationally and thus analyse the emergence of significant patterns. As a preliminary study, in this paper, we conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science3 (MES) research community. The results are promising and suggest our hypothesis is worth exploring further. Indeed, in 702 cases out of 3,075 (22.83%), there are substantial variations between the authors participating in the publication and the authors participating in the supplementary dataset (or software), thus posing the premises for a longitudinal, large-scale analysis of the phenomenon.Source: STI 2022 - 26th International Conference on Science, Technology and Innovation Indicators, Granada, Spain, 7-9/09/2022
DOI: 10.5281/zenodo.6975411
Project(s): OpenAIRE Nexus
Metrics:
Mannocci A., Irrera O., Manghi P.
While, in early science, most of the papers were authored by a handful of scientists, modern science is characterised by more extensive collaborations, and the average number of authors per article has increased across many disciplines (Baethge, 2008; Cronin, 2001; Fernandes & Monteiro, 2017; Frandsen & Nicolaisen, 2010; Wren et al., 2007). Indeed, in some fields of science (e.g., High Energy Physics), it is not infrequent to encounter hundreds or thousands of authors co-participating in the same piece of research. Such intricate collaboration patterns make it difficult to establish a correct relationship between contributor and scientific contribution and hence get an accurate and fair reward during research evaluation (Brand, Allen, Altman, Hlava, & Scott, 2015; Vasilevsky et al., 2021; Vergoulis et al., 2022). Thus, as widely known, scientific authorship tends to be a rather hot-button topic in academia, as roughly one-fifth of academic disputes among authors stem from this (Dance, 2012). Open Science, however, has the potential to disrupt such traditional mechanisms by injecting into the "academic market" new kinds of "currency" for credit attribution, merit and impact assessment (Mooney & Newton, 2012; Silvello, 2018). To this end, the new practices of supplementary research data (and software) deposition and citation could be perceived as an opportunity to diversify the attribution portfolio and eventually give credit to the different contributors involved in the diverse phases of the lifecycle within the same research endeavour (Bierer, Crosas, & Pierce, 2017; Brand et al., 2015). While, on the one hand, it is known that authors' ordering tells little or nothing about authors' roles and contributions (Kosmulski, 2012), on the other hand, we argue that variations of any kind in author sets of paired publications and supplementary material can be indicative. Despite being unclear the actual reason behind such a variation, the presence of a fracture between the publication and research data realms might suggest once more that current practices for research assessment and reward should be revised and updated to capture such peculiarities as well. In (Mannocci, Irrera, & Manghi, 2022), we argue that modern Open Science Graphs (OSGs) can be used to analyse whether this is the case or not and understand if the opportunity has been seized already. By offering extensive metadata descriptions of both literature, research data, software, and their semantic relations, OSGs constitute a fertile ground to analyse this phenomenon computationally and thus analyse the emergence of significant patterns. As a preliminary study, in this paper, we conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science3 (MES) research community. The results are promising and suggest our hypothesis is worth exploring further. Indeed, in 702 cases out of 3,075 (22.83%), there are substantial variations between the authors participating in the publication and the authors participating in the supplementary dataset (or software), thus posing the premises for a longitudinal, large-scale analysis of the phenomenon.Source: STI 2022 - 26th International Conference on Science, Technology and Innovation Indicators, Granada, Spain, 7-9/09/2022
DOI: 10.5281/zenodo.6975411
Project(s): OpenAIRE Nexus
Metrics:
See at:
ISTI Repository | zenodo.org | CNR ExploRA
2020
Conference article
Open Access
Context-Driven Discoverability of Research Data
Baglioni M., Manghi P., Mannocci A.
