2025
Journal article
Restricted
NAVIGATOR: a regional multimodal imaging biobank initiative powered by AI tools for precision medicine in oncology
Aghakhanyan G., Barucci A., Pascali M. A., Assante M., Bagnacci G., Bertelli E., Caputo F. P., Cuibari M. E., Carlini E., Carpi R., Caudai C., Cioni D., Colantonio S., Colcelli V., Dell'Amico A., Vecchio V. D., Gangi D. D., Faggioni L., Formica V., Francischello R., Frosini L., Kotsa C., Lipari G., Manghi P., Martino V. D., Marzi C., Mazzei M. A., Mangiacrapa F., Meglio N. D., Miele V., Molinaro E., Paiar F., Pagano P., Panichi G., Pasquinelli F., Peccerillo B., Perrella A., Piccioli T., Oliviero A., Olivoni M., Rucci D., Tampucci M., Tumminello L., Volpini F., Zanuzzi A., Fanni S. C., Neri E.
The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.Source: EUROPEAN JOURNAL OF RADIOLOGY, vol. 191 (issue 112327)
DOI: 10.1016/j.ejrad.2025.112327
Project(s): An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Metrics:
Aghakhanyan G., Barucci A., Pascali M. A., Assante M., Bagnacci G., Bertelli E., Caputo F. P., Cuibari M. E., Carlini E., Carpi R., Caudai C., Cioni D., Colantonio S., Colcelli V., Dell'Amico A., Vecchio V. D., Gangi D. D., Faggioni L., Formica V., Francischello R., Frosini L., Kotsa C., Lipari G., Manghi P., Martino V. D., Marzi C., Mazzei M. A., Mangiacrapa F., Meglio N. D., Miele V., Molinaro E., Paiar F., Pagano P., Panichi G., Pasquinelli F., Peccerillo B., Perrella A., Piccioli T., Oliviero A., Olivoni M., Rucci D., Tampucci M., Tumminello L., Volpini F., Zanuzzi A., Fanni S. C., Neri E.
The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.Source: EUROPEAN JOURNAL OF RADIOLOGY, vol. 191 (issue 112327)
DOI: 10.1016/j.ejrad.2025.112327
Project(s): An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Metrics:
See at:
European Journal of Radiology 
| Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS | Archivio della Ricerca - Università di Pisa
2025
Journal article
Open Access
Towards the interoperability of scholarly repository registries
Baglioni M., Pavone G., Mannocci A., Manghi P.
The enactment of Open Science relies on scholarly repositories that make research products findable and accessible, while scholarly repository registries maintain authoritative metadata and persistent identifiers (PIDs) to help researchers and infrastructure providers discover and access needed repositories. However, the proliferation of repositories targeting different research products (e.g., publications, data, and software) or serving specific disciplines has led to the creation of multiple registries whose scope is not mutually exclusive. Such a fragmented landscape poses significant concerns regarding authoritativeness, disambiguation, and coverage for scholarly communication service and infrastructure providers who consume content from these registries. These providers must either limit their focus to a single registry or manage complex data fusion strategies to integrate diverse repository profiles from various sources. While favouring the existence of a plurality of registries, this paper advocates for their interoperability, which is essential to eliminate the aforementioned barriers and enable their full, unambiguous utilisation. We analyse the data models of four prominent registries—FAIRsharing, re3data, OpenDOAR, and ROAR—and classify their properties and overlap. We provide a crosswalk between their data models and suggest a common data model shared across the examined registries to pave the way toward interoperability. As a means of validation, we include a coverage evaluation of the proposed data model.The paper adopts a pragmatic approach towards scholarly registry interoperability and suggests a common metadata model to foster the exchange of information across these platforms. The purpose of the paper is to serve as a cornerstone, initiating and engaging the community in discussions surrounding the interoperability of scholarly repository registries.Source: INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES, vol. 26 (issue 1)
DOI: 10.1007/s00799-025-00414-y
Project(s): EOSC Future
, OpenAIRE Nexus
Metrics:
Baglioni M., Pavone G., Mannocci A., Manghi P.
The enactment of Open Science relies on scholarly repositories that make research products findable and accessible, while scholarly repository registries maintain authoritative metadata and persistent identifiers (PIDs) to help researchers and infrastructure providers discover and access needed repositories. However, the proliferation of repositories targeting different research products (e.g., publications, data, and software) or serving specific disciplines has led to the creation of multiple registries whose scope is not mutually exclusive. Such a fragmented landscape poses significant concerns regarding authoritativeness, disambiguation, and coverage for scholarly communication service and infrastructure providers who consume content from these registries. These providers must either limit their focus to a single registry or manage complex data fusion strategies to integrate diverse repository profiles from various sources. While favouring the existence of a plurality of registries, this paper advocates for their interoperability, which is essential to eliminate the aforementioned barriers and enable their full, unambiguous utilisation. We analyse the data models of four prominent registries—FAIRsharing, re3data, OpenDOAR, and ROAR—and classify their properties and overlap. We provide a crosswalk between their data models and suggest a common data model shared across the examined registries to pave the way toward interoperability. As a means of validation, we include a coverage evaluation of the proposed data model.The paper adopts a pragmatic approach towards scholarly registry interoperability and suggests a common metadata model to foster the exchange of information across these platforms. The purpose of the paper is to serve as a cornerstone, initiating and engaging the community in discussions surrounding the interoperability of scholarly repository registries.Source: INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES, vol. 26 (issue 1)
DOI: 10.1007/s00799-025-00414-y
Project(s): EOSC Future
, OpenAIRE Nexus Metrics:
See at:
International Journal on Digital Libraries 
| CNR IRIS | link.springer.com | Software Heritage | Software Heritage | GitHub | GitHub | CNR IRIS
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Z., Miori V., Tolomei G., Waheed T., Marchetti E., Calabrò A., Rossetti G., Stella M., Cazabet R., Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo A., Sebastiani F., Sperduti G., Nguyen D., Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte G., Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann J., Meyer T., Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang D., Di Giandomenico F., Jee E., Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia G.
