2025
Other
Open Access
Linking Dante UI: manuale d’uso
Trupiano L., Concordia C., Aloia N., Tomazzoli G., Meghini C.
Questo documento descrive l’interfaccia grafica per l’accesso ai dati del grafo di conoscenza Linking Dante (LiDa). L’interfaccia grafica (GUI), accessibile all’indirizzo https://lida.dantenetwork.it, è stata progettata e sviluppata per fornire a utenti con vari gradi di esperienza un accesso semplificato ai dati del grafo di conoscenza LiDa.
Trupiano L., Concordia C., Aloia N., Tomazzoli G., Meghini C.
Questo documento descrive l’interfaccia grafica per l’accesso ai dati del grafo di conoscenza Linking Dante (LiDa). L’interfaccia grafica (GUI), accessibile all’indirizzo https://lida.dantenetwork.it, è stata progettata e sviluppata per fornire a utenti con vari gradi di esperienza un accesso semplificato ai dati del grafo di conoscenza LiDa.
See at:
CNR IRIS 
| lida.dantenetwork.it | CNR IRIS 
2025
Other
Open Access
Il progetto Linking Dante
Meghini C., Concordia C., Tomazzoli G.
Presentazione dei risultati del progetto Linking Dante.
Meghini C., Concordia C., Tomazzoli G.
Presentazione dei risultati del progetto Linking Dante.
2024
Conference article
Open Access
Per l'interoperabilità e la sostenibilità delle risorse digitali dantesche: il progetto LiDa
Concordia C., Tomazzoli G., Aloia N., Meghini C., Trupiano L.
In questo contributo presentiamo LiDa (Linking Dante), un progetto che mira a promuovere una maggiore interoperabilità e sostenibilità dei dati sulle opere dantesche raccolti in progetti precedenti tramite l’utilizzo di tecnologie e linguaggi del Web Semantico. Dopo un’introduzione in cui definiamo gli scopi del progetto (§1), facciamo cenno ai limiti con cui si scontra l’annotazione in dei dati linguistici e alle potenzialità di una loro implementazione in RDF (§2); introduciamo poi gli elementi fondamentali della nuova ontologia che abbiamo elaborato a tal scopo (§3), e descriviamo la procedura con cui abbiamo realizzato un nuovo grafo di conoscenza che colleghi il testo della Commedia alle risorse linguistiche che lo descrivono (§4); infine, presentiamo le modalità di navigazione e interrogazione di tale grafo (§5) e riflettiamo sui risultati raggiunti e sugli sviluppi futuri (§6).DOI: 10.6092/unibo/amsacta/7927
Metrics:
Concordia C., Tomazzoli G., Aloia N., Meghini C., Trupiano L.
In questo contributo presentiamo LiDa (Linking Dante), un progetto che mira a promuovere una maggiore interoperabilità e sostenibilità dei dati sulle opere dantesche raccolti in progetti precedenti tramite l’utilizzo di tecnologie e linguaggi del Web Semantico. Dopo un’introduzione in cui definiamo gli scopi del progetto (§1), facciamo cenno ai limiti con cui si scontra l’annotazione in dei dati linguistici e alle potenzialità di una loro implementazione in RDF (§2); introduciamo poi gli elementi fondamentali della nuova ontologia che abbiamo elaborato a tal scopo (§3), e descriviamo la procedura con cui abbiamo realizzato un nuovo grafo di conoscenza che colleghi il testo della Commedia alle risorse linguistiche che lo descrivono (§4); infine, presentiamo le modalità di navigazione e interrogazione di tale grafo (§5) e riflettiamo sui risultati raggiunti e sugli sviluppi futuri (§6).DOI: 10.6092/unibo/amsacta/7927
Metrics:
See at:
amsacta.unibo.it | CNR IRIS | AMS Acta | CNR IRIS
2023
Contribution to conference
Open Access
The Social Sciences and Humanities Open Marketplace: contextualising digital resources in a registry
Barbot L, Battaner Moro E, Buddenbohm S, Concordia C, Dolinar M, Durco M, Gray E, Grisot C, Illmayer K, Kirnbauer M, Kleemola M, König A, Kurzmeier M, Mcgillivray B, Parente Boavida C, Schuster C, Vipavc Brvar I, Wnuk M
The SSH Open Marketplace is a discovery portal which pools and contextualises resources for Social Sciences and Humanities research communities: tools, services, training materials, datasets, publications and workflows. This poster presents how this service can provide insights into the use of tools, methods and standards in the DH research communities.DOI: 10.5281/zenodo.8107637
Metrics:
Barbot L, Battaner Moro E, Buddenbohm S, Concordia C, Dolinar M, Durco M, Gray E, Grisot C, Illmayer K, Kirnbauer M, Kleemola M, König A, Kurzmeier M, Mcgillivray B, Parente Boavida C, Schuster C, Vipavc Brvar I, Wnuk M
The SSH Open Marketplace is a discovery portal which pools and contextualises resources for Social Sciences and Humanities research communities: tools, services, training materials, datasets, publications and workflows. This poster presents how this service can provide insights into the use of tools, methods and standards in the DH research communities.DOI: 10.5281/zenodo.8107637
Metrics:
See at:
CNR IRIS | ISTI Repository | www.conftool.pro | ZENODO | CNR IRIS
2023
Other
Open Access
Publishing SSHOC multilingual terminologies
Concordia C
The SSHOC Multilingual Terminologies consist of a set of multilingual metadata concepts and an automatically extracted multilingual Data Stewardship terminology. They have been created in the context of the SSHOC project as case studies, during an activity of investigation on how Language Technologies can help in promoting and facilitating multilingualism in the Social Sciences and Humanities. The SSHOC Multilingual terminologies has been created to evaluate performances of state-of-the-art tools and to derive a set of recommendations as to how best apply them. The talk will present the main recommendations derived from the investigation and the workflow and tools adopted to publish the Terminologies as SKOS resources.DOI: 10.5281/zenodo.7828486