Research data sharing has been proved to be key for accelerating scientific progress and fostering interdisciplinary research; hence, the ability to search, discover and reuse data items is nowadays vital in doing science. However, research data discovery is yet an open challenge. In many cases, descriptive metadata exhibit poor quality, and the ability to automatically enrich metadata with semantic information is limited by the data files format, which is typically not textual and hard to mine. More generally, however, researchers would like to find data used across different research experiments or even disciplines. Such needs are not met by traditional metadata description schemata, which are designed to freeze research data features at deposition time. In this paper, we propose a methodology that enables "context-driven discovery" for research data thanks to their proven usage across research activities that might differ from the original one, potentially across diverse disciplines. The methodology exploits the collection of publication-dataset and dataset-dataset links provided by OpenAIRE Scholexplorer data citation index so to propagate articles metadata into related research datasets by leveraging semantic relatedness. Such "context propagation" process enables the construction of "context-enriched" metadata of datasets, which enables "context-driven" discoverability of research data. To this end, we provide a real-case evaluation of this technique applied to Scholexplorer. Due to the broad coverage of Scholexplorer, the evaluation documents the effectiveness of this technique at improving data discovery on a variety of research data repositories and databases.Source: 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, pp. 197–211, Lyon, France, August 25-27, 2020
DOI: 10.1007/978-3-030-54956-5_15
Project(s): OpenAIRE-Advance
Metrics:
Baglioni M., Manghi P., Mannocci A.
Research data sharing has been proved to be key for accelerating scientific progress and fostering interdisciplinary research; hence, the ability to search, discover and reuse data items is nowadays vital in doing science. However, research data discovery is yet an open challenge. In many cases, descriptive metadata exhibit poor quality, and the ability to automatically enrich metadata with semantic information is limited by the data files format, which is typically not textual and hard to mine. More generally, however, researchers would like to find data used across different research experiments or even disciplines. Such needs are not met by traditional metadata description schemata, which are designed to freeze research data features at deposition time. In this paper, we propose a methodology that enables "context-driven discovery" for research data thanks to their proven usage across research activities that might differ from the original one, potentially across diverse disciplines. The methodology exploits the collection of publication-dataset and dataset-dataset links provided by OpenAIRE Scholexplorer data citation index so to propagate articles metadata into related research datasets by leveraging semantic relatedness. Such "context propagation" process enables the construction of "context-enriched" metadata of datasets, which enables "context-driven" discoverability of research data. To this end, we provide a real-case evaluation of this technique applied to Scholexplorer. Due to the broad coverage of Scholexplorer, the evaluation documents the effectiveness of this technique at improving data discovery on a variety of research data repositories and databases.Source: 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, pp. 197–211, Lyon, France, August 25-27, 2020
DOI: 10.1007/978-3-030-54956-5_15
Project(s): OpenAIRE-Advance
Metrics:
See at:
ZENODO | zenodo.org | Lecture Notes in Computer Science | link.springer.com | CNR ExploRA
2014
Report
Open Access
EAGLE - EAGLE metadata model specification (version 1.0)
Sicilia M., Gomez-Pantoja J., Fuentes M. J. R., Ruiz E. R., Mannocci A., Manghi P., Zoppi F.
The main goal of the the EAGLE project is to aggregate into the EAGLE portal data from the EAGLE partners who have epigraphic data bases (the so called Content Providers), and then ingest these data into Europeana. The purpose of this document is to define a common data model to which all the Content Providers can map their own data sets so that all the collected material can be managed in a uniform an coherent way, both for ingestion to Europeana and for supporting advanced search functionality over the aggregated data.Source: Project report, EAGLE, Deliverable D3.1, 2014
Project(s): EAGLE
Sicilia M., Gomez-Pantoja J., Fuentes M. J. R., Ruiz E. R., Mannocci A., Manghi P., Zoppi F.
The main goal of the the EAGLE project is to aggregate into the EAGLE portal data from the EAGLE partners who have epigraphic data bases (the so called Content Providers), and then ingest these data into Europeana. The purpose of this document is to define a common data model to which all the Content Providers can map their own data sets so that all the collected material can be managed in a uniform an coherent way, both for ingestion to Europeana and for supporting advanced search functionality over the aggregated data.Source: Project report, EAGLE, Deliverable D3.1, 2014
Project(s): EAGLE
See at:
ISTI Repository | CNR ExploRA
2014
Conference article
Restricted
The Europeana network of Ancient Greek and Latin Epigraphy data infrastructure
Mannocci A., Casarosa V., Manghi P., Zoppi F.