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Z., Miori V., Tolomei G., Waheed T., Marchetti E., Calabrò A., Rossetti G., Stella M., Cazabet R., Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo A., Sebastiani F., Sperduti G., Nguyen D., Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte G., Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann J., Meyer T., Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang D., Di Giandomenico F., Jee E., Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia G.
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Other
Open Access
Towards an infrastructure for responsible research assessment data management
Mannocci A., Candela L., Manghi P.
Research evaluation is undergoing a profound transformation, and it is now widely recognised that the true value of a researcher’s contribution extends far beyond the sheer volume of papers published in scientific outlets. Yet, despite the growing adoption of revised CV templates and assessment frameworks across many organisations participating in the research ecosystem, a critical gap remains: the lack of structured, interoperable metadata to represent the full spectrum of scholarly contributions. Essential contributions—such as organising conferences, mentoring, teaching, serving on scientific boards, or engaging in collaborative projects—are often undocumented or scattered across ephemeral sources, e.g. emails, web pages, or printouts. Without a robust system for capturing and preserving this information, much of the valuable scholarly record risks being lost as digital content is deleted, websites are updated or decommissioned, or institutional memory fades. To address this challenge, we propose piloting a suite of tools and services that harness the power of Scientific Knowledge Graphs (SKGs), Semantic Web technologies, and Artificial Intelligence. These tools will empower researchers applying for evaluation to capture, persist, and reference their diverse contributions in a CV-ready, machine-readable, and compelling format—on demand and with minimal friction. AI can complement this picture by assisting evaluands in generating narrative sections and impact stories, drafting text and retrieving supporting evidence online. Even more so, by aligning with SKG interoperability standards, this approach will enable the cross-institutional and transnational exchange of evaluation data, paving the way for a more streamlined, verifiable, and up-to-date research assessment process, which will reduce reliance on manual data entry, enhance transparency, and support the principles of Open Science and responsible research evaluation. This research endeavour—posing challenges including dynamic data collection and collation, data provenance and quality, data certification and reliability, generative AI—is not just a technical development; rather, it lays the foundations for a more inclusive, accurate, and future-proof evaluation ecosystem.Project(s): GraspOS
Mannocci A., Candela L., Manghi P.
Research evaluation is undergoing a profound transformation, and it is now widely recognised that the true value of a researcher’s contribution extends far beyond the sheer volume of papers published in scientific outlets. Yet, despite the growing adoption of revised CV templates and assessment frameworks across many organisations participating in the research ecosystem, a critical gap remains: the lack of structured, interoperable metadata to represent the full spectrum of scholarly contributions. Essential contributions—such as organising conferences, mentoring, teaching, serving on scientific boards, or engaging in collaborative projects—are often undocumented or scattered across ephemeral sources, e.g. emails, web pages, or printouts. Without a robust system for capturing and preserving this information, much of the valuable scholarly record risks being lost as digital content is deleted, websites are updated or decommissioned, or institutional memory fades. To address this challenge, we propose piloting a suite of tools and services that harness the power of Scientific Knowledge Graphs (SKGs), Semantic Web technologies, and Artificial Intelligence. These tools will empower researchers applying for evaluation to capture, persist, and reference their diverse contributions in a CV-ready, machine-readable, and compelling format—on demand and with minimal friction. AI can complement this picture by assisting evaluands in generating narrative sections and impact stories, drafting text and retrieving supporting evidence online. Even more so, by aligning with SKG interoperability standards, this approach will enable the cross-institutional and transnational exchange of evaluation data, paving the way for a more streamlined, verifiable, and up-to-date research assessment process, which will reduce reliance on manual data entry, enhance transparency, and support the principles of Open Science and responsible research evaluation. This research endeavour—posing challenges including dynamic data collection and collation, data provenance and quality, data certification and reliability, generative AI—is not just a technical development; rather, it lays the foundations for a more inclusive, accurate, and future-proof evaluation ecosystem.Project(s): GraspOS
2025
Other
Restricted
InfraScience research activity report 2024
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
2024
Conference article
Open Access
FDup framework: a general-purpose solution for efficient entity deduplication of record collections
De Bonis M., Atzori C., La Bruzzo S., Manghi P.
Deduplication is a technique aimed at identifying and resolving duplicate metadata records in a collection with a special focus on the performances of the approach. This paper describes FDup(Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity-matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Graph, a known open-access knowledge base in Scholarly communication.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3741, pp. 624-632. Villasimius, Italy, 23-26/06/2024
Project(s): FAIRCORE4EOSC
De Bonis M., Atzori C., La Bruzzo S., Manghi P.
Deduplication is a technique aimed at identifying and resolving duplicate metadata records in a collection with a special focus on the performances of the approach. This paper describes FDup(Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity-matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Graph, a known open-access knowledge base in Scholarly communication.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3741, pp. 624-632. Villasimius, Italy, 23-26/06/2024
Project(s): FAIRCORE4EOSC
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Challenges in building scholarly knowledge graphs for research assessment in open science
Manghi P.
