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This report discusses the scholarly communication issues in Social Sciences and Humanities that are relevant to the future development and functioning of OPERAS. The outcomes collected here can be divided into two groups of innovations regarding 1) the operation of OPERAS, and 2) its activities. The “operational” issues include the ways in which an innovative research infrastructure should be governed (Chapter 1) as well as the business models for open access publications in Social Sciences and Humanities (Chapter 2). The other group of issues is dedicated to strategic areas where OPERAS and its services may play an instrumental role in providing, enabling, or unlocking innovation: FAIR data (Chapter 3), bibliodiversity and multilingualism in scholarly communication (Chapter 4), the future of scholarly writing (Chapter 5), and quality assessment (Chapter 6). Each chapter provides an overview of the main findings and challenges with emphasis on recommendations for OPERAS and other stakeholders like e-infrastructures, publishers, SSH researchers, research performing organisations, policy makers, and funders. Links to data and further publications stemming from work concerning particular tasks are located at the end of each chapter.

How can researchers identify suitable research data repositories for the deposit of their research data? Which repository matches best the technical and legal requirements of a specific research project? For this end and with a humanities perspective the Data Deposit Recommendation Service (DDRS) has been developed as a prototype. It not only serves as a functional service for selecting humanities research data repositories but it is particularly a technical demonstrator illustrating the potential of re-using an already existing infrastructure - in this case re3data - and the feasibility to set up this kind of service for other research disciplines. The documentation and the code of this project can be found in the DARIAH GitHub repository: ​https://dariah-eric.github.io/ddrs/​.

Dictionaries could be considered as the most comprehensive reservoir of human knowledge, which carry not only the lexical description of words in one or more languages, but also the common awareness of a certain communityabout every known piece of knowledge in a time frame. Print dictionaries are the principle resources which enable the documentation and transfer of such knowledge. They already exist in abundant numbers, while new onesare continuously compiled, even with the recent strong move to digital resources.However, a majority of these dictionaries, even when available digitally, is still not fully structured due to the absence of scalable methods and techniques that can cover the variety of corresponding material. Moreover, the relatively few existing structured resources present limited exchange and query alternatives, given the discrepancy of their data models and formats.In this thesis we address the task of parsing lexical information in print dictionaries through the design of computer models that enable their automatic structuring. Solving this task goes hand in hand with finding a standardised output for these models to guarantee a maximum interoperability among resources and usability for downstream tasks.First, we present different classifications of the dictionaric resources to delimit the category of print dictionaries we aim to process. Second, we introduce the parsing task by providing an overview of the processing challengesand a study of the state of the art. Then, we present a novel approach based on a top-down parsing of the lexical information. We also outline the archiecture of the resulting system, called GROBID-Dictionaries, and the methodology we followed to close the gap between the conception of the system and its applicability to real-world scenarios.After that, we draw the landscape of the leading standards for structured lexical resources. In addition, we provide an analysis of two ongoing initiatives, TEI-Lex-0 and LMF, that aim at the unification of modelling the lexical information in print and electronic dictionaries. Based on that, we present a serialisation format that is inline with the schemes of the two standardisation initiatives and fits the approach implemented in our parsing system.After presenting the parsing and standardised serialisation facets of our lexical models, we provide an empirical study of their performance and behaviour. The investigation is based on a specific machine learning setup andseries of experiments carried out with a selected pool of varied dictionaries.We try in this study to present different ways for feature engineering and exhibit the strength and the limits of the best resulting models. We also dedicate two series of experiments for exploring the scalability of our models with regard to the processed documents and the employed machine learning technique.Finally, we sum up this thesis by presenting the major conclusions and opening new perspectives for extending our investigations in a number of research directions for parsing entry-based documents.; Les dictionnaires peuvent être considérés comme le réservoir le plus compréhensible de connaissances humaines, qui contiennent non seulement la description lexicale des mots dans une ou plusieurs langues, mais aussi la conscience commune d’une certaine communauté sur chaque élément de connaissance connu dans une période de temps donnée. Les dictionnaires imprimés sont les principales ressources qui permettent la documentation et le transfert de ces connaissances. Ils existent déjà en grand nombre, et de nouveaux dictionnaires sont continuellement compilés.Cependant, la majorité de ces dictionnaires dans leur version numérique n’est toujours pas structurée en raison de l’absence de méthodes et de techniques évolutives pouvant couvrir le nombre du matériel croissant et sa variété. En outre, les ressources structurées existantes, relativement peu nombreuses, présentent des alternatives d’échange et de recherche limitées, en raison d’un sérieux manque de synchronisation entre leurs schémas de structure.Dans cette thèse, nous abordons la tâche d’analyse des informations lexicales dans les dictionnaires imprimés en construisant des modèles qui permettent leur structuration automatique. La résolution de cette tâche va depair avec la recherche d’une sortie standardisée de ces modèles afin de garantir une interopérabilité maximale entre les ressources et une facilité d’utilisation pour les tâches en aval.Nous commençons par présenter différentes classifications des ressources dictionnaires pour délimiter les catégories des dictionnaires imprimés sur lesquelles ce travail se focalise. Ensuite, nous définissions la tâche d’analyse en fournissant un aperçu des défis de traitement et une étude de l’état de l’art.Nous présentons par la suite une nouvelle approche basée sur une analyse en cascade de l’information lexicale. Nous décrivons également l’architecture du système résultant, appelé GROBID-Dictionaries, et la méthodologie quenous avons suivie pour rapprocher la conception du système de son applicabilité aux scénarios du monde réel.Ensuite, nous prestons des normes clés pour les ressources lexicales structurées. En outre, nous fournissons une analyse de deux initiatives en cours, TEI-Lex-0 et LMF, qui visent à unifier la modélisation de l’information lexicale dans les dictionnaires imprimés et électroniques. Sur cette base, nous présentons un format de sérialisation conforme aux schémas des deux initiatives de normalisation et qui est assorti à l’approche développée dans notresystème d’analyse lexicale.Après avoir présenté les facettes d’analyse et de sérialisation normalisées de nos modèles lexicaux, nous fournissons une étude empirique de leurs performances et de leurs comportements. L’étude est basée sur une configuration spécifique d’apprentissage automatique et sur une série d’expériences menées avec un ensemble sélectionné de dictionnaires variés. Dans cette étude, nous essayons de présenter différentes manières d’ingénierie des caractéristiques et de montrer les points forts et les limites des meilleurs modèles résultants. Nous consacrons également deux séries d’expériences pour explorer l’extensibilité de nos modèles en ce qui concerne les documents traités et la technique d’apprentissage automatique employée.Enfin, nous clôturons cette thèse en présentant les principales conclusions et en ouvrant de nouvelles perspectives pour l’extension de nos investigations dans un certain nombre de directions de recherche pour l’analyse des documents structurés en un ensemble d’entrées.

The study and preservation of tangible cultural and natural heritage is a global challenge for science and society at large. The European Research Infrastructure for Heritage Science (E-RIHS) will play a leading role in research on the interpretation, preservation, documentation and management of heritage. As an interdisciplinary infrastructure, E-RIHS will interconnect knowledge and methodologies to address key scientific questions in the field of heritage as a whole. The infrastructure is built on ten core pillars. It will provide a structured and unified input of large-scale instruments, portable devices, physical and digital archives. Its implementation will focus on scientific excellence, interdisciplinarity and cooperation. In doing so, it will offer unprecedented research opportunities to a wide range of interdisciplinary scientific communities.

The digital humanities (DH) enrich the traditional fields of the humanities with new practices, approaches and methods. Since the turn of the millennium, the necessary skills to realise these new possibilities have been taught in summer schools, workshops and other alternative formats. In the meantime, a growing number of Bachelor's and Master's programmes in digital humanities have been launched worldwide. The DH Course Registry, which is the focus of this article, was created to provide an overview of the growing range of courses on offer worldwide. Its mission is to gather the rich offerings of different courses and to provide an up-to-date picture of the teaching and training opportunities in the field of DH. The article provides a general introduction to this emerging area of research and introduces the two European infrastructures CLARIN and DARIAH, which jointly operate the DH Course Registry. A short history of the Registry is accompanied by a description of the data model and the data curation workflow. Current data, available through the API of the Registry, is evaluated to quantitatively map the international landscape of DH teaching.Preprint of a publication for LibraryTribune (China) (accepted)

There is a growing need to establish domain-or discipline-specific approaches to research data sharing workflows. A defining feature of data and data workflows in the arts and humanities domain is their dependence on cultural heritage sources hosted and curated in museums, libraries, galleries and archives. A major difficulty when scholars interact with heritage data is that the nature of the cooperation between researchers and Cultural Heritage Institutions (henceforth CHIs) is often constrained by structural and legal challenges but even more by uncertainties as to the expectations of both parties. The Heritage Data Reuse Charter aims to address these by designing a common environment that will enable all the relevant actors to work together to connect and improve access to heritage data and make transactions related to the scholarly use of cultural heritage data more visible and transparent. As a first step, a wide range of stakeholders on the Cultural Heritage and research sector agreed upon a set of generic principles, summarized in the Mission Statement of the Charter, that can serve as a baseline governing the interactions between CHIs, researchers and data centres. This was followed by a long and thorough validation process related to these principles through surveys 1 and workshops 2. As a second step, we now put forward a questionnaire template tool that helps researchers and CHIs to translate the 6 core principles into specific research project settings. It contains questions about access to data, provenance information, preferred citation standards, hosting responsibilities etc. on the basis of which the parties can arrive at mutual reuse agreements that could serve as a starting point for a FAIR-by-construction data management, right from the project planning/application phase. The questionnaire template and the resulting mutual agreements can be flexibly applied to projects of different scale and in platform-independent ways. Institutions can embed them into their own exchange protocols while researchers can add them to their Data Management Plans. As such, they can show evidence for responsible and fair conduct of cultural heritage data, and fair (but also FAIR) research data management practices that are based on partnership with the holding institution.

The study and preservation of tangible cultural and natural heritage is a global challenge for science and society at large. The European Research Infrastructure for Heritage Science (E-RIHS) will play a leading role in research on the interpretation, preservation, documentation and management of heritage. As an interdisciplinary infrastructure, E-RIHS will interconnect knowledge and methodologies to address key scientific questions in the field of heritage as a whole. The infrastructure is built on ten core pillars. It will provide a structured and unified input of large-scale instruments, portable devices, physical and digital archives. Its implementation will focus on scientific excellence, interdisciplinarity and cooperation. In doing so, it will offer unprecedented research opportunities to a wide range of interdisciplinary scientific communities.

There is a growing need to establish domain-or discipline-specific approaches to research data sharing workflows. A defining feature of data and data workflows in the arts and humanities domain is their dependence on cultural heritage sources hosted and curated in museums, libraries, galleries and archives. A major difficulty when scholars interact with heritage data is that the nature of the cooperation between researchers and Cultural Heritage Institutions (henceforth CHIs) is often constrained by structural and legal challenges but even more by uncertainties as to the expectations of both parties. The Heritage Data Reuse Charter aims to address these by designing a common environment that will enable all the relevant actors to work together to connect and improve access to heritage data and make transactions related to the scholarly use of cultural heritage data more visible and transparent. As a first step, a wide range of stakeholders on the Cultural Heritage and research sector agreed upon a set of generic principles, summarized in the Mission Statement of the Charter, that can serve as a baseline governing the interactions between CHIs, researchers and data centres. This was followed by a long and thorough validation process related to these principles through surveys 1 and workshops 2. As a second step, we now put forward a questionnaire template tool that helps researchers and CHIs to translate the 6 core principles into specific research project settings. It contains questions about access to data, provenance information, preferred citation standards, hosting responsibilities etc. on the basis of which the parties can arrive at mutual reuse agreements that could serve as a starting point for a FAIR-by-construction data management, right from the project planning/application phase. The questionnaire template and the resulting mutual agreements can be flexibly applied to projects of different scale and in platform-independent ways. Institutions can embed them into their own exchange protocols while researchers can add them to their Data Management Plans. As such, they can show evidence for responsible and fair conduct of cultural heritage data, and fair (but also FAIR) research data management practices that are based on partnership with the holding institution.

The technicality of network visualization applied to history and its relative novelty often result in a superficial use of a software, limited to describing a situation immediately extracted from a data set. This approach is justified in the exploratory phase of an analysis in most cases where the network is very explicitly present in the object studied. But the complexity of the entanglement of historical actors, places, institutions or temporal sequences makes finer modeling necessary if we want to go beyond a simplistic "datafication". To encourage curiosity towards other modes of analysis and put the data modeling (and therefore the historical sources) at the center of the research process, this article proposes a short introduction on how to discuss what makes a specific historical network, its components, its relationships, its layers and its different facets. It offers a kind of visual guide to help historians follow a multilayer framework to think their research object from another (multidimensional) angle and to combine them.