Epigraphic archives, containing collections of editions about ancient Greek and Latin inscriptions, have been created in several European countries during the last couple of centuries. Today, the project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aims at providing a single access point for the content of about 15 epigraphic archives, totaling about 1,5M digital objects. This paper illustrates some of the challenges encountered and their solution for the realization of the EAGLE data infrastructure. The challenges mainly concern the harmonization, interoperability and service integration issues caused by the aggregation of metadata from heterogeneous archives (different data models and metadata schemas, and exchange formats). EAGLE has defined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols.Source: MTSR 2014 - Metadata and Semantics Research. 8th Research Conference, pp. 286–300, Karlsruhe, Germany, 27-29 November 2014
DOI: 10.1007/978-3-319-13674-5_27
Metrics:
Mannocci A., Casarosa V., Manghi P., Zoppi F.
Epigraphic archives, containing collections of editions about ancient Greek and Latin inscriptions, have been created in several European countries during the last couple of centuries. Today, the project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aims at providing a single access point for the content of about 15 epigraphic archives, totaling about 1,5M digital objects. This paper illustrates some of the challenges encountered and their solution for the realization of the EAGLE data infrastructure. The challenges mainly concern the harmonization, interoperability and service integration issues caused by the aggregation of metadata from heterogeneous archives (different data models and metadata schemas, and exchange formats). EAGLE has defined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols.Source: MTSR 2014 - Metadata and Semantics Research. 8th Research Conference, pp. 286–300, Karlsruhe, Germany, 27-29 November 2014
DOI: 10.1007/978-3-319-13674-5_27
Metrics:
See at:
doi.org | link.springer.com | www.scopus.com | CNR ExploRA
2014
Conference article
Open Access
A conceptual model for inscriptions: harmonizing digital epigraphy data sources
Casarosa V., Manghi P., Mannocci A., Ruiz E. R., Zoppi F.
Epigraphic archives, containing collections of editions about ancient Greek and Latin inscriptions, have been created in several European countries during the last couple of centuries. Today, the project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aims at providing a single access point for the content of about 15 epigraphic archives, totaling about 1,5M digital objects. This paper illustrates some of the challenges encountered and their solution for the realization of the EAGLE data infrastructure. The challenges mainly concern the harmonization, interoperability and service integration issues caused by the aggregation of metadata from heterogeneous archives (different data models and metadata schemas, and exchange formats). EAGLE has de-fined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols.Source: EAGLE 2014 - Information Technologies for Epigraphy and Cultural Heritage. First EAGLE International Conference, pp. 23–40, Paris, France, 29 September - 1 October 2014
Casarosa V., Manghi P., Mannocci A., Ruiz E. R., Zoppi F.
Epigraphic archives, containing collections of editions about ancient Greek and Latin inscriptions, have been created in several European countries during the last couple of centuries. Today, the project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aims at providing a single access point for the content of about 15 epigraphic archives, totaling about 1,5M digital objects. This paper illustrates some of the challenges encountered and their solution for the realization of the EAGLE data infrastructure. The challenges mainly concern the harmonization, interoperability and service integration issues caused by the aggregation of metadata from heterogeneous archives (different data models and metadata schemas, and exchange formats). EAGLE has de-fined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols.Source: EAGLE 2014 - Information Technologies for Epigraphy and Cultural Heritage. First EAGLE International Conference, pp. 23–40, Paris, France, 29 September - 1 October 2014
See at:
archiv.ub.uni-heidelberg.de | CNR ExploRA
2014
Report
Unknown
EAGLE Portal
Prandoni C., Alfarano N., Casarosa V., Fresa A., Masi P., Buono M., Cionini N., Zoppi F., Mannocci A
This deliverable describes the main functionalities and the technical infrastructure of the EAGLE Portal, which is available online at www.eagle-network.eu. It reflects the status of the EAGLE Portal in October 2014, at the time of the first release. The deliverable will be updated in M30, when the second release of the EAGLE Portal is planned. The EAGLE Portal is the main gateway into the world of the EAGLE services and initiatives. On the portal, users can find all the background information about the project, the partners and the collections. It hosts a section where news related to the content providers or of interest for the community of epigraphists will be posted. A comprehensive presentation on the visual identity, layout and structure of the portal can be found in Deliverable D6.1 EAGLE Project Website. The portal is the main access to the set of EAGLE services, which can be accessed by a human through a browser or by the EAGLE Flagship Mobile Application, or by other applications through a set of APIs. This document is organised in two parts: Part A