Periodic Reporting for period 1 - n-Dame_Heritage (n-Dimensional analysis and memorisation ecosystem for building cathedrals of knowledge in Heritage Science)
Período documentado: 2022-09-01 hasta 2025-02-28
Research on heritage transforms the interaction between material objects and multidisciplinary studies into a vector for the production of collective knowledge. Our innovative approach to computational modeling and digitization leverages the scientific project of Notre-Dame de Paris, mobilizing specialists from various fields (archaeology, anthropology, architecture, history, chemistry, physics, computer science) to develop a data corpus reflecting current scientific practices in heritage studies in the digital age.
We aspire to transcend mere digitization of physical objects to embrace an in-depth knowledge of them, examining how the characteristics of material objects and the associated knowledge mutually nourish each other through the prism of research.
Scientifically, our ambition is to introduce approaches for the correlation of multidimensional data to synthesize, structure, and interpret a vast array of scientific resources, focusing on four fundamental axes: space, form, time, and domain of knowledge. These axes are explored through geometrico-visual representation, annotation and classification of forms, temporal tracking, and the study of thematic and disciplinary intersections.
Methodologically, we are committed to documenting and deciphering multidisciplinary scientific approaches, shedding light on how various actors produce, use, and interconnect digital data related to their study subjects. The challenge is to reveal the nature of emerging knowledge, its disciplinary specificities, and its links with the attributes of the objects analyzed.
Technologically, we are working towards the creation of an innovative digital ecosystem. This socio-technical system integrates a suite of collaborative tools for the acquisition, categorization, and annotation of resources, fostering the design of an autonomous correlation engine that facilitates the integration and dynamic interrelation of data and multiple scientific perspectives.
We aspire to transcend mere digitization of physical objects to embrace an in-depth knowledge of them, examining how the characteristics of material objects and the associated knowledge mutually nourish each other through the prism of research.
Scientifically, our ambition is to introduce approaches for the correlation of multidimensional data to synthesize, structure, and interpret a vast array of scientific resources, focusing on four fundamental axes: space, form, time, and domain of knowledge. These axes are explored through geometrico-visual representation, annotation and classification of forms, temporal tracking, and the study of thematic and disciplinary intersections.
Methodologically, we are committed to documenting and deciphering multidisciplinary scientific approaches, shedding light on how various actors produce, use, and interconnect digital data related to their study subjects. The challenge is to reveal the nature of emerging knowledge, its disciplinary specificities, and its links with the attributes of the objects analyzed.
Technologically, we are working towards the creation of an innovative digital ecosystem. This socio-technical system integrates a suite of collaborative tools for the acquisition, categorization, and annotation of resources, fostering the design of an autonomous correlation engine that facilitates the integration and dynamic interrelation of data and multiple scientific perspectives.
At this stage of the project, our primary focus has been the development of a socio-technical digital ecosystem that integrates multi-scale, evolving representations of Notre-Dame de Paris with the extensive knowledge base generated by the collaborative scientific effort surrounding its restoration. This initiative bridges technological innovation and interdisciplinary collaboration, uniting diverse stakeholders within a shared framework for data and knowledge production.
On the social dimension, a key milestone has been the creation of a collaborative platform that integrates research methodologies and findings from multiple disciplines. This effort has culminated in a comprehensive collection of resources and methods aimed at fostering interdisciplinary understanding and advancing the collective study of the cathedral. Technologically, we have established an integrated software platform for data management, visualization, and semantic enrichment.
The platform’s growing digital resource collection encompasses a wide range of data, documenting the cathedral across multiple temporal states. This corpus includes surveys, structural analyses, and anthropological data, offering a multi-dimensional view of the site. Collaboration with the working groups has ensured comprehensive data descriptions, which will be made available as open access beginning in mid 2025, following the embargo period.
By deploying tailored solutions for digitizing large collections of elements, we have generated consistent and comparable datasets. Our efforts have also advanced the integration of spatial and temporal data streams.
The project’s initial steps toward transforming raw data into disciplinary knowledge have been realized through case studies, such as the hypothetical reconstruction of a collapsed arch and the formalization of structural behavior analyses.
In parallel with our core work packages, we have developed innovative methodologies that enhance the production and analysis of multidisciplinary data and knowledge. These efforts focus on enriching data with semantic layers, tracing the process from initial scientific inquiries to final knowledge production. Through regular workshops and continuous dialogue, we aligned methodologies and objectives across disciplines, harmonizing terminologies and analytical techniques. Extending this approach, we have constructed a collaborative thesaurus containing over 13,000 concepts across nine domains, supporting consistent and interdisciplinary data management.
To explore interactions between scientists and material objects, we leveraged the Aioli platform for large-scale 2D/3D annotation experiments. This platform allowed us to spatially organize tens of thousands of photographs representing various temporal states of the cathedral. By integrating thematic descriptive structures and controlled vocabularies, we captured interdisciplinary observations, creating a structured dataset that now includes nearly 14,000 annotations spanning over 400 collaborative projects.
On the social dimension, a key milestone has been the creation of a collaborative platform that integrates research methodologies and findings from multiple disciplines. This effort has culminated in a comprehensive collection of resources and methods aimed at fostering interdisciplinary understanding and advancing the collective study of the cathedral. Technologically, we have established an integrated software platform for data management, visualization, and semantic enrichment.
The platform’s growing digital resource collection encompasses a wide range of data, documenting the cathedral across multiple temporal states. This corpus includes surveys, structural analyses, and anthropological data, offering a multi-dimensional view of the site. Collaboration with the working groups has ensured comprehensive data descriptions, which will be made available as open access beginning in mid 2025, following the embargo period.
By deploying tailored solutions for digitizing large collections of elements, we have generated consistent and comparable datasets. Our efforts have also advanced the integration of spatial and temporal data streams.
The project’s initial steps toward transforming raw data into disciplinary knowledge have been realized through case studies, such as the hypothetical reconstruction of a collapsed arch and the formalization of structural behavior analyses.
In parallel with our core work packages, we have developed innovative methodologies that enhance the production and analysis of multidisciplinary data and knowledge. These efforts focus on enriching data with semantic layers, tracing the process from initial scientific inquiries to final knowledge production. Through regular workshops and continuous dialogue, we aligned methodologies and objectives across disciplines, harmonizing terminologies and analytical techniques. Extending this approach, we have constructed a collaborative thesaurus containing over 13,000 concepts across nine domains, supporting consistent and interdisciplinary data management.
To explore interactions between scientists and material objects, we leveraged the Aioli platform for large-scale 2D/3D annotation experiments. This platform allowed us to spatially organize tens of thousands of photographs representing various temporal states of the cathedral. By integrating thematic descriptive structures and controlled vocabularies, we captured interdisciplinary observations, creating a structured dataset that now includes nearly 14,000 annotations spanning over 400 collaborative projects.
The ERC n-Dame_Heritage project is achieving significant advancements that elevate heritage research beyond the current state of the art. By integrating semantically enriched, multi-scale, and multi-temporal digitization with knowledge formalization, we are constructing a unified “galaxy of concepts” that interconnects vast, heterogeneous datasets, enabling unprecedented multidimensional correlations. This innovative framework not only synthesizes and structures complex relationships between material and knowledge objects but also fosters deeper interdisciplinary insights.
A key milestone is the creation of a rich, interconnected data corpus centered on Notre-Dame de Paris. This corpus, unparalleled in size, diversity, and depth of description, provides a unique analytical field for studying the convergence of disciplines around shared objects of inquiry. Beginning in 2025, as embargoes are lifted, this data will progressively become accessible through narrative-driven trajectories and tools for interactive visualization and web-based querying, reinforcing its usability for researchers and the public.
The project also tackles critical challenges, such as bridging the semantic gap by transforming raw data into enriched insights through the integration of semantic layers. This approach ensures that data remains interpretable and usable across diverse fields while preserving the memory of research decisions, protocols, and expertise. Additionally, we are developing a comprehensive graph database that integrates spatial, temporal, and semantic dimensions, enabling the creation of new interfaces for advanced exploration and analysis. This innovation establishes seamless connections between physical and conceptual data.
Leveraging artificial intelligence, the project is pioneering automated methods for image segmentation and shape recognition. These tools significantly accelerate data annotation and cross-fertilization, setting new benchmarks for the analysis and documentation of heritage data. Complementing these technological innovations, we have developed a collaborative thesaurus comprising over 13,000 concepts across nine domains. This shared semantic framework enhances interdisciplinary collaboration by harmonizing terminologies and facilitating integrated knowledge sharing.
A key milestone is the creation of a rich, interconnected data corpus centered on Notre-Dame de Paris. This corpus, unparalleled in size, diversity, and depth of description, provides a unique analytical field for studying the convergence of disciplines around shared objects of inquiry. Beginning in 2025, as embargoes are lifted, this data will progressively become accessible through narrative-driven trajectories and tools for interactive visualization and web-based querying, reinforcing its usability for researchers and the public.
The project also tackles critical challenges, such as bridging the semantic gap by transforming raw data into enriched insights through the integration of semantic layers. This approach ensures that data remains interpretable and usable across diverse fields while preserving the memory of research decisions, protocols, and expertise. Additionally, we are developing a comprehensive graph database that integrates spatial, temporal, and semantic dimensions, enabling the creation of new interfaces for advanced exploration and analysis. This innovation establishes seamless connections between physical and conceptual data.
Leveraging artificial intelligence, the project is pioneering automated methods for image segmentation and shape recognition. These tools significantly accelerate data annotation and cross-fertilization, setting new benchmarks for the analysis and documentation of heritage data. Complementing these technological innovations, we have developed a collaborative thesaurus comprising over 13,000 concepts across nine domains. This shared semantic framework enhances interdisciplinary collaboration by harmonizing terminologies and facilitating integrated knowledge sharing.