Open science has revolutionized scholarly communication and research assessment by introducing research data and software as first-class citizens. Scholarly knowledge graphs (SKGs) are expected to play a crucial role in generating research assessment indicators being able to aggregate bibliographic metadata records and semantic relationships describing all research products and their links (e.g., citations, affiliations, funding). However, the rapid advance of open science has led to publication workflows that do not adequately support and guarantee the authenticity of products and metadata quality required for research assessment. Additionally, the heterogeneity of research communities and the multitude of data sources and exchange formats complicate the provision of consistent and stable SKGs. This work builds upon the experience gained from pioneering and addressing these challenges in the OpenAIRE Graph SKG. The aim is twofold and broader. First, we identify obstacles to the creation of SKGs for research assessment caused by the state-of-the-art publishing workflows for publications, software, and data. Second, we describe repurposing SKGs as tools to monitor such workflows to identify and heal their shortcomings, taking advantage of tools, techniques, and practices that support the actors involved, namely research communities, scientists, organizations, data source providers, and SKG providers, to improve the Open Science scholarly publishing ecosystem.Source: QUANTITATIVE SCIENCE STUDIES, vol. 5 (issue 4), pp. 991-1021
DOI: 10.1162/qss_a_00322
Project(s): EOSC Future, OpenAIRE Nexus
Metrics:
Manghi P.
Open science has revolutionized scholarly communication and research assessment by introducing research data and software as first-class citizens. Scholarly knowledge graphs (SKGs) are expected to play a crucial role in generating research assessment indicators being able to aggregate bibliographic metadata records and semantic relationships describing all research products and their links (e.g., citations, affiliations, funding). However, the rapid advance of open science has led to publication workflows that do not adequately support and guarantee the authenticity of products and metadata quality required for research assessment. Additionally, the heterogeneity of research communities and the multitude of data sources and exchange formats complicate the provision of consistent and stable SKGs. This work builds upon the experience gained from pioneering and addressing these challenges in the OpenAIRE Graph SKG. The aim is twofold and broader. First, we identify obstacles to the creation of SKGs for research assessment caused by the state-of-the-art publishing workflows for publications, software, and data. Second, we describe repurposing SKGs as tools to monitor such workflows to identify and heal their shortcomings, taking advantage of tools, techniques, and practices that support the actors involved, namely research communities, scientists, organizations, data source providers, and SKG providers, to improve the Open Science scholarly publishing ecosystem.Source: QUANTITATIVE SCIENCE STUDIES, vol. 5 (issue 4), pp. 991-1021
DOI: 10.1162/qss_a_00322
Project(s): EOSC Future
, OpenAIRE Nexus Metrics:
See at:
direct.mit.edu | Quantitative Science Studies | CNR IRIS | CNR IRIS
2024
Dataset
Open Access
OpenAIRE Graph Dataset v8.0.0 (July 2024)
Manghi P., Atzori C., Bardi A., Baglioni M., Dimitropoulos H., La Bruzzo S., Foufoulas I., Mannocci A., Horst M., Iatropoulou K., Kokogiannaki A., De Bonis M., Artini M., Lempesis A., Ioannidis A., Manola N., Principe P., Vergoulis T., Chatzopoulos S.
The OpenAIRE Graph is a large and rich collection of open and linked scholarly records from trusted data sources, such as journals, repositories, and registries. It aims to foster Open Science practices and enable the scientific community to discover, monitor, and evaluate science. The Graph is cleaned, deduplicated, enriched, and full-text mined to generate statistics and insights. The Graph is accessible via various services, such as OpenAIRE MONITOR, EXPLORE, ScholeXplorer (Scholix API for the retrieval of literature-data links), search APIs and snapshots in json format updated every six months. The Graph data are openly available with CC-BY license for third-parties to reuse and create added value services. The documentation is available at: https://graph.openaire.euDOI: 10.5281/zenodo.12819872
Project(s): FAIRCORE4EOSC, SciLake , EOSC Beyond , GraspOS , OSTrails
Metrics:
Manghi P., Atzori C., Bardi A., Baglioni M., Dimitropoulos H., La Bruzzo S., Foufoulas I., Mannocci A., Horst M., Iatropoulou K., Kokogiannaki A., De Bonis M., Artini M., Lempesis A., Ioannidis A., Manola N., Principe P., Vergoulis T., Chatzopoulos S.
The OpenAIRE Graph is a large and rich collection of open and linked scholarly records from trusted data sources, such as journals, repositories, and registries. It aims to foster Open Science practices and enable the scientific community to discover, monitor, and evaluate science. The Graph is cleaned, deduplicated, enriched, and full-text mined to generate statistics and insights. The Graph is accessible via various services, such as OpenAIRE MONITOR, EXPLORE, ScholeXplorer (Scholix API for the retrieval of literature-data links), search APIs and snapshots in json format updated every six months. The Graph data are openly available with CC-BY license for third-parties to reuse and create added value services. The documentation is available at: https://graph.openaire.euDOI: 10.5281/zenodo.12819872
Project(s): FAIRCORE4EOSC
, SciLake , EOSC Beyond , GraspOS , OSTrails Metrics:
See at:
CNR IRIS | zenodo.org | CNR IRIS
2024
Journal article
Restricted
Methods for generation, recommendation, exploration and analysis of scholarly publications
Silvello G., Corcho O., Manghi P.
In the shifting landscape of sharing knowledge, it is no longer only about writing papers. After a paper is written, what comes next is an integral part of the process. This special issue delves into the transformative landscape of scholarly communication, exploring novel methodologies and technologies reshaping how scholarly content is generated, recommended, explored and analysed. Indeed, the contemporary perspective on scholarly publication recognizes the centrality of post-publication activities. The criticality of refining and scrutinizing manuscripts has gained prominence, surpassing the act of dissemination. The emphasis has shifted from publication to ensuring visibility and comprehension of the conveyed content. The papers compiled in this special issue scrutinize these evolving dynamics. They delve into the intricacies of post-processing and close examination of manuscripts, acknowledging the impact of these aspects. The overarching objective is to stimulate scholarly discussions on the evolving nature of communication in academia.Source: INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES, vol. 25 (issue 3), pp. 427-429
DOI: 10.1007/s00799-024-00409-1
Metrics:
Silvello G., Corcho O., Manghi P.
In the shifting landscape of sharing knowledge, it is no longer only about writing papers. After a paper is written, what comes next is an integral part of the process. This special issue delves into the transformative landscape of scholarly communication, exploring novel methodologies and technologies reshaping how scholarly content is generated, recommended, explored and analysed. Indeed, the contemporary perspective on scholarly publication recognizes the centrality of post-publication activities. The criticality of refining and scrutinizing manuscripts has gained prominence, surpassing the act of dissemination. The emphasis has shifted from publication to ensuring visibility and comprehension of the conveyed content. The papers compiled in this special issue scrutinize these evolving dynamics. They delve into the intricacies of post-processing and close examination of manuscripts, acknowledging the impact of these aspects. The overarching objective is to stimulate scholarly discussions on the evolving nature of communication in academia.Source: INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES, vol. 25 (issue 3), pp. 427-429
DOI: 10.1007/s00799-024-00409-1
Metrics:
See at:
International Journal on Digital Libraries | CNR IRIS | CNR IRIS | link.springer.com
2023
Contribution to book
Open Access
A primer on open science-driven repository platforms
Bardi A, Manghi P, Mannocci A, Ottonello E, Pavone G
Following Open Science mandates, institutions and communities increasingly demand repositories with native support for publishing scientific literature together with research data, software, and other research products. Such repositories may be thematic or general-purpose and are deeply integrated with the scholarly communication ecosystem to ensure versioning, persistent identifiers, data curation, usage stats, and so on. Identifying the most suitable off-the-shelf repository platform is often a non-trivial task as the choice depends on functional requirements, programming and technical skills, and infrastructure resources. This work analyses four state-of-the-art Open Source repository platforms, namely Dryad, Dataverse, DSpace, and InvenioRDM, from both a functional and a software perspective. This work intends to provide an overview serving as a primer for choosing repository platform solutions in different application scenarios. Moreover, this paper highlights how these platforms reacted to some key Open Science demands, moving away from the original and old-fashioned concept of a repository serving as a static container of files and metadata.DOI: 10.1007/978-3-031-39141-5_19
Project(s): OpenAIRE Nexus
Metrics:
Bardi A, Manghi P, Mannocci A, Ottonello E, Pavone G
Following Open Science mandates, institutions and communities increasingly demand repositories with native support for publishing scientific literature together with research data, software, and other research products. Such repositories may be thematic or general-purpose and are deeply integrated with the scholarly communication ecosystem to ensure versioning, persistent identifiers, data curation, usage stats, and so on. Identifying the most suitable off-the-shelf repository platform is often a non-trivial task as the choice depends on functional requirements, programming and technical skills, and infrastructure resources. This work analyses four state-of-the-art Open Source repository platforms, namely Dryad, Dataverse, DSpace, and InvenioRDM, from both a functional and a software perspective. This work intends to provide an overview serving as a primer for choosing repository platform solutions in different application scenarios. Moreover, this paper highlights how these platforms reacted to some key Open Science demands, moving away from the original and old-fashioned concept of a repository serving as a static container of files and metadata.DOI: 10.1007/978-3-031-39141-5_19
Project(s): OpenAIRE Nexus
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
(Semi)automated disambiguation of scholarly repositories
Baglioni M, Mannocci A, Pavone G, De Bonis M, Manghi P
The full exploitation of scholarly repositories is pivotal in modern Open Science, and scholarly repository registries are kingpins in enabling researchers and research infrastructures to list and search for suitable repositories. However, since multiple registries exist, repository managers are keen on registering multiple times the repositories they manage to maximise their traction and visibility across different research communities, disciplines, and applications. These multiple registrations ultimately lead to information fragmentation and redundancy on the one hand and, on the other, force registries' users to juggle multiple registries, profiles and identifiers describing the same repository. Such problems are known to registries, which claim equivalence between repository profiles whenever possible by cross-referencing their identifiers across different registries. However, as we will see, this "claim set" is far from complete and, therefore, many replicas slip under the radar, possibly creating problems downstream. In this work, we combine such claims to create duplicate sets and extend them with the results of an automated clustering algorithm run over repository metadata descriptions. Then we manually validate our results to produce an "as accurate as possible" de-duplicated dataset of scholarly repositories.Source: CEUR WORKSHOP PROCEEDINGS, pp. 47-59. Bari, Italy, 23-24/02/2023
Project(s): OpenAIRE Nexus
Baglioni M, Mannocci A, Pavone G, De Bonis M, Manghi P
The full exploitation of scholarly repositories is pivotal in modern Open Science, and scholarly repository registries are kingpins in enabling researchers and research infrastructures to list and search for suitable repositories. However, since multiple registries exist, repository managers are keen on registering multiple times the repositories they manage to maximise their traction and visibility across different research communities, disciplines, and applications. These multiple registrations ultimately lead to information fragmentation and redundancy on the one hand and, on the other, force registries' users to juggle multiple registries, profiles and identifiers describing the same repository. Such problems are known to registries, which claim equivalence between repository profiles whenever possible by cross-referencing their identifiers across different registries. However, as we will see, this "claim set" is far from complete and, therefore, many replicas slip under the radar, possibly creating problems downstream. In this work, we combine such claims to create duplicate sets and extend them with the results of an automated clustering algorithm run over repository metadata descriptions. Then we manually validate our results to produce an "as accurate as possible" de-duplicated dataset of scholarly repositories.Source: CEUR WORKSHOP PROCEEDINGS, pp. 47-59. Bari, Italy, 23-24/02/2023
Project(s): OpenAIRE Nexus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
A novel curated scholarly graph connecting textual and data publications
Irrera O, Mannocci A, Manghi P, Silvello G
In the last decade, scholarly graphs became fundamental to storing and managing scholarly knowledge in a structured and machine-readable way. Methods and tools for discovery and impact assessment of science rely on such graphs and their quality to serve scientists, policymakers, and publishers. Since research data became very important in scholarly communication, scholarly graphs started including dataset metadata and their relationships to publications. Such graphs are the foundations for Open Science investigations, data-article publishing workflows, discovery, and assessment indicators. However, due to the heterogeneity of practices (FAIRness is indeed in the making), they often lack the complete and reliable metadata necessary to perform accurate data analysis; e.g., dataset metadata is inaccurate, author names are not uniform, and the semantics of the relationships is unknown, ambiguous or incomplete.This work describes an open and curated scholarly graph we built and published as a training and test set for data discovery, data connection, author disambiguation, and link prediction tasks. Overall the graph contains 4,047 publications, 5,488 datasets, 22 software, 21,561 authors; 9,692 edges interconnect publications to datasets and software and are labeled with semantics that outline whether a publication is citing, referencing, documenting, supplementing another product.To ensure high-quality metadata and semantics, we relied on the information extracted from PDFs of the publications and the datasets and software webpages to curate and enrich nodes metadata and edges semantics. To the best of our knowledge, this is the first ever published resource, including publications and datasets with manually validated and curated metadata.Source: ACM JOURNAL OF DATA AND INFORMATION QUALITY, vol. 15 (issue 3)
DOI: 10.1145/3597310
Project(s): OpenAIRE Nexus
Metrics:
Irrera O, Mannocci A, Manghi P, Silvello G
In the last decade, scholarly graphs became fundamental to storing and managing scholarly knowledge in a structured and machine-readable way. Methods and tools for discovery and impact assessment of science rely on such graphs and their quality to serve scientists, policymakers, and publishers. Since research data became very important in scholarly communication, scholarly graphs started including dataset metadata and their relationships to publications. Such graphs are the foundations for Open Science investigations, data-article publishing workflows, discovery, and assessment indicators. However, due to the heterogeneity of practices (FAIRness is indeed in the making), they often lack the complete and reliable metadata necessary to perform accurate data analysis; e.g., dataset metadata is inaccurate, author names are not uniform, and the semantics of the relationships is unknown, ambiguous or incomplete.This work describes an open and curated scholarly graph we built and published as a training and test set for data discovery, data connection, author disambiguation, and link prediction tasks. Overall the graph contains 4,047 publications, 5,488 datasets, 22 software, 21,561 authors; 9,692 edges interconnect publications to datasets and software and are labeled with semantics that outline whether a publication is citing, referencing, documenting, supplementing another product.To ensure high-quality metadata and semantics, we relied on the information extracted from PDFs of the publications and the datasets and software webpages to curate and enrich nodes metadata and edges semantics. To the best of our knowledge, this is the first ever published resource, including publications and datasets with manually validated and curated metadata.Source: ACM JOURNAL OF DATA AND INFORMATION QUALITY, vol. 15 (issue 3)
DOI: 10.1145/3597310
Project(s): OpenAIRE Nexus
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
A discovery hub for Diamond Open Access publishing
Bardi A, Bargheer M, Manghi P
Open Access (OA) publishing is the set of practices thanks to which research publications are accessible freely without barriers. With Diamond Open Access, authors can publish free of charge as the institutional sector with universities, research institutions or libraries provide the necessary technological infrastructure. However, the Diamond OA landscape continues to be fragmented, is often underfunded, and is not always technically proficient enough to develop its full potential for science and society. The CRAFT-OA project, started in January 2023, aims to consolidate the Diamond OA publishing landscape both from the technical and organisational point of views. In this paper we describe the context and architecture of the Diamond Discovery Hub that will be released by the project to increase visibility, discoverability and recognition of Diamond OA institutional publishers and their content. The Diamond Discovery Hub will facilitate the integration with the wider scholarly communication ecosystem and the European Open Science Cloud to enlarge visibility, discoverability and reach of open access publications as part of the emerging Open Science paradigm.Source: CEUR WORKSHOP PROCEEDINGS, pp. 162-166. Bari, Italy, 23-24/02/2023
Bardi A, Bargheer M, Manghi P
Open Access (OA) publishing is the set of practices thanks to which research publications are accessible freely without barriers. With Diamond Open Access, authors can publish free of charge as the institutional sector with universities, research institutions or libraries provide the necessary technological infrastructure. However, the Diamond OA landscape continues to be fragmented, is often underfunded, and is not always technically proficient enough to develop its full potential for science and society. The CRAFT-OA project, started in January 2023, aims to consolidate the Diamond OA publishing landscape both from the technical and organisational point of views. In this paper we describe the context and architecture of the Diamond Discovery Hub that will be released by the project to increase visibility, discoverability and recognition of Diamond OA institutional publishers and their content. The Diamond Discovery Hub will facilitate the integration with the wider scholarly communication ecosystem and the European Open Science Cloud to enlarge visibility, discoverability and reach of open access publications as part of the emerging Open Science paradigm.Source: CEUR WORKSHOP PROCEEDINGS, pp. 162-166. Bari, Italy, 23-24/02/2023
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
Data management plans as linked open data: exploiting ARGOS FAIR and machine actionable outputs in the OpenAIRE research graph
Papadopoulou E, Bardi A, Kakaletris G, Tziotzios D, Manghi P, Manola N
Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes, institutions) and the relationships among them. They present a contextualized view of current research that supports discovery, re-use, reproducibility, monitoring, transparency and omni-comprehensive assessment. A Data Management Plan (DMP) contains information concerning both the research processes and the data collected, generated and/or re-used during a project's lifetime. Automated solutions and workflows that connect DMPs with the actual data and other contextual information (e.g., publications, fundings) are missing from the landscape. DMPs being submitted as deliverables also limit their findability. In an open and FAIR-enabling research ecosystem information linking between research processes and research outputs is essential. ARGOS tool for FAIR data management contributes to the OpenAIRE Research Graph (RG) and utilises its underlying services and trusted sources to progressively automate validation and automations of Research Data Management (RDM) practices.Source: JOURNAL OF BIOMEDICAL SEMANTICS, vol. 14 (issue 17)
DOI: 10.1186/s13326-023-00297-5
Metrics:
Papadopoulou E, Bardi A, Kakaletris G, Tziotzios D, Manghi P, Manola N
Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes, institutions) and the relationships among them. They present a contextualized view of current research that supports discovery, re-use, reproducibility, monitoring, transparency and omni-comprehensive assessment. A Data Management Plan (DMP) contains information concerning both the research processes and the data collected, generated and/or re-used during a project's lifetime. Automated solutions and workflows that connect DMPs with the actual data and other contextual information (e.g., publications, fundings) are missing from the landscape. DMPs being submitted as deliverables also limit their findability. In an open and FAIR-enabling research ecosystem information linking between research processes and research outputs is essential. ARGOS tool for FAIR data management contributes to the OpenAIRE Research Graph (RG) and utilises its underlying services and trusted sources to progressively automate validation and automations of Research Data Management (RDM) practices.Source: JOURNAL OF BIOMEDICAL SEMANTICS, vol. 14 (issue 17)
DOI: 10.1186/s13326-023-00297-5
Metrics:
See at:
CNR IRIS | jbiomedsem.biomedcentral.com | CNR IRIS
2023
Other
Open Access
OpenAIRE, comunità e servizi per praticare la scienza aperta
Pavone G, Atzori C, Baglioni M, Bardi A, Manghi P, Castelli D
Per praticare la ricerca secondo i principi dell'Open Science sono al contempo necessarie tecnologie - con infrastrutture che consentano e facilitino la collaborazione e lo scambio massivo di informazioni su scala internazionale - e competenze che permettano di massimizzarne uso e risultati. In altre parole occorrono servizi, scambio di competenze e formazione. Su queste direttrici si concentra il lavoro di OpenAIRE (Open Access Infrastructure for Research in Europe), l'infrastruttura europea per la Scienza Aperta che offre servizi tecnologici e una rete europea di scambio e sinergia per favorire la scienza aperta. Avviata come progetto europeo nel 2009 per il monitoraggio dell'Open Access, nel corso degli anni l'iniziativa è stata rifinanziata e il suo ambito di interesse esteso a tutte le componenti dell'Open Science. Nel 2018 si è costituita come organizzazione senza scopo di lucro per garantire una struttura permanente a supporto delle politiche nazionali ed europee per l'Open Science. Il network di OpenAIRE conta oltre 40 membri tra centri di ricerca, università, fondazioni ed enti gestori di servizi distribuiti in tutta Europa. Come comunità di pratica, OpenAIRE ha la missione di costituire e gestire un'infrastruttura che supporti una comunicazione scientifica aperta e sostenibile, fornendo i servizi, le risorse e il coordinamento di iniziative ed esperti necessari per implementare un ambiente comune europeo per la scienza aperta. Per realizzare questa visione, OpenAIRE offre servizi tecnologici, di training e di supporto, coprendo l'intero ciclo di vita della ricerca (la lista completa dei servizi è consultabile su catalogue.openaire.eu). I servizi tecnologici spaziano dalla gestione dei dati al discovery, dalla gestione di riviste al monitoraggio dei risultati della ricerca e dell'adozione di pratiche Open Science. Inoltre la rete internazionale dei NOAD (National Open Access Desk: openaire.eu/contact-noads) promuove la scienza aperta fornendo assistenza e formazione a vari livelli. L'obiettivo è abilitare i vari attori coinvolti nell'attività scientifica nelle pratiche dell'open science e dell'open access organizzando workshop nazionali e training dedicati. I NOADs inoltre forniscono consulenza esperta sulle infrastrutture che supportano i flussi di lavoro per la scienza aperta, nonché per la definizione di politiche per la sua implementazione, quali stesura e aggiornamento di policies istituzionali, individuazione degli obblighi normativi, di adempimenti relativi ai finanziamenti o di strumenti per il Data Management Plan (DMP). Il CNR, in particolare il suo istituto ISTI, in qualità di centro di sviluppo e innovazione tecnologica dell'infrastruttura e di gestore del NOAD Italiano, opera in accordo con la missione di OpenAIRE contribuendo in modo significativo alle sue attività e agli organismi di governo. L'ente offre dunque le sue competenze per garantire il mantenimento, l'operatività e l'innovazione dell'infrastruttura partecipando in iniziative e progetti che contribuiscono alla sostenibilità e all'innovazione dei servizi di questa infrastruttura. Come NOAD, offre formazione e supporto per affrontare problematiche quali la definizione di DMP, il rispetto dei principi "FAIR" per la gestione dei dati, e la stesura di politiche istituzionali. Le attività sono portate avanti in collaborazione con i NOAD in altri paesi europei in modo da massimizzare l'integrazione di soluzioni e politiche a livello europeo.
Pavone G, Atzori C, Baglioni M, Bardi A, Manghi P, Castelli D
Per praticare la ricerca secondo i principi dell'Open Science sono al contempo necessarie tecnologie - con infrastrutture che consentano e facilitino la collaborazione e lo scambio massivo di informazioni su scala internazionale - e competenze che permettano di massimizzarne uso e risultati. In altre parole occorrono servizi, scambio di competenze e formazione. Su queste direttrici si concentra il lavoro di OpenAIRE (Open Access Infrastructure for Research in Europe), l'infrastruttura europea per la Scienza Aperta che offre servizi tecnologici e una rete europea di scambio e sinergia per favorire la scienza aperta. Avviata come progetto europeo nel 2009 per il monitoraggio dell'Open Access, nel corso degli anni l'iniziativa è stata rifinanziata e il suo ambito di interesse esteso a tutte le componenti dell'Open Science. Nel 2018 si è costituita come organizzazione senza scopo di lucro per garantire una struttura permanente a supporto delle politiche nazionali ed europee per l'Open Science. Il network di OpenAIRE conta oltre 40 membri tra centri di ricerca, università, fondazioni ed enti gestori di servizi distribuiti in tutta Europa. Come comunità di pratica, OpenAIRE ha la missione di costituire e gestire un'infrastruttura che supporti una comunicazione scientifica aperta e sostenibile, fornendo i servizi, le risorse e il coordinamento di iniziative ed esperti necessari per implementare un ambiente comune europeo per la scienza aperta. Per realizzare questa visione, OpenAIRE offre servizi tecnologici, di training e di supporto, coprendo l'intero ciclo di vita della ricerca (la lista completa dei servizi è consultabile su catalogue.openaire.eu). I servizi tecnologici spaziano dalla gestione dei dati al discovery, dalla gestione di riviste al monitoraggio dei risultati della ricerca e dell'adozione di pratiche Open Science. Inoltre la rete internazionale dei NOAD (National Open Access Desk: openaire.eu/contact-noads) promuove la scienza aperta fornendo assistenza e formazione a vari livelli. L'obiettivo è abilitare i vari attori coinvolti nell'attività scientifica nelle pratiche dell'open science e dell'open access organizzando workshop nazionali e training dedicati. I NOADs inoltre forniscono consulenza esperta sulle infrastrutture che supportano i flussi di lavoro per la scienza aperta, nonché per la definizione di politiche per la sua implementazione, quali stesura e aggiornamento di policies istituzionali, individuazione degli obblighi normativi, di adempimenti relativi ai finanziamenti o di strumenti per il Data Management Plan (DMP). Il CNR, in particolare il suo istituto ISTI, in qualità di centro di sviluppo e innovazione tecnologica dell'infrastruttura e di gestore del NOAD Italiano, opera in accordo con la missione di OpenAIRE contribuendo in modo significativo alle sue attività e agli organismi di governo. L'ente offre dunque le sue competenze per garantire il mantenimento, l'operatività e l'innovazione dell'infrastruttura partecipando in iniziative e progetti che contribuiscono alla sostenibilità e all'innovazione dei servizi di questa infrastruttura. Come NOAD, offre formazione e supporto per affrontare problematiche quali la definizione di DMP, il rispetto dei principi "FAIR" per la gestione dei dati, e la stesura di politiche istituzionali. Le attività sono portate avanti in collaborazione con i NOAD in altri paesi europei in modo da massimizzare l'integrazione di soluzioni e politiche a livello europeo.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2023
Other
Open Access
OpenAIRE Graph: una risorsa aperta per la scienza aperta
Atzori C, Bardi A, Baglioni M, Manghi P
OpenAIRE Graph (OAG) is a knowledge graph that aggregates information (metadata, relationships) about different entities in the research world, such as publications, datasets, software, funded projects, repositories, and organisations. These entities are interconnected through semantic relationships, such as citations, supplements, similarity, and participation in projects. OAG is an open resource that can be used by funders, organisations, researchers, research communities, and publishers to gain a better understanding of the research landscape and dynamics at various levels, both local and global. As an open and freely accessible resource, produced in accordance with the fundamental values of Open Science as outlined in the UNESCO Recommendation on Open Science, OAG overcomes the use of proprietary data sources, supporting the reform of research assessment, researchers, and organisations as envisaged by the Coalition for Advancing Research Assessment (CoARA). OAG is built from bibliographic records obtained from well-known sources such as Crossref, open access journals registered in DOAJ (Directory of Open Access Journals), ORCID, Microsoft Academic Graph, Datacite, as well as from over 1,000 institutional repositories. The metadata of research products contained in the graph are disambiguated and enriched through full-text and data mining processes, making OAG usable for a variety of purposes, including: Research discovery Research assessment Analysis and/or prediction of research collaborations Support for research policy decision-making OAG is a freely accessible resource: search and discovery features are available through the explore.openaire.eu portal, programmatic integration is available through the HTTP Search API, the complete dataset, as well as other datasets that offer specialised views, are available on Zenodo. The monitor.openaire.eu portal hosts several dashboards dedicated to research organisations and funders that include the results of statistical, bibliometrics, and indicator analyses. Additional information is available at https://graph.openaire.eu, where the data models to which the datasets conform, API documentation, as well as the methodological approach used to build and process OAG are described. OAG can play a significant role in research assessment by providing a more comprehensive and accurate view of research output and impact. By aggregating data from a variety of sources, OAG can provide a more holistic picture of a researcher's or organization's research activities. This can help to identify areas of strength and weakness, as well as potential areas for collaboration. OAG can also be used to track the impact of research over time. By tracking citations, downloads, and other forms of engagement, OAG can help to measure the influence of research and the impact it has on society. This information can be used to inform research funding decisions, as well as to promote the dissemination of research findings. In addition to its quantitative measures, OAG can also provide qualitative insights into research. By analyzing the relationships between different research products, OAG can help to identify emerging trends and areas of collaboration. This information can be used to support research policy development and to promote the cross-fertilization of ideas. In conclusion, OAG is a powerful tool that has the potential to revolutionise the way research is assessed. By providing a more comprehensive and accurate view of research output and impact, OAG can help to make research assessment more fair, transparent, and equitable.
Atzori C, Bardi A, Baglioni M, Manghi P
OpenAIRE Graph (OAG) is a knowledge graph that aggregates information (metadata, relationships) about different entities in the research world, such as publications, datasets, software, funded projects, repositories, and organisations. These entities are interconnected through semantic relationships, such as citations, supplements, similarity, and participation in projects. OAG is an open resource that can be used by funders, organisations, researchers, research communities, and publishers to gain a better understanding of the research landscape and dynamics at various levels, both local and global. As an open and freely accessible resource, produced in accordance with the fundamental values of Open Science as outlined in the UNESCO Recommendation on Open Science, OAG overcomes the use of proprietary data sources, supporting the reform of research assessment, researchers, and organisations as envisaged by the Coalition for Advancing Research Assessment (CoARA). OAG is built from bibliographic records obtained from well-known sources such as Crossref, open access journals registered in DOAJ (Directory of Open Access Journals), ORCID, Microsoft Academic Graph, Datacite, as well as from over 1,000 institutional repositories. The metadata of research products contained in the graph are disambiguated and enriched through full-text and data mining processes, making OAG usable for a variety of purposes, including: Research discovery Research assessment Analysis and/or prediction of research collaborations Support for research policy decision-making OAG is a freely accessible resource: search and discovery features are available through the explore.openaire.eu portal, programmatic integration is available through the HTTP Search API, the complete dataset, as well as other datasets that offer specialised views, are available on Zenodo. The monitor.openaire.eu portal hosts several dashboards dedicated to research organisations and funders that include the results of statistical, bibliometrics, and indicator analyses. Additional information is available at https://graph.openaire.eu, where the data models to which the datasets conform, API documentation, as well as the methodological approach used to build and process OAG are described. OAG can play a significant role in research assessment by providing a more comprehensive and accurate view of research output and impact. By aggregating data from a variety of sources, OAG can provide a more holistic picture of a researcher's or organization's research activities. This can help to identify areas of strength and weakness, as well as potential areas for collaboration. OAG can also be used to track the impact of research over time. By tracking citations, downloads, and other forms of engagement, OAG can help to measure the influence of research and the impact it has on society. This information can be used to inform research funding decisions, as well as to promote the dissemination of research findings. In addition to its quantitative measures, OAG can also provide qualitative insights into research. By analyzing the relationships between different research products, OAG can help to identify emerging trends and areas of collaboration. This information can be used to support research policy development and to promote the cross-fertilization of ideas. In conclusion, OAG is a powerful tool that has the potential to revolutionise the way research is assessed. By providing a more comprehensive and accurate view of research output and impact, OAG can help to make research assessment more fair, transparent, and equitable.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
Graph-based methods for author name disambiguation: a survey
De Bonis M, Falchi F, Manghi P
Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers' activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.Source: PEERJ. COMPUTER SCIENCE., vol. 9
DOI: 10.7717/peerj-cs.1536
Project(s): EOSC Future, OpenAIRE Nexus
Metrics:
De Bonis M, Falchi F, Manghi P
Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers' activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.Source: PEERJ. COMPUTER SCIENCE., vol. 9
DOI: 10.7717/peerj-cs.1536
Project(s): EOSC Future
, OpenAIRE Nexus Metrics:
See at:
PeerJ Computer Science | CNR IRIS | ISTI Repository | peerj.com | CNR IRIS
2023
Conference article
Open Access
A graph neural network approach for evaluating correctness of groups of duplicates
De Bonis M, Minutella F, Falchi F, Manghi P
Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of "groups of duplicates", which can be used to measure the group's accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.DOI: 10.1007/978-3-031-43849-3_18
Project(s): OpenAIRE Nexus
Metrics:
De Bonis M, Minutella F, Falchi F, Manghi P
Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of "groups of duplicates", which can be used to measure the group's accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.DOI: 10.1007/978-3-031-43849-3_18
Project(s): OpenAIRE Nexus
Metrics:
See at:
doi.org | CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS
2023
Other
Open Access
InfraScience research activity report 2023
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud, EOSC Future , TAILOR
Metrics:
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud
, EOSC Future , TAILOR Metrics:
2023
Conference article
Open Access
Tracing data footprints: formal and informal data citations in the scientific literature
Irrera O., Mannocci A., Manghi P., Silvello G.
Data citation has become a prevalent practice within the scientific community, serving the purpose of facilitating data discovery, reproducibility, and credit attribution. Consequently, data has gained significant importance in the scholarly process. Despite its growing prominence, data citation is still at an early stage, with considerable variations in practices observed across scientific domains. Such diversity hampers the ability to consistently analyze, detect, and quantify data citations. We focus on the European Marine Science (MES) community to examine how data is cited in this specific context. We identify four types of data citations: formal, informal, complete, and incomplete. By analyzing the usage of these diverse data citation modalities, we investigate their impact on the widespread adoption of data citation practices.Source: TPDL 2023 - 27th International Conference on Theory and Practice of Digital Libraries, pp. 79–92, Zadar, Croatia, 26-29/09/2023
DOI: 10.1007/978-3-031-43849-3_7
Metrics:
Irrera O., Mannocci A., Manghi P., Silvello G.
Data citation has become a prevalent practice within the scientific community, serving the purpose of facilitating data discovery, reproducibility, and credit attribution. Consequently, data has gained significant importance in the scholarly process. Despite its growing prominence, data citation is still at an early stage, with considerable variations in practices observed across scientific domains. Such diversity hampers the ability to consistently analyze, detect, and quantify data citations. We focus on the European Marine Science (MES) community to examine how data is cited in this specific context. We identify four types of data citations: formal, informal, complete, and incomplete. By analyzing the usage of these diverse data citation modalities, we investigate their impact on the widespread adoption of data citation practices.Source: TPDL 2023 - 27th International Conference on Theory and Practice of Digital Libraries, pp. 79–92, Zadar, Croatia, 26-29/09/2023
DOI: 10.1007/978-3-031-43849-3_7
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA