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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 paper analyses the Concept Development and Experimentation (CD&E) approach which has been developed and implemented in NATO over the last 20 years. NATO’s CD&E approach is explained as an organisational innovation and institutional response to external and internal pressures. Within the theoretical framing of institutional isomorphism, the paper analyses the adoption of CD&E in the European Union’s (EU) Common Security and Defence Policy (CSDP). The research hypothesis is that the EU will adopt and apply NATO’s CD&E approach in the EU’s defence planning and capability development process. The empirical findings from the analysis of the EU policy practice, however, show that CD&E has actually been adopted from NATO and applied by the EU to a very limited extent. The low degree of isomorphism between NATO and the EU with regard to applying CD&E is explained by a complex set of factors. The research results have broader implications, suggesting that under the current institutional settings it is highly unlikely CD&E to be adopted by other international organisations in the field of international security.

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema$.$org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://www.researchobject.org/2021-packaging-research-artefacts-with-ro-crate/ Comment: 42 pages. Submitted to Data Science

Au long du xx e siècle les mondes du travail et la condition ouvrière sont travaillés par des lignes de faille nombreuses et croisées, approfondies par l’ampleur des restructurations industrielles et des saignées dans les effectifs ouvriers depuis les années 1970 et 1980. Cet article, consacré à l’exploration des rapports entre inégalités de carrières, mobilités professionnelles et récession économique, propose d’étudier la déstabilisation des mondes ouvriers au prisme des carrières ouvrières. Une démarche prosopographique est mise en œuvre afin d’observer la transformation des carrières d’ouvriers mineurs – français et marocains essentiellement – des Houillères du Bassin du Nord-Pas-de-Calais, de leur création en 1946 à leur fermeture définitive en décembre 1990, au fil de la politique de récession charbonnière.

A transnational flow of capital exchange during the 19th and early 20th centuries brought planning ideas and modernity into China. Since European countries and America used violence to place China ...

Published in OA on RESSI (http://www.ressi.ch/) on the 15.02.22. 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. International audience

Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process. These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network. We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs. Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that underlines the non-human-like nature of the knowledge learned by the network.