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In view of the developments of eSciences and accompanying infrastructures in German academia, the Cluster of Excellence Africa Multiple with its Digital Solutions portfolio has set the ambitious aim of establishing a digital research repository for its disciplinary heterogenous research projects ranging from economics to climate studies to linguistics, history and media studies, the “only” common ground being the studied area “Africa” and “African diasporas”. The four thematic sections that all touch upon eSciences, or put more generally, the digital transformation of the academic world and society from very different perspectives and disciplinary diversity. Be it knowledge management as a big data business model for academic services, digital neo-colonialism, the different legal aspects, problems of bias in semantic data processing, digitalization projects in Africa or digitization projects and collections built up in Europe.

The textbook introduces works by Sappho, Christine de Pizan, Aphra Behn, Maria Edgeworth, Jane Austen, Charlotte Brontë, George Eliot, George Sand, Caroline Norton, Elizabeth Barrett Browning, Emily Dickinson, Laura Marholm, Amalie Skram, Virginia Woolf and Amy Lowell. Some of these authors are presented as the role-models, the others as writers looking for their own female tradition and finding it in the writings of their literary foremothers. These responses - in form of poems, extracts from essays and novels - are included in the textbook and enriched with comments, interpretations and tasks for students. In the introduction, the question of female literary tradition is discussed by presenting various theoretical answers from distinguished feminist scholars. Učbenik predstavlja dela Sappho, Christine de Pizan, Aphre Behn, Marije Edgeworth, Jane Austen, Charlotte Brontë, George Eliot, George Sand, Caroline Norton, Elizabeth Barrett Browning, Emily Dickinson, Laure Marholm, Amalie Skram, Virginije Woolf in Amy Lowell . Nekatere od teh avtoric so predstavljene kot vzornice, drugi kot literarne ustvarjalke, ki iščejo lastno žensko tradicijo in jo najdejo v spisih svojih literarnih prednic. Ti odzivi - v obliki pesmi, izvlečkov iz esejev in romanov - so vključeni v učbenik in obogateni s komentarji, interpretacijami in nalogami za študente in študentke V uvodu je vprašanje ženske literarne tradicije obravnavano s predstavitvijo različnih teoretičnih odgovorov uglednih feminističnih teoretičark.

International audience; Published in OA on RESSI (http://www.ressi.ch/) at the end of Octobre 2021. We present here highlights from an enquiry on the innovations in scholarly writing in the Humanities and Social Sciences in the H2020 project OPERAS-P. This article explores the theme of Open Research Data and its role in the emergence of new models of scholarly writing. We examine more closely the obstacles and fostering conditions to the publication of research data, both from a social and a technical perspective.

Il patrimonio culturale rappresenta un settore strategico per lo sviluppo della società, dell'economia e del lavoro. Una dimensione essenziale della gestione e della fruizione del patrimonio culturale è il processo della sua digitalizzazione. Accanto al patrimonio culturale esiste ormai un patrimonio culturale digitale che ne garantisce la conservazione, la diffusione e la valorizzazione. Le nuove tecno-logie hanno trasformato l'organizzazione di musei, gallerie, siti d'arte e siti archeologici. Queste stesse tecnologie hanno consentito la diffusione e l'operabilità a livello internazionale di infrastrutture digitali di informazione e ricerca. La digitalizzazione ha consentito ai luoghi della cultura di sperimentare nuovi legami, con i territori e con i cittadini, già dall'inizio degli anni 2000 e soprattutto a seguito del lockdown imposto dalla pandemia da Covid 19. Le tante attività di digitalizzazione volte a valorizzare i beni culturali richiedono sia co-noscenze umanistiche che scientifiche. Da un lato, esse implicano la creazione di realtà virtuali e modellizzazioni per una diversa e più profonda conoscenza, dall'altro lato, richiedono l'uso dell'intelligenza artificiale e dei big data per ricostruire il passato delle culture o per conoscere i flussi turistici nei siti d'arte. Anche le professioni, le competenze ed i percorsi formativi legati alla digitalizzazione dei beni culturali nascono dalle interazioni tra sistemi fisici e sistemi virtuali, da conoscenze ed esperienze di diversa natura.

Research and development are facilitated by sharing knowledge bases, and the innovation process benefits from collaborative efforts that involve the collective utilization of data. Until now, most companies and organizations have produced and collected various types of data, and stored them in data silos that still have to be integrated with one another in order to enable knowledge creation. For this to happen, both public and private actors must adopt a flexible approach to achieve the necessary transition to break data silos and create collaborative data sharing between data producers and users. In this paper, we investigate several factors influencing cooperative data usage and explore the challenges posed by the participation in cross-organizational data ecosystems by performing an interview study among stakeholders from private and public organizations in the context of the project IDE@S, which aims at fostering the cooperation in data science in the Austrian federal state of Styria. We highlight technological and organizational requirements of data infrastructure, expertise, and practises towards collaborative data usage.

This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission. This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.

The ROSSIO Infrastructure is developing a free and open-access platform for aggregating, organising, and connecting the digital resources in the Social Sciences, Arts and Humanities provided by Portuguese higher education and cultural institutions. This paper presents an overview of the ROSSIO Infrastructure, its main objectives, the institutions involved, and the services offered by the infrastructure’s aims through its platform—namely, a discovery portal, digital exhibitions, collections, and a virtual research environment. These services rely on a metadata-aggregation solution for bringing the digital objects’ metadata from the providing institutions into ROSSIO. The aggregated datasets are converted into linked data and undergo an enrichment process based on controlled vocabularies, which are developed and published by ROSSIO. The paper will describe this process, the applications involved, and how they interoperate. We will further reflect on how these services may enhance the dissemination of science, considering the FAIR principles.

AbstractThis article considers that reasoning over archives is a joint enterprise between archivists and researchers and that both groups are increasingly using machine agents to assist them in it. It starts by considering the processing of archivists, researchers and machine agents separately. Using the different perspectives this brings to highlight different aspects of that processing, as a process of sense-making, as scholarly research activity, as practices that realise and achieve data for the drawing of further inference, it reasserts the argument that archives cannot be regarded as raw data to be reasoned over, but must be seen as the result of multiple representative and interpretive acts, of iterative realisation and activation as ‘data’ potentially involving many, many additional actors. It then goes on to consider how the involvement of machine agents fits into and potentially alters this picture by providing more detail about the basis on which they currently perform such acts.