Metrics:
Concordia C
The SSHOC Multilingual Terminologies consist of a set of multilingual metadata concepts and an automatically extracted multilingual Data Stewardship terminology. They have been created in the context of the SSHOC project as case studies, during an activity of investigation on how Language Technologies can help in promoting and facilitating multilingualism in the Social Sciences and Humanities. The SSHOC Multilingual terminologies has been created to evaluate performances of state-of-the-art tools and to derive a set of recommendations as to how best apply them. The talk will present the main recommendations derived from the investigation and the workflow and tools adopted to publish the Terminologies as SKOS resources.DOI: 10.5281/zenodo.7828486
Metrics:
See at:
CNR IRIS | ISTI Repository | zenodo.org | CNR IRIS | CNR IRIS | CNR IRIS
2023
Other
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
SSHOC - D5.5 'Archive in a Box' repository software and proof of concept of centralised installation in the cloud
Wittenberg M, Tykhonov V, Indarto E, Steinhoff W, Huis In 't Veld L, Kasberger S, Conzett P, Concordia C, Kiraly P, Parkola T
Within task 5.2 (Hosting and sharing data repositories) of the SSHOC project, repository software is being developed based on Dataverse, for the sharing and publication of research data within the Social Science and Humanities (SSH) domain. Dataverse is open-source research data repository software developed by the Institute for Quantitative Social Science (IQSS), Harvard University. This document describes the work done by task 5.2, for the development of 'Archive in a Box' repository software and proof of concept of centralised installation in the cloud. The 'Archive in a Box' makes the installation of Dataverse repository software easier for institutes with a lack of technical staff. This document describes the advantages of such a package. Additionally, task 5.2 worked on a proof of concept of a centralised cloud installation of the Dataverse software at the Google cloud infrastructure of CESSDA ERIC. A cloud installation makes it possible to automate the installation and keep the application up and running, for instance by scaling up or down resources when needed. Another advantage of a cloud orchestrator is the ability to start a new component or part of the application, if it should fail for some reason. Furthermore, task 5.2 developed several additional functionalities to the Dataverse software to make the software more compliant to the needs of the SSH communities in Europe. This document describes the results of the accomplished work, and refers to technical details published in GitHub repositories. Already many of the results of task 5.2 are used by the European and Global Dataverse community and some functionalities are integrated in the new versions of the Dataverse master branch of Harvard.DOI: 10.5281/zenodo.6676391
Project(s): SSHOC
Metrics:
Wittenberg M, Tykhonov V, Indarto E, Steinhoff W, Huis In 't Veld L, Kasberger S, Conzett P, Concordia C, Kiraly P, Parkola T
Within task 5.2 (Hosting and sharing data repositories) of the SSHOC project, repository software is being developed based on Dataverse, for the sharing and publication of research data within the Social Science and Humanities (SSH) domain. Dataverse is open-source research data repository software developed by the Institute for Quantitative Social Science (IQSS), Harvard University. This document describes the work done by task 5.2, for the development of 'Archive in a Box' repository software and proof of concept of centralised installation in the cloud. The 'Archive in a Box' makes the installation of Dataverse repository software easier for institutes with a lack of technical staff. This document describes the advantages of such a package. Additionally, task 5.2 worked on a proof of concept of a centralised cloud installation of the Dataverse software at the Google cloud infrastructure of CESSDA ERIC. A cloud installation makes it possible to automate the installation and keep the application up and running, for instance by scaling up or down resources when needed. Another advantage of a cloud orchestrator is the ability to start a new component or part of the application, if it should fail for some reason. Furthermore, task 5.2 developed several additional functionalities to the Dataverse software to make the software more compliant to the needs of the SSH communities in Europe. This document describes the results of the accomplished work, and refers to technical details published in GitHub repositories. Already many of the results of task 5.2 are used by the European and Global Dataverse community and some functionalities are integrated in the new versions of the Dataverse master branch of Harvard.DOI: 10.5281/zenodo.6676391
Project(s): SSHOC
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
Diary of our initiatory journey on the continent of data citation in SSH
Larrousse N, Gray Ej, Concordia C
If citation is a common practice for publications, it is relatively new for data especially in SSH. This paper will present the work carried out during the SSHOC project about data citation in general and more precisely how to make them actionable. The metaphor of a travel journal of an expedition seemed appropriate to us to present this work carried out during the SSHOC project.DOI: 10.5281/zenodo.6921457
Project(s): SSHOC
Metrics:
Larrousse N, Gray Ej, Concordia C
