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Since the first ESFRI roadmap in 2006, multiple humanities Research Infrastructures (RIs) have been set up all over the European continent, supporting archaeologists (ARIADNE), linguists (CLARIN-ERIC), Holocaust researchers (EHRI), cultural heritage specialists (IPERION-CH) and others. These examples only scratch the surface of the breadth of research communities that have benefited from close cooperation in the European Research Area.While each field developed discipline-specific services over the years, common themes can also be distinguished. All humanities RIs address, in varying degrees, questions around research data management, the use of standards and the desired interoperability of data across disciplinary boundaries.This article sheds light on how cluster project PARTHENOS developed pooled services and shared solutions for its audience of humanities researchers, RI managers and policymakers. In a time where the convergence of existing infrastructure is becoming ever more important – with the construction of a European Open Science Cloud as an audacious, ultimate goal – we hope that our experiences inform future work and provide inspiration on how to exploit synergies in interdisciplinary, transnational, scientific cooperation. This article has been accepted for publication by EUP in the IJHAC: International Journal of Humanities and Arts Computing (https://www.euppublishing.com/loi/ijhac) International audience

This article presents a study of the French-speaking digital humanities. It is based on the experience of two research engineers from the French National Center for Scientific Research (CNRS) who have been studying these issues for the last ten years. They conducted a survey at the École Normale Supérieure (ENS-Paris) which enabled them to draw up an overview of the transformation of the profession of humanities and social sciences research engineers in the context of the digital humanities. The Digit_Hum initiative, which they run in parallel with their respective activities at the ENS, also provided information for this overview thanks to its role as a space for discussion about the digital humanities along with training and structuring of this field at the ENS and the Université Paris Sciences & Lettres (PSL). Cet article est une réflexion sur les humanités numériques en contexte francophone. Elle s’appuie sur l'expérience de deux ingénieures du Centre National de la Recherche Scientifique travaillant sur ces questions depuis une dizaine d'années. À travers l'enquête qu'elles ont menée à l'École normale supérieure (ENS-Paris), elles dressent un panorama de la transformation du métier d'ingénieur(e) en sciences humaines et sociales dans le contexte des humanités numériques. L'initiative Digit_Hum, qu'elles animent en parallèle de leurs activités respectives à l'École, nourrit également ce témoignage en constituant un espace de discussions, de formations et de structuration des humanités numériques au sein de l'ENS et de l’Université Paris Sciences & Lettres.

D 2.1: Initial plan for communication and dissemination of project results and RI access, including audience/instrument mapping and communications KPIs (M6, to be updated continuously from then on until M 48).

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.

Abstract 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/. H2020 Research Infrastructures http://dx.doi.org/10.13039/100010666 Georg-August-Universität Göttingen (1018)

My First article revealed about Fifth Force or New Force discovered by Fermi-Lab or LHC etc. other than Strong, Weak, Electromagnetic & Gravitational force published in the Journal of Calcutta Mathematical Society, Salt Lake, Kolkata, West Bengal, India who received this article on 2007 but published on 2011.

We study an important fact called Mind & Sense Datum (matter based). Considering these emerges by the photoelectric effects conducted by the photon-like energy packets “narayan” including photons or quanta of ordinary light through electromagnetic interactions of the matter nuclei weak energy forces of SU(2) in the respective framework of SU(2) ´ U(1), also interacted with bio-molecular particles constructed by the various combinations of lepton-like but quark-type [assuming six set of quark-types each set having five different quark-type] of exotic matter fluids in wave status are tightly binding by the bosons of SU(6) combining with quarks of usual matter energy in brain neuron microtubules for the case of human or any other suitable places of brain-likes of lives. We then found several new unknown Particles-Likes “bhadras” with a new kind of strong forces those are combining with quarks of ordinary matter particles formed Bio-Molecules through chemical bonding of matter atoms or polymers etc. are internally linked through photoelectric-like current for exchanging the necessary information between all parts of the body-system. Hence created a living-body-system of the whole system link through laser-like beam of new energies etc. inclinable to the photo-electrodes-like tube in brain cell for the case of Human-likes and then created holography mind with gradually unfolding sense datum. Thus human brain conceived as an interfacing organ that not only produces mind through consciousness but also received instructional information. Considering there possible variable wave frequencies packets of light constructed by photon-like [made up from neutrinos-like of the new energies SU(6), SU(12), SU(24) etc.] quanta in wave status "narayan" within our Physical Universe produce consciousness of us then by photoelectric effects created mind with sense datum.