describes the EAGLE functions accessible through a browser, and Part B describes the APIs available for other applications, such as the Flagship Mobile Application and the Flagship Storytelling Application developed in EAGLE. In particular, Part A can be viewed as a user manual, describing the functionalities offered by the EAGLE Portal and how to use them through the Graphical User Interface (GUI). Part B can be viewed as a developer guide, providing a deeper insight on how these functionalities have been implemented and on the interactions between the different components and modules of the system. Part B provides also a description of the "external" APIs that can be used by applications that intend to take advantage of the rich set of data made available by EAGLE. In particular, the interfaces used by the Flagship Mobile Application (which is being developed in the frame of the EAGLE project) are here described in more details. Firstly, users who want to find information about inscriptions can access a search interface that is at the same time friendly and sophisticated, allowing both general users and experts to perform queries on the collections. It is possible to perform both full-text searches using a simple interface, or to launch more advanced queries. The seven controlled multilingual vocabularies (types of inscriptions, object types, material, writing, decoration, state of preservation and dating criteria), that were created to help aligning the multilingual metadata of the inscriptions from the different content providers, have been also integrated in the search engine and help users to create their search strings. Secondly, users that are interested in gaining access to the more advanced functionality can register an account and manage their Personal Space on the EAGLE Portal. In this area, they can save and annotate queries, results and single objects (including - in Release 2 - the pictures that they took using the Flagship Mobile Application) to access them in a future session.Source: Project report, EAGLE, Deliverable D5.2, pp.1–50, 2014
Project(s): EAGLE
Prandoni C., Alfarano N., Casarosa V., Fresa A., Masi P., Buono M., Cionini N., Zoppi F., Mannocci A
This deliverable describes the main functionalities and the technical infrastructure of the EAGLE Portal, which is available online at www.eagle-network.eu. It reflects the status of the EAGLE Portal in October 2014, at the time of the first release. The deliverable will be updated in M30, when the second release of the EAGLE Portal is planned. The EAGLE Portal is the main gateway into the world of the EAGLE services and initiatives. On the portal, users can find all the background information about the project, the partners and the collections. It hosts a section where news related to the content providers or of interest for the community of epigraphists will be posted. A comprehensive presentation on the visual identity, layout and structure of the portal can be found in Deliverable D6.1 EAGLE Project Website. The portal is the main access to the set of EAGLE services, which can be accessed by a human through a browser or by the EAGLE Flagship Mobile Application, or by other applications through a set of APIs. This document is organised in two parts: Part A describes the EAGLE functions accessible through a browser, and Part B describes the APIs available for other applications, such as the Flagship Mobile Application and the Flagship Storytelling Application developed in EAGLE. In particular, Part A can be viewed as a user manual, describing the functionalities offered by the EAGLE Portal and how to use them through the Graphical User Interface (GUI). Part B can be viewed as a developer guide, providing a deeper insight on how these functionalities have been implemented and on the interactions between the different components and modules of the system. Part B provides also a description of the "external" APIs that can be used by applications that intend to take advantage of the rich set of data made available by EAGLE. In particular, the interfaces used by the Flagship Mobile Application (which is being developed in the frame of the EAGLE project) are here described in more details. Firstly, users who want to find information about inscriptions can access a search interface that is at the same time friendly and sophisticated, allowing both general users and experts to perform queries on the collections. It is possible to perform both full-text searches using a simple interface, or to launch more advanced queries. The seven controlled multilingual vocabularies (types of inscriptions, object types, material, writing, decoration, state of preservation and dating criteria), that were created to help aligning the multilingual metadata of the inscriptions from the different content providers, have been also integrated in the search engine and help users to create their search strings. Secondly, users that are interested in gaining access to the more advanced functionality can register an account and manage their Personal Space on the EAGLE Portal. In this area, they can save and annotate queries, results and single objects (including - in Release 2 - the pictures that they took using the Flagship Mobile Application) to access them in a future session.Source: Project report, EAGLE, Deliverable D5.2, pp.1–50, 2014
Project(s): EAGLE
See at: CNR ExploRA
2015
Contribution to book
Open Access
The EAGLE Europeana network of Ancient Greek and Latin Epigraphy: a technical perspective
Mannocci A., Casarosa V., Manghi P., Zoppi F.
