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Among the potential intellectual outputs of the ENCODE project is the production of a MOOC that introduces teaching staff and scientific experts to the digital transition in the field of ancient writing cultures. The basis for this MOOC is the need to foster awareness of the importance of digital competences and to use a structured framework to introduce people to the available innovative teaching and learning materials and opportunities for organizing (self-)training in this field of research. For specialists in the humanities, there is often an unexpected reluctance to go beyond simply using digital tools and to deepen their understanding of the implications of the digital transitions of research fields, as well as considering the readiness of young graduates to acquire digital competences. This MOOC, which is easily accessible, affordable, sustainable, and flexible, may achieve the initial aim of the project, namely, bridging the gap between the highly specialized competences in the humanities and the innovative digital skills needed in open science practices. The main methodological issue concerns the design and adaptation of cooperative tools in order to implement a common pedagogical approach and to produce MOOC content that integrates the different competences and insights of the project participants. This report on the experiment provides useful insights into the differing expectations of academic staff as content producers, issues surrounding MOOC-cooperative design between universities in different countries, the usability of the tested platform and of the different features provided, and sustainability, as guaranteed through the connection with digital infrastructures. In the concluding section, the originality of the MOOC at a more general scale is emphasized. The ways in which the MOOC can facilitate and support the digital transition are assessed according to the FAIR principles in Higher Education Institutions. Moreover, the MOOC offers models for hands-on experiences of digital training and the evaluation of learning outcomes according to shared European frameworks; it demonstrates the importance of being connected with larger projects and digital infrastructures. ispartof: Education Sciences vol:13 issue:43 pages:1-12 status: Published online

TRAME is a research infrastructure for medieval manuscripts. The TRAME engine scans a set of sources for searched terms and retrieves links to a wide range of possible information, from simple reference, to detailed manuscript record, to full text transcriptions. Currently, it is possible to perform queries by: free-text, shelfmark, author, title, date, copyst or incipit, on more than 80 selected scholarly digital resources across EU and USA. Since 2014 September 1st, TRAME has entered a new phase and the current work is focused on: extending the meta-search approach to other web resources, leveraging the users interaction to define an ontology for medieval manuscripts, re-designing the front-end towards a new UX approach.

This study aims to improve the knowledge of polychrome Roman sarcophagi by showing the first observations about the colour palette detected and the painting techniques by combining archaeological research with scientific results. These results are the base to understand the ancient colour and to improve the virtual reconstruction through the use of 3D Computer Graphic.

Abstract‘Social and Cultural Innovation’ is a syntagma that is receiving increased usage among researchers since it was the title chosen by the European Strategy Forum Research Infrastructures for the working group that deals with research infrastructures primarily connected with Social Sciences and the Humanities. Innovation refers to the creation of new products and services by bringing a new idea to the market. Economic growth turns on infrastructures, which provide access to services and knowledge, e.g. by overcoming the digital divide. The current migrant and refugee crisis has made it clear with extraordinary effectiveness that a most urgent objective is to work out policies of social and cultural innovation to the advantage of new citizens – policies that will make them feel welcome in full dignity.

Given the dynamic rate of change in the OA books business models landscape, the OPERAS Open Access Business Models Special Interest Group launched a survey in 2021 to 1) improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs; and 2) to identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in Social Science and the Humanities. This white paper updates and expands an earlier version published in 2021, which presented the preliminary analysis of the findings. Despite a small sample of presses meaning that no strong trends ought to be discussed, several insights were drawn and should be considered important directions for the future. Key findings in the report have been grouped into three main areas: collaboration, funding, and support. The report found that, although not opposed to the idea, a majority of presses do not engage in collaboration, specifically collaborative models for shared infrastructure, mainly due to the lack of knowledge and information, or perceived lack of need. This indicates that, for OA books, we are still at the early stage of the adoption curve for collaborative shared infrastructure. In terms of funding, most publishers perceive themselves to be somewhat sustainable. For institutional publishers, parent organisations are crucial as providers of financial or non-monetary support of OA. In addition, most publishers stress the need to have more resources and rely on more than one funding source, including grants and subsidies. The report found that awareness-raising and targeted support and training could be used to engage the presses but further incentivisation may be required to encourage publishers to collaborate more widely. We believe that the insights from this white paper may be interesting to a number of projects, such as DIAMAS, OPERAS-PLUS, and Palomera and have presented areas for further research and more specific actionable points for these projects.

Obtaining high-quality labelled data for training a classifier in a new application domain is often costly. Transfer Learning (a.k.a. “Inductive Transfer”) tries to alleviate these costs by transferring, to the “target” domain of interest, knowledge available from a different “source” domain. In transfer learning the lack of labelled information from the target domain is compensated by the availability at training time of a set of unlabelled examples from the target distribution. Transductive Transfer Learning denotes the transfer learning setting in which the only set of target documents that we are interested in classifying is known and available at training time. Although this definition is indeed in line with Vapnik’s original definition of “transduction”, current terminology in the field is confused. In this article, we discuss how the term “transduction” has been misused in the transfer learning literature, and propose a clarification consistent with the original characterization of this term given by Vapnik. We go on to observe that the above terminology misuse has brought about misleading experimental comparisons, with inductive transfer learning methods that have been incorrectly compared with transductive transfer learning methods. We then, give empirical evidence that the difference in performance between the inductive version and the transductive version of a transfer learning method can indeed be statistically significant (i.e., that knowing at training time the only data one needs to classify indeed gives an advantage). Our clarification allows a reassessment of the field, and of the relative merits of the major, state-of-the-art algorithms for transfer learning in text classification.