If citation is a common practice for publications, it is relatively new for data especially in SSH. This paper will present the work carried out during the SSHOC project about data citation in general and more precisely how to make them actionable. The metaphor of a travel journal of an expedition seemed appropriate to us to present this work carried out during the SSHOC project.DOI: 10.5281/zenodo.6921457
Project(s): SSHOC
Metrics:
See at:
CNR IRIS | ISTI Repository | zenodo.org | CNR IRIS
2022
Conference article
Open Access
The SSH Data Citation Service, a tool to explore and collect citation metadata
Concordia C, Larrousse N, Gray E
This paper presents the SSH Data Citation Service (DCS), a software tool that provides functionalities to find, collect and analyse metadata related to digital objects, in particular datasets, referred to in citation strings. Starting from the citation string of a dataset, the DCS aggregates metadata related to the data from different sources: the repository hosting the dataset, PID Registration Agencies and Knowledge Graphs and gives a unified view of information about datasets coming from these sources. The DCS has been designed and developed in the Social Sciences & Humanities Open Cloud (SSHOC) project. It has been used in a project activity as a tool to help investigate approaches adopted for data citation by Social Sciences and Humanities organisations managing data repositories, and as an utility to help data managers to create citation metadata. The paper presents motivations underlying the creation of the tool, the design principles adopted, an overall description of the functionalities of the current release and a summary of ongoing activities.DOI: 10.1007/978-3-031-16802-4_32
Project(s): SSHOC
Metrics:
Concordia C, Larrousse N, Gray E
This paper presents the SSH Data Citation Service (DCS), a software tool that provides functionalities to find, collect and analyse metadata related to digital objects, in particular datasets, referred to in citation strings. Starting from the citation string of a dataset, the DCS aggregates metadata related to the data from different sources: the repository hosting the dataset, PID Registration Agencies and Knowledge Graphs and gives a unified view of information about datasets coming from these sources. The DCS has been designed and developed in the Social Sciences & Humanities Open Cloud (SSHOC) project. It has been used in a project activity as a tool to help investigate approaches adopted for data citation by Social Sciences and Humanities organisations managing data repositories, and as an utility to help data managers to create citation metadata. The paper presents motivations underlying the creation of the tool, the design principles adopted, an overall description of the functionalities of the current release and a summary of ongoing activities.DOI: 10.1007/978-3-031-16802-4_32
Project(s): SSHOC
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
Towards FAIR data access
Broeder D, Elbers W, Gawor M, Concordia C, Larrousse N, Van Uytvanck D
Background In the past decade many different national, EU and global projects have been successful in raising awareness about Open Science and the importance of making data findable and accessible such as stated in the FAIR principles (Wilkinson et al. 2016).In this respect, there have been many advances with respect to options for discovering data. A multitude of either thematic or general catalogues are providing faceted browsing interfaces for humans and Application Programming Interfaces (APIs) for use by machines and similarly, data-citations in publications offer references to resources hosted by repositories. However, using such catalogues and data-citations, researchers are not guaranteed to obtain access to the data itself. Mostly the resource link in the catalogue (and also in the metadata) or citation is a "landing-page", a description of the resource meant for human consumption. The landing-page may contain instructions how to access or download the resource itself but usually it is difficult to parse by machines.FAIR data accessThus the approach sketched above does not meet the requirements in scenarios where applications need assured and quick access to data. Also the FAIR principles interpretation from GO FAIR states*1 that these "emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data." The requirement for providing a Persistent Identifier (PID) for a resource*2, is mostly interpreted as meaning a PID for the resource's metadata or landing-page only. Note that we ignore the need for user authentication and authorization prior to accessing data, here we will only consider data that is 'freely' accessible.To improve the situation with respect to machine data accessibility a number of technologies and approaches that have been discussed in the CLARIN and Social Sciences and Humanities (SSH) infrastructure domain can be useful. We present some and comment on their suitability.SignpostingSignposting*3 is a technology proposed by van de Sompel (Sompel and Nelson 2015) to release relevant technical and bibliographical attributes from a resource URI. It's well described, and uses the HTTP protocol to provide additional information via HTTP Link Headers*4. Alternatively, for HTML type resources, the information may also be provided in HTML Link elements. In the CLARIN community the signposting concept was accepted, but its proposed implementation deviated from van de Sompel and made it less dependent on the HTTP protocol (Arnold et al. 2021). However on the downside, the signposting information is embedded in the CLARIN specific Component Metadata (CMDI) (Broeder et al. 2012), and so makes it CLARIN specific, or at least requires clients to have specific knowledge about CMDI.CLARIN Digital Object Gateway (DOG)One approach that is currently worked on for the CLARIN research infrastructure is the creation of a DOG library*5 and (later) a service that provides a proxy gateway from the resource PID to the actual data. DOG uses implicit knowledge about the different repository solutions that are used by the CLARIN B-type centres*6 and some repositories outside the CLARIN infrastructure. DOG works in two steps: first obtaining metadata from the resource PID and secondly extracting resource links from the metadata. Each of the repositories registered within DOG has a minimal configuration specifying how to parse fields of interest from the resource's metadata. For B-type CLARIN centres DOG uses content negotiation as the primary way of obtaining the metadata in CMDI format. For repositories outside the CLARIN infrastructure, DOG primarily relies on the API provided by the repository in order to access metadata and data resources. The DOG solution does have scalability problems, but within the limited domain of CLARIN centres, it can offer a solution until a better one becomes available.Limited PID kernel informationThe (limited) PID kernel information approach assumes that for every Digital Object (DO) (Berg-Cross et al. 2015) and its metadata a Handle type PID (CNRI 2020) is issued and that the Handle information record can be used to store and associate additional important information with the (meta) data PID using handle value types such as for example a checksum and references to the data or metadata. This is a simplification of the architecture proposed in the work done in RDA context: PID Kernel Info recommendations (Weigel et al. 2018). Consistent use of Handle information records could solve the data access problem, but just as for the signposting strategy, it requires strong discipline to maintain the additional information source. Examples from smaller projects and repositories exist that do manage this information in the Handle record eg. the DARIAH-DE repository*7. FAIR Digital Objects (FDO)FDOs*8 attempt to overcome the data management challenges posed by the heterogeneity and complexity of data using a combination of abstraction, virtualization and encapsulation (Schwardmann 2020). In practice, in the context of our access to data problem, the FDO solution can be seen as both a generalization and upgrade of the PID kernel information approach. The key characteristics here are the (conceptual) encapsulation of data objects with data structure and services that allow aware applications to recognize the data objects metadata and bitstream format, and process as intended by the programmer. Eligible data processing services, either general ones from communities, can be found through the FDO typing mechanism, or can be directly linked from the FDO.A rich set of FDO attributes permit signaling machines processing FDOs where and how to access bitstream data including for instance additional information about supported protocols and APIs.What to do?For our community and in our collaboration with others, we need solutions now but would prefer not to invest and get closed in unscalable technologies.We would propose to combine the DOG approach with signposting. First testing URIs (obtained by resolving the Handle PID) for the presence of HTTP Link Headers. If these are missing, (extended) DOG could use its idiosyncratic workflow. Long term we see advantages of the general, scalable and protocol independent approach that FDOs offer. Hybrid solutions are conceivable where FDO proxies can sit between the FDO machinery and data hosted by signposting compliant repositories.DOI: 10.3897/rio.8.e94386
Metrics:
Broeder D, Elbers W, Gawor M, Concordia C, Larrousse N, Van Uytvanck D
Background In the past decade many different national, EU and global projects have been successful in raising awareness about Open Science and the importance of making data findable and accessible such as stated in the FAIR principles (Wilkinson et al. 2016).In this respect, there have been many advances with respect to options for discovering data. A multitude of either thematic or general catalogues are providing faceted browsing interfaces for humans and Application Programming Interfaces (APIs) for use by machines and similarly, data-citations in publications offer references to resources hosted by repositories. However, using such catalogues and data-citations, researchers are not guaranteed to obtain access to the data itself. Mostly the resource link in the catalogue (and also in the metadata) or citation is a "landing-page", a description of the resource meant for human consumption. The landing-page may contain instructions how to access or download the resource itself but usually it is difficult to parse by machines.FAIR data accessThus the approach sketched above does not meet the requirements in scenarios where applications need assured and quick access to data. Also the FAIR principles interpretation from GO FAIR states*1 that these "emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data." The requirement for providing a Persistent Identifier (PID) for a resource*2, is mostly interpreted as meaning a PID for the resource's metadata or landing-page only. Note that we ignore the need for user authentication and authorization prior to accessing data, here we will only consider data that is 'freely' accessible.To improve the situation with respect to machine data accessibility a number of technologies and approaches that have been discussed in the CLARIN