The project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aims at aggregating epigraphic material provided by some 15 different epigraphic archives (about 80% of the classified epigraphic material from the Mediterranean area) for ingestion to Europeana. The collected material will be made available also to the scholarly community and to the general public, for research and cultural dissemination. This paper briefly presents the main services provided by EAGLE and the challenges encountered for the aggregation of material coming from heterogeneous archives (different data models and metadata schemas, and exchange formats). EAGLE has defined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols.Source: Digital Libraries on the Move 11th Italian Research Conference on Digital Libraries, IRCDL 2015, Bolzano, Italy, January 29-30, 2015, Revised Selected Papers, edited by Diego Calvanese, Dario De Nart, Carlo Tasso, pp. 75–78, 2015
DOI: 10.1007/978-3-319-41938-1_8
Project(s): EAGLE
Metrics:
Mannocci A., Casarosa V., Manghi P., Zoppi F.
The project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aims at aggregating epigraphic material provided by some 15 different epigraphic archives (about 80% of the classified epigraphic material from the Mediterranean area) for ingestion to Europeana. The collected material will be made available also to the scholarly community and to the general public, for research and cultural dissemination. This paper briefly presents the main services provided by EAGLE and the challenges encountered for the aggregation of material coming from heterogeneous archives (different data models and metadata schemas, and exchange formats). EAGLE has defined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols.Source: Digital Libraries on the Move 11th Italian Research Conference on Digital Libraries, IRCDL 2015, Bolzano, Italy, January 29-30, 2015, Revised Selected Papers, edited by Diego Calvanese, Dario De Nart, Carlo Tasso, pp. 75–78, 2015
DOI: 10.1007/978-3-319-41938-1_8
Project(s): EAGLE
Metrics:
See at:
link.springer.com | doi.org | CNR ExploRA
2016
Journal article
Open Access
EAGLE - L'infrastruttura di aggregazione dei dati e i servizi a supporto del portale e delle applicazioni
Mannocci A., Casarosa V., Manghi P., Zoppi F.
La lunga tradizione epigrafica, risalente al secolo XVI, epoca ben lontana dai concetti di globalizzazione, standardizzazione e interoperabilità, ha fatto sì che nel tempo si sedimentassero, nelle varie comunità di studiosi, modus operandi spesso contrastanti. All'inizio degli anni '30 il Sistema di Leida (B. a. Van GroninGen, "Projet d'unification des systèmes de signes critiques", in Chronique d'Égypte 7, 1932, pp. 262-269) ha contribuito a ridurre notevolmente la frammentazione presente nei testi, ma una nuova deriva si è verificata dagli anni 90 in poi quando, con l'arrivo di Internet e del Web, gli archivi epigrafici hanno iniziato la loro conversione al digitale. Nonostante la definizione di uno standard per l'annotazione di documenti a carattere epigrafico (EpiDoc: http://sourceforge.net/p/epidoc/wiki/Home/) le comunità hanno per lo più operato in modo indipendente e senza nessuna linea guida condivisa, lasciando di fatto il panorama altamente frammentario. Il progetto EAGLE mira proprio a riconciliare e riunificare sotto un'unica egida le varie comunità epigrafiche e rendere i loro contenuti ricercabili da un unico punto di accesso, e a questo scopo ha sviluppato un'infrastruttura che consente l'aggregazione di tali contenuti e la loro armonizzazione secondo un modello di dati condiviso, e permette infine di interrogare i dati sia attraverso il proprio portale che attraverso Europeana.Source: Forma urbis XXI (2016): 18–21.
Project(s): EAGLE
Mannocci A., Casarosa V., Manghi P., Zoppi F.
La lunga tradizione epigrafica, risalente al secolo XVI, epoca ben lontana dai concetti di globalizzazione, standardizzazione e interoperabilità, ha fatto sì che nel tempo si sedimentassero, nelle varie comunità di studiosi, modus operandi spesso contrastanti. All'inizio degli anni '30 il Sistema di Leida (B. a. Van GroninGen, "Projet d'unification des systèmes de signes critiques", in Chronique d'Égypte 7, 1932, pp. 262-269) ha contribuito a ridurre notevolmente la frammentazione presente nei testi, ma una nuova deriva si è verificata dagli anni 90 in poi quando, con l'arrivo di Internet e del Web, gli archivi epigrafici hanno iniziato la loro conversione al digitale. Nonostante la definizione di uno standard per l'annotazione di documenti a carattere epigrafico (EpiDoc: http://sourceforge.net/p/epidoc/wiki/Home/) le comunità hanno per lo più operato in modo indipendente e senza nessuna linea guida condivisa, lasciando di fatto il panorama altamente frammentario. Il progetto EAGLE mira proprio a riconciliare e riunificare sotto un'unica egida le varie comunità epigrafiche e rendere i loro contenuti ricercabili da un unico punto di accesso, e a questo scopo ha sviluppato un'infrastruttura che consente l'aggregazione di tali contenuti e la loro armonizzazione secondo un modello di dati condiviso, e permette infine di interrogare i dati sia attraverso il proprio portale che attraverso Europeana.Source: Forma urbis XXI (2016): 18–21.
Project(s): EAGLE
See at:
www.formavrbis.com | CNR ExploRA
2015
Conference article
Unknown
The EAGLE data aggregator: data quality monitoring
Mannocci A., Casarosa V., Manghi P., Zoppi F.
The EAGLE project aggregates epigraphy related content from about 20 different data providers, and makes its content available to both Europeana and to scholars. Data Quality monitoring is a key issue in Aggregative Data Infrastructures, where content is collected from a number of different sources with different data models and quality standards. This paper presents a Monitoring Framework for enabling the observation and monitoring of an aggregative infrastructure focusing on the description of the Data Flow and Dynamics Service, and exemplifying these concepts with a use case tailored to the characteristics of the EAGLE aggregation data flow. An Infrastructure Quality Manager (IQM) is provided with a Web user interface (WebUI), allowing her to describe the data flows taking place in the infrastructure and to define monitoring scenarios. The scenarios will include the definition of sensors (pieces of software plugged into the data flow), which will provide observations of measured objects. The scenarios include also the definition of controls and analysers, which will store and process the observations received from the sensors and will verify if the values of the measured features comply with some expected behaviour over time. A monitoring scenario for EAGLE has been defined and tested on simulated data (the monitoring framework is still under development) in order to monitor the "health" of different data collections involved in the EAGLE collection and transformation workflows.Source: 7th EAGLE International Conference, Roma, Italy, 27-29 Gennaio 2016
Project(s): EAGLE
Mannocci A., Casarosa V., Manghi P., Zoppi F.
The EAGLE project aggregates epigraphy related content from about 20 different data providers, and makes its content available to both Europeana and to scholars. Data Quality monitoring is a key issue in Aggregative Data Infrastructures, where content is collected from a number of different sources with different data models and quality standards. This paper presents a Monitoring Framework for enabling the observation and monitoring of an aggregative infrastructure focusing on the description of the Data Flow and Dynamics Service, and exemplifying these concepts with a use case tailored to the characteristics of the EAGLE aggregation data flow. An Infrastructure Quality Manager (IQM) is provided with a Web user interface (WebUI), allowing her to describe the data flows taking place in the infrastructure and to define monitoring scenarios. The scenarios will include the definition of sensors (pieces of software plugged into the data flow), which will provide observations of measured objects. The scenarios include also the definition of controls and analysers, which will store and process the observations received from the sensors and will verify if the values of the measured features comply with some expected behaviour over time. A monitoring scenario for EAGLE has been defined and tested on simulated data (the monitoring framework is still under development) in order to monitor the "health" of different data collections involved in the EAGLE collection and transformation workflows.Source: 7th EAGLE International Conference, Roma, Italy, 27-29 Gennaio 2016
Project(s): EAGLE
See at: CNR ExploRA
2017
Conference article
Open Access
Coping with interoperability in cultural heritage data infrastructures: the Europeana network of Ancient Greek and Latin Epigraphy
Amato G., Mannocci A., Vadicamo L., Zoppi F.
One of the main motivations of the project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) is to restore some unity of our past by collecting in a single repository information about the thousands of inscriptions now scattered across all Europe. The collected information is ingested in Europeana and it is made available to the scholarly community and to the general public, for research and cultural dissemination, through a user-friendly portal supporting advanced query and search capabilities. In addition to the traditional search options (full-text search a la Google, fielded search, faceted search and filtering), the EAGLE portal supports two applications intended to make the fruition of the epigraphic material easier and more useful: the EAGLE Flagship Mobile Application and the Story Telling Application. Along the same lines, in order to make the epigraphic material more interesting and usable also by non-epigraphists, EAGLE, in collaboration with the Italian chapter of the Wikimedia Foundation, is leading an effort for the enrichment of the epigraphic images and text with additional information and translations into modern languages. During the whole project life frame, the maintainability and sustainability issues have been constantly considered from both the technical and the scientific point of view. This poster gives some insights of the overall infrastructure.Source: AIUCD 2017- Il telescopio inverso: big data e distant reading nelle discipline umanistiche, pp. 211–215, Rome, Italy, 24-28 January 2017
Project(s): EAGLE
Amato G., Mannocci A., Vadicamo L., Zoppi F.
One of the main motivations of the project EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) is to restore some unity of our past by collecting in a single repository information about the thousands of inscriptions now scattered across all Europe. The collected information is ingested in Europeana and it is made available to the scholarly community and to the general public, for research and cultural dissemination, through a user-friendly portal supporting advanced query and search capabilities. In addition to the traditional search options (full-text search a la Google, fielded search, faceted search and filtering), the EAGLE portal supports two applications intended to make the fruition of the epigraphic material easier and more useful: the EAGLE Flagship Mobile Application and the Story Telling Application. Along the same lines, in order to make the epigraphic material more interesting and usable also by non-epigraphists, EAGLE, in collaboration with the Italian chapter of the Wikimedia Foundation, is leading an effort for the enrichment of the epigraphic images and text with additional information and translations into modern languages. During the whole project life frame, the maintainability and sustainability issues have been constantly considered from both the technical and the scientific point of view. This poster gives some insights of the overall infrastructure.Source: AIUCD 2017- Il telescopio inverso: big data e distant reading nelle discipline umanistiche, pp. 211–215, Rome, Italy, 24-28 January 2017
Project(s): EAGLE
See at:
aiucd2017.aiucd.it | ISTI Repository | CNR ExploRA
2017
Contribution to book
Open Access
The OpenAIRE workflows for data management
Atzori C., Bardi A., Manghi P., Mannocci A.
The OpenAIRE initiative is the point of reference for Open Access in Europe and aims at the creation of an e-Infrastructure for the free flow, access, sharing, and re-use of research outcomes, services and processes for the advancement of research and the dissemination of scientific knowledge. OpenAIRE makes openly accessible a rich Information Space Graph (ISG) where products of the research life-cycle (e.g. publications, datasets, projects) are semantically linked to each other. Such an information space graph is constructed by a set of autonomic (orchestrated) workflows operating in a regimen of continuous data integration. This paper discusses the principal workflows operated by the OpenAIRE technical infrastructure in its different functional areas and provides the reader with the extent of the several challenges faced and the solutions realized.Source: Digital Libraries and Archives, edited by Costantino Grana, Lorenzo Baraldi, pp. 95–107, 2017
DOI: 10.1007/978-3-319-68130-6_8
DOI: 10.5281/zenodo.996006
DOI: 10.5281/zenodo.996005
Project(s): OpenAIRE2020
Metrics:
Atzori C., Bardi A., Manghi P., Mannocci A.
The OpenAIRE initiative is the point of reference for Open Access in Europe and aims at the creation of an e-Infrastructure for the free flow, access, sharing, and re-use of research outcomes, services and processes for the advancement of research and the dissemination of scientific knowledge. OpenAIRE makes openly accessible a rich Information Space Graph (ISG) where products of the research life-cycle (e.g. publications, datasets, projects) are semantically linked to each other. Such an information space graph is constructed by a set of autonomic (orchestrated) workflows operating in a regimen of continuous data integration. This paper discusses the principal workflows operated by the OpenAIRE technical infrastructure in its different functional areas and provides the reader with the extent of the several challenges faced and the solutions realized.Source: Digital Libraries and Archives, edited by Costantino Grana, Lorenzo Baraldi, pp. 95–107, 2017
DOI: 10.1007/978-3-319-68130-6_8
DOI: 10.5281/zenodo.996006
DOI: 10.5281/zenodo.996005
Project(s): OpenAIRE2020
Metrics:
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