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The authors also acknowledge the support and collaboration of many other colleagues in their respective institutes, research communities and IT Infrastructures, together with the funding received by these from many different sources. These include but are not limited to the following: (i) The Worldwide LHC Computing Grid (WLCG) project is a global collaboration of more than 170 computing centres in 43 countries, linking up national and international grid infrastructures. Funding is acknowledged from many national funding bodies and we acknowledge the support of several operational infrastructures including EGI, OSG and NDGF/NeIC. (ii) EGI acknowledges the funding and support received from the European Commission and the many National Grid Initiatives and other members. EOSC-hub receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777536. (iii) The work leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 730941 (AARC2). (iv) Work on the development of ESGF's identity management system has been supported by The UK Natural Environment Research Council and funding from the European Union's Seventh Framework Programme for research, technological development and demonstration through projects IS-ENES (grant agreement no 228203) and IS-ENES2 (grant agreement no 312979). (v) Ludek Matyska and Michal Prochazka acknowledge funding from the RI ELIXIR CZ project funded by MEYS Czech Republic No. LM2015047. (vi) Scott Koranda acknowledges support provided by the United States National Science Foundation under Grant No. PHY-1700765. (vii) GÉANT Association on behalf of the GN4 Phase 2 project (GN4-2).The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 731122(GN4-2). (viii) ELIXIR acknowledges support from Research Infrastructure programme of Horizon 2020 grant No 676559 EXCELERATE. (ix) CORBEL life science cluster acknowledges support from Horizon 2020 research and innovation programme under grant agreement No 654248. (x) Mirjam van Daalen acknowledges that the research leading to this result has been supported by the project CALIPSOplus under the Grant Agreement 730872 from the EU Framework Programme for Research and Innovation HORIZON 2020. (xi) EISCAT is an international association supported by research organisations in China (CRIRP), Finland (SA), Japan (NIPR), Norway (NFR), Sweden (VR), and the United Kingdom (NERC). This white-paper expresses common requirements of Research Communities seeking to leverage Identity Federation for Authentication and Authorisation. Recommendations are made to Stakeholders to guide the future evolution of Federated Identity Management in a direction that better satisfies research use cases. The authors represent research communities, Research Services, Infrastructures, Identity Federations and Interfederations, with a joint motivation to ease collaboration for distributed researchers. The content has been edited collaboratively by the Federated Identity Management for Research (FIM4R) Community, with input sought at conferences and meetings in Europe, Asia and North America.

International audience; Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity.

In this article, we propose a Category Theory approach to (syntactic) interoperability between linguistic tools. The resulting category consists of textual documents, including any linguistic annotations, NLP tools that analyze texts and add additional linguistic information, and format converters. Format converters are necessary to make the tools both able to read and to produce different output formats, which is the key to interoperability. The idea behind this document is the parallelism between the concepts of composition and associativity in Category Theory with the NLP pipelines. We show how pipelines of linguistic tools can be modeled into the conceptual framework of Category Theory and we successfully apply this method to two real-life examples. Paper submitted to Applied Category Theory 2020 and accepted for Virtual Poster Session

AbstractThe paper presents Intergraph, a graph-based visual analytics technical demonstrator for the exploration and study of content in historical document collections. The designed prototype is motivated by a practical use case on a corpus of circa 15.000 digitized resources about European integration since 1945. The corpus allowed generating a dynamic multilayer network which represents different kinds of named entities appearing and co-appearing in the collections. To our knowledge, Intergraph is one of the first interactive tools to visualize dynamic multilayer graphs for collections of digitized historical sources. Graph visualization and interaction methods have been designed based on user requirements for content exploration by non-technical users without a strong background in network science, and to compensate for common flaws with the annotation of named entities. Users work with self-selected subsets of the overall data by interacting with a scene of small graphs which can be added, altered and compared. This allows an interest-driven navigation in the corpus and the discovery of the interconnections of its entities across time.

International audience; Le web sémantique et l'ouverture des données (publications, archives, référentiels) dans les sciences humaines et sociales (SHS) ont permis la création outils de recherche nouveaux permettant à la fois de créer des portails documentaires (autour de moteurs de recherche sémantiques) et des applications pouvant être embarquées dans des sites web. ISIDORE, l'accès unifié aux données, publications et informations des SHS entre dans cette catégorie. Lancé en 2010 par le CNRS et actuellement développé par Huma-Num, ISIDORE propose, outre un moteur de recherche sur le web, une collection d'applications permettant de l'utiliser de façon « embarqué » pour des usages aux plus près des besoins des enseignants-chercheurs et des étudiants.

This article describes how virtual research environments (VREs) offer new opportunities for researchers to analyse open data and to obtain new insights for policy making. Although various VRE-related initiatives are under development, there is a lack of insight into how VREs support collaborative open data analysis by researchers and how this might be improved, ultimately leading to input for policy making to solve societal issues. This article clarifies in which ways VREs support researchers in open data analysis. Seven cases presenting different modes of researcher support for open data analysis were investigated and compared. Four types of support were identified: 1) ‘Figure it out yourself', 2) ‘Leading users by the hand', 3) ‘Training to provide the basics' and 4) ‘Learning from peers'. The author provides recommendations to improve the support of researchers' open data analysis and to subsequently obtain new insights for policy making to solve societal challenges.

Research in theoretical and computer-based archaeology, from the 1950s onwards, established important perspectives for the formal representation and analysis of tangible cultural entities such as complex artefacts, iconographic compositions and archaeological assemblages, and became a precursor for the emergence of knowledge-based tools, methodologies and standards for artefact-centred information systems in contemporary museums. One particular case in point is CLIO, a semantic information system intended for research use, developed by ICS/FORTH and the Benaki Museum in Greece in the early 1990s, which became a foundation for the definition of the Conceptual Reference Model of the International Documentation Committee of ICOM (CIDOC CRM), recently adopted as the ISO standard for cultural information representation. It is argued here that, as the capabilities of computer applications to provide access to complex, multimedia cultural information increase, so does also the validity and importance of earlier research advances in artefact-centred archaeological computing; and, conversely, that the advent of digital infrastructures for material culture disciplines such as archaeology highlights the pertinence, and potential benefits, of further work on archaeological formal analysis and knowledge representation.

International audience; Academic dictionary writing is making greater and greater use of the TEI Guidelines’ dictionary module. And as increasing numbers of TEI dictionaries become available, there is an ever more palpable need to work towards greater interoperability among dictionary writing systems and other language resources that are needed by dictionaries and dictionary tools. In particular this holds true for the crucial role that statistical data obtained from language resources play in lexicographic workflow—a role that also has to be reflected in the model of the data produced in these workflows. Presenting a range of current projects, the authors address two main questions in this area: How can the relationship between a dictionary and other language resources be conceptualized, irrespective of whether they are used in the production of the dictionary or to enrich existing lexicographic data? And how can this be documented using the TEI Guidelines? Discussing a variety of options, this paper proposes a customization of the TEI dictionary module that tries to respond to the emerging requirements in an environment of increasingly intertwined language resources.