and Social Sciences and Humanities (SSH) infrastructure domain can be useful. We present some and comment on their suitability.SignpostingSignposting*3 is a technology proposed by van de Sompel (Sompel and Nelson 2015) to release relevant technical and bibliographical attributes from a resource URI. It's well described, and uses the HTTP protocol to provide additional information via HTTP Link Headers*4. Alternatively, for HTML type resources, the information may also be provided in HTML Link elements. In the CLARIN community the signposting concept was accepted, but its proposed implementation deviated from van de Sompel and made it less dependent on the HTTP protocol (Arnold et al. 2021). However on the downside, the signposting information is embedded in the CLARIN specific Component Metadata (CMDI) (Broeder et al. 2012), and so makes it CLARIN specific, or at least requires clients to have specific knowledge about CMDI.CLARIN Digital Object Gateway (DOG)One approach that is currently worked on for the CLARIN research infrastructure is the creation of a DOG library*5 and (later) a service that provides a proxy gateway from the resource PID to the actual data. DOG uses implicit knowledge about the different repository solutions that are used by the CLARIN B-type centres*6 and some repositories outside the CLARIN infrastructure. DOG works in two steps: first obtaining metadata from the resource PID and secondly extracting resource links from the metadata. Each of the repositories registered within DOG has a minimal configuration specifying how to parse fields of interest from the resource's metadata. For B-type CLARIN centres DOG uses content negotiation as the primary way of obtaining the metadata in CMDI format. For repositories outside the CLARIN infrastructure, DOG primarily relies on the API provided by the repository in order to access metadata and data resources. The DOG solution does have scalability problems, but within the limited domain of CLARIN centres, it can offer a solution until a better one becomes available.Limited PID kernel informationThe (limited) PID kernel information approach assumes that for every Digital Object (DO) (Berg-Cross et al. 2015) and its metadata a Handle type PID (CNRI 2020) is issued and that the Handle information record can be used to store and associate additional important information with the (meta) data PID using handle value types such as for example a checksum and references to the data or metadata. This is a simplification of the architecture proposed in the work done in RDA context: PID Kernel Info recommendations (Weigel et al. 2018). Consistent use of Handle information records could solve the data access problem, but just as for the signposting strategy, it requires strong discipline to maintain the additional information source. Examples from smaller projects and repositories exist that do manage this information in the Handle record eg. the DARIAH-DE repository*7. FAIR Digital Objects (FDO)FDOs*8 attempt to overcome the data management challenges posed by the heterogeneity and complexity of data using a combination of abstraction, virtualization and encapsulation (Schwardmann 2020). In practice, in the context of our access to data problem, the FDO solution can be seen as both a generalization and upgrade of the PID kernel information approach. The key characteristics here are the (conceptual) encapsulation of data objects with data structure and services that allow aware applications to recognize the data objects metadata and bitstream format, and process as intended by the programmer. Eligible data processing services, either general ones from communities, can be found through the FDO typing mechanism, or can be directly linked from the FDO.A rich set of FDO attributes permit signaling machines processing FDOs where and how to access bitstream data including for instance additional information about supported protocols and APIs.What to do?For our community and in our collaboration with others, we need solutions now but would prefer not to invest and get closed in unscalable technologies.We would propose to combine the DOG approach with signposting. First testing URIs (obtained by resolving the Handle PID) for the presence of HTTP Link Headers. If these are missing, (extended) DOG could use its idiosyncratic workflow. Long term we see advantages of the general, scalable and protocol independent approach that FDOs offer. Hybrid solutions are conceivable where FDO proxies can sit between the FDO machinery and data hosted by signposting compliant repositories.DOI: 10.3897/rio.8.e94386
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
SSHOC - D3.5 Report on citation enabled SSH catalogues and SSH citation exploitation
Larrousse N, Gray E, Broeder D, Concordia C, Brase J, Papadopoulou A
Citation is a pillar for the construction of knowledge. By creating proper citations in a standardized way researchers can constitute a mesh of linked information for various purposes (from credit to reuse). This becomes increasingly important as the SSHOC Task 3.4 team confronts the realities of Social Sciences and Humanities Research in a digital age, when machine actionability takes on a renewed and vital importance. After conducting an inventory of data citation practices (SSHOC D3.2 "Inventory of SSH citation practices, and choice for SSHOC citation formats and implementation planning") and analysing the citation of data in DH1 2019 conference abstracts in order to build specifications for the citation prototype, the team discovered a very diverse landscape of data repositories. As a result, the team developed recommendations for citation in coordination with SSHOC Work Package 2 (Communication, Dissemination, and Impact), validated by external reviewers. These recommendations were used to guide a deeper analysis of citation practices in various SSH repositories and how they correspond to these recommendations in order to have a better idea of the current situation. This analysis was carried out in both a quantitative and qualitative fashion. For the qualitative part, the main goal was to describe, in detail, a selection of repositories representative of the SSH domain. The choice of repositories was made in collaboration with the SSHOC network in order to have a good representation of the very diverse contexts in SSH. This qualitative analysis focused on how these repositories were constructed to provide data citation services in detail. For the quantitative part, a list of repositories was already established by SSHOC Task 8.2 "Trust & Quality Assurance" and the team took this opportunity to establish synergies and extract a list of repositories to be checked according to defined criteria regarding data citation. The analysis checked 85 repositories from a list of 125 against a set of 7 criteria. In order to facilitate the work, the team used the citation viewer which is part of the prototype mentioned above. The main result of this quick study is that while there are positive signs, especially with respect to the use of landing pages and Persistent Identifiers (PIDs), there is quite a bit of room for improvement as a lot of repositories do not provide machine actionable information. This makes the prototype the Task 3.4 team is currently developing to create actionable citations all the more useful. It also appears from this work that it will be necessary to manually curate some citations in order to enrich them and make them actionable as the information is not always directly available (e.g., a landing page provides a link to a page which contains metadata expressed in another format). The result of this study will feed the development of the citation prototype developed in Task 3.4 and also liaise with SSHOC WP7 "Creating the SSH Open Marketplace" to integrate citations in SSH Open Marketplace2 with a "Cite As" property in the backend and Cite As box in the frontend interface. Another link exists with the very similar work currently being carried out in CLARIN for the Digital Object Gateway.DOI: 10.5281/zenodo.5603306
DOI: 10.5281/zenodo.5603305
Project(s): SSHOC
Metrics:
Larrousse N, Gray E, Broeder D, Concordia C, Brase J, Papadopoulou A
Citation is a pillar for the construction of knowledge. By creating proper citations in a standardized way researchers can constitute a mesh of linked information for various purposes (from credit to reuse). This becomes increasingly important as the SSHOC Task 3.4 team confronts the realities of Social Sciences and Humanities Research in a digital age, when machine actionability takes on a renewed and vital importance. After conducting an inventory of data citation practices (SSHOC D3.2 "Inventory of SSH citation practices, and choice for SSHOC citation formats and implementation planning") and analysing the citation of data in DH1 2019 conference abstracts in order to build specifications for the citation prototype, the team discovered a very diverse landscape of data repositories. As a result, the team developed recommendations for citation in coordination with SSHOC Work Package 2 (Communication, Dissemination, and Impact), validated by external reviewers. These recommendations were used to guide a deeper analysis of citation practices in various SSH repositories and how they correspond to these recommendations in order to have a better idea of the current situation. This analysis was carried out in both a quantitative and qualitative fashion. For the qualitative part, the main goal was to describe, in detail, a selection of repositories representative of the SSH domain. The choice of repositories was made in collaboration with the SSHOC network in order to have a good representation of the very diverse contexts in SSH. This qualitative analysis focused on how these repositories were constructed to provide data citation services in detail. For the quantitative part, a list of repositories was already established by SSHOC Task 8.2 "Trust & Quality Assurance" and the team took this opportunity to establish synergies and extract a list of repositories to be checked according to defined criteria regarding data citation. The analysis checked 85 repositories from a list of 125 against a set of 7 criteria. In order to facilitate the work, the team used the citation viewer which is part of the prototype mentioned above. The main result of this quick study is that while there are positive signs, especially with respect to the use of landing pages and Persistent Identifiers (PIDs), there is quite a bit of room for improvement as a lot of repositories do not provide machine actionable information. This makes the prototype the Task 3.4 team is currently developing to create actionable citations all the more useful. It also appears from this work that it will be necessary to manually curate some citations in order to enrich them and make them actionable as the information is not always directly available (e.g., a landing page provides a link to a page which contains metadata expressed in another format). The result of this study will feed the development of the citation prototype developed in Task 3.4 and also liaise with SSHOC WP7 "Creating the SSH Open Marketplace" to integrate citations in SSH Open Marketplace2 with a "Cite As" property in the backend and Cite As box in the frontend interface. Another link exists with the very similar work currently being carried out in CLARIN for the Digital Object Gateway.DOI: 10.5281/zenodo.5603306
DOI: 10.5281/zenodo.5603305
Project(s): SSHOC
Metrics:
See at:
ZENODO | ZENODO | CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
Dataverse: switchboard plugin
Concordia C
Overall description of the 'dataverse-lrs' plugin, a plugin enabling the integration of Dataverse repositories with the CLARIN Language Resource Switchboard (LRS) software, developed in the "Social Sciences and Humanities Open Cloud (SSHOC)" project. The software is available in the SSHOC GitHub repository: https://github.com/SSHOC/dataverse-lrsProject(s): SSHOC
Concordia C
Overall description of the 'dataverse-lrs' plugin, a plugin enabling the integration of Dataverse repositories with the CLARIN Language Resource Switchboard (LRS) software, developed in the "Social Sciences and Humanities Open Cloud (SSHOC)" project. The software is available in the SSHOC GitHub repository: https://github.com/SSHOC/dataverse-lrsProject(s): SSHOC
See at:
CNR IRIS | ISTI Repository | www.clarin.eu | CNR IRIS
2021
Software
Open Access
Dataverse-LRS: integrating Dataverse with the CLARIN Language Resource Switchboard
Concordia C
The "dataverse-lrs" software is a plugin that can be installed in a Dataverse repository to enable the integration in the Dataverse GUI of the CLARIN Language Resource Switchboard (LRS). A Dataverse repository is implemented using an open-source software platform, developed and maintained by the Harvard Institute for Quantitative Social Science (IQSS) (https://dataverse.harvard.edu/), the CLARIN LRS (https://switchboard.clarin.eu) can be seen as a Virtual Tool Registry: for a given resource, it identifies all tools that can process the resource, sorts the tools in terms of the tasks they perform, and presents a task-oriented list of those tools, each tool of the list can be invoked and the resource is automatically processed. The 'dataverse-lrs' software has been developed in the Social Sciences & Humanities Open Cloud (SSHOC) project and is described in the deliverable "3.8 Implementation report and available SSHOC Switchboard and VCR services". The software has been also presented during the CLARIN Centre Meeting 2021 (https://www.clarin.eu/event/2021/centre-meeting-2021).Project(s): SSHOC
Concordia C
The "dataverse-lrs" software is a plugin that can be installed in a Dataverse repository to enable the integration in the Dataverse GUI of the CLARIN Language Resource Switchboard (LRS). A Dataverse repository is implemented using an open-source software platform, developed and maintained by the Harvard Institute for Quantitative Social Science (IQSS) (https://dataverse.harvard.edu/), the CLARIN LRS (https://switchboard.clarin.eu) can be seen as a Virtual Tool Registry: for a given resource, it identifies all tools that can process the resource, sorts the tools in terms of the tasks they perform, and presents a task-oriented list of those tools, each tool of the list can be invoked and the resource is automatically processed. The 'dataverse-lrs' software has been developed in the Social Sciences & Humanities Open Cloud (SSHOC) project and is described in the deliverable "3.8 Implementation report and available SSHOC Switchboard and VCR services". The software has been also presented during the CLARIN Centre Meeting 2021 (https://www.clarin.eu/event/2021/centre-meeting-2021).Project(s): SSHOC
See at:
github.com | CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
SSHOC - D7.4 Marketplace - Data population & curation
Gray E, Larrousse N, Petitfils C, Barbot L, Fischer F, Durco M, Illmayer K, Concordia C, Konig A, Van Uytvanck D, Buddenbohm S
The SSH Open Marketplace is a discovery portal for digital tools, services, and methods in the Social Sciences and Humanities (SSH), with an emphasis placed on placing these resources in their research context. It is the SSH component of the EOSC Marketplace. Previous initiatives have shown that curation of content is the key element to maintain the attractiveness and the utility of a discovery portal like the SSH Open Marketplace.1 Thus at each stage of the development of the Marketplace, from the first ingestion of existing catalogues to the curation sprint, curation has been at the heart of conception. This emphasis, based on previous examples and the work done throughout the SSHOC project, has led to profound changes in the data model, definition of quality criteria, standardization of the content as the team adapts the machinery of the Marketplace based on lessons learned. This continuous work has laid the foundation of the development of Editorial Guidelines which will be used by future curation actors as well as the definition of their different curation roles and the organisation of their relationship.DOI: 10.5281/zenodo.5783358
Project(s): SSHOC
Metrics:
Gray E, Larrousse N, Petitfils C, Barbot L, Fischer F, Durco M, Illmayer K, Concordia C, Konig A, Van Uytvanck D, Buddenbohm S
The SSH Open Marketplace is a discovery portal for digital tools, services, and methods in the Social Sciences and Humanities (SSH), with an emphasis placed on placing these resources in their research context. It is the SSH component of the EOSC Marketplace. Previous initiatives have shown that curation of content is the key element to maintain the attractiveness and the utility of a discovery portal like the SSH Open Marketplace.1 Thus at each stage of the development of the Marketplace, from the first ingestion of existing catalogues to the curation sprint, curation has been at the heart of conception. This emphasis, based on previous examples and the work done throughout the SSHOC project, has led to profound changes in the data model, definition of quality criteria, standardization of the content as the team adapts the machinery of the Marketplace based on lessons learned. This continuous work has laid the foundation of the development of Editorial Guidelines which will be used by future curation actors as well as the definition of their different curation roles and the organisation of their relationship.DOI: 10.5281/zenodo.5783358
Project(s): SSHOC
Metrics:
See at:
ZENODO | CNR IRIS | ISTI Repository | CNR IRIS
2021
Software
Metadata Only Access
SSHOC data stewardship terminology and metadata SKOSifying mapping
Trupiano L, Concordia C
A Jupyter Notebook implementing a simple parser used to transform the Multilingual Data Stewardship terminology and Metadata, created in the Task 3.1 of the SSHOC project, into SKOS resources. The parser transforms the content in SKOS data following a set of mapping rules, the result is stored in two Turtle files.Project(s): SSHOC
Trupiano L, Concordia C
A Jupyter Notebook implementing a simple parser used to transform the Multilingual Data Stewardship terminology and Metadata, created in the Task 3.1 of the SSHOC project, into SKOS resources. The parser transforms the content in SKOS data following a set of mapping rules, the result is stored in two Turtle files.Project(s): SSHOC
See at:
hdl.handle.net | CNR IRIS
2020
Other
Restricted
Catalogue of E-RIHS resources and services
Meghini C., Benassi L., Galeotti M., Bartalesi Lenzi V., Concordia C., Castillejo M., Calvo C., Andreassi E., Striova J.
The Deliverable 8.1 describes the work carried in Task 8.3 "Catalogue of E-RIHS resources and services". With the term resource we intend any entity that may be of interest to a Infrastructure such as the one that the E-RIHS PP project is setting up, i.e. mainly datasets and services, but also people, organizations, instruments, and other entities. The catalogue aims at providing researchers with information related to Heritage Science resources available within E-RIHS through a formal model that helps categorising them according to their different features. Such features will also be used for discovering the resources in the Catalogue. The catalogue structure was designed to serve as the basis for accounting in-kind contributions and, in perspective, to be a single access point for resource discovery and access services.Project(s): E-RIHS PP
Meghini C., Benassi L., Galeotti M., Bartalesi Lenzi V., Concordia C., Castillejo M., Calvo C., Andreassi E., Striova J.
The Deliverable 8.1 describes the work carried in Task 8.3 "Catalogue of E-RIHS resources and services". With the term resource we intend any entity that may be of interest to a Infrastructure such as the one that the E-RIHS PP project is setting up, i.e. mainly datasets and services, but also people, organizations, instruments, and other entities. The catalogue aims at providing researchers with information related to Heritage Science resources available within E-RIHS through a formal model that helps categorising them according to their different features. Such features will also be used for discovering the resources in the Catalogue. The catalogue structure was designed to serve as the basis for accounting in-kind contributions and, in perspective, to be a single access point for resource discovery and access services.Project(s): E-RIHS PP
2020
Conference article
Open Access
Store Scientific Workflows Data in SSHOC Repository
Concordia C, Meghini C, Benedetti F
Today scientific workflows are used by scientists as a way to define automated, scalable, and portable in-silico experiments. Having a formal description of an experiment can improve replicability and reproducibility of the experiment. However, simply storing and publishing the workflow may be not enough, an accurate management of provenance data generated during workflow life cycle is crucial to achieve reproducibility. This document presents the activity being carried out by CNR-ISTI in task 5.2 of the SSHOC project to add to the repository service developed in the task, functionalities to store, access and manage `workflow data' in order to improve replicability and reproducibility of e-science experiments.Project(s): SSHOC
Concordia C, Meghini C, Benedetti F
Today scientific workflows are used by scientists as a way to define automated, scalable, and portable in-silico experiments. Having a formal description of an experiment can improve replicability and reproducibility of the experiment. However, simply storing and publishing the workflow may be not enough, an accurate management of provenance data generated during workflow life cycle is crucial to achieve reproducibility. This document presents the activity being carried out by CNR-ISTI in task 5.2 of the SSHOC project to add to the repository service developed in the task, functionalities to store, access and manage `workflow data' in order to improve replicability and reproducibility of e-science experiments.Project(s): SSHOC
See at:
CNR IRIS | ISTI Repository | www.aclweb.org | CNR IRIS
2020
Other
Open Access
AIMH research activities 2020
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.DOI: 10.32079/isti-ar-2020/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.DOI: 10.32079/isti-ar-2020/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS