The National Library of Scotland’s Digital Scholarship Service has been releasing collections as data on its data-delivery platform, the Data Foundry, since September 2019. Following the COVID-19 lockdown, this service experienced significantly higher traffic, as library users increasingly made use of online resources. To ensure that as many users as possible were able to explore the datasets on the Data Foundry, the Library invested in a Digital Research Intern post, with a remit to provide introductory analysis of the Data Foundry collections using Jupyter Notebooks. This article provides a case study of this project, explaining the Library’s work to date around its new Digital Scholarship Service and releasing datasets on the Data Foundry; the reasoning behind the decision to begin to provide Jupyter Notebooks; the Notebooks themselves and what types of analysis they contain, as well as the challenges faced in creating them; and the publication and impact of the Notebooks.
Mitigating harms from gender biased language in Natural Language Processing (NLP) systems remains a challenge, and the situated nature of language means bias is inescapable in NLP data. Though efforts to mitigate gender bias in NLP are numerous, they often vaguely define gender and bias, only consider two genders, and do not incorporate uncertainty into models. To address these limitations, in this paper we present a taxonomy of gender biased language and apply it to create annotated datasets. We created the taxonomy and annotated data with the aim of making gender bias in language transparent. If biases are communicated clearly, varieties of biased language can be better identified and measured. Our taxonomy contains eleven types of gender biases inclusive of people whose gender expressions do not fit into the binary conceptions of woman and man, and whose gender differs from that they were assigned at birth, while also allowing annotators to document unknown gender information. The taxonomy and annotated data will, in future work, underpin analysis and more equitable language model development.
Peace is not only a universal concern, 1 but also a complex process of negotiations between select 1 www.un.org/development/desa/disabilities/ envision2030-goal16.html groups (i.e. policy makers, mediators, scholars and civil society groups) [4]. In this paper we present PaxVis, a platform of three interactive data visualizations for a large database of peace agreements (PA-X), developed to support comparative analysis of peace processes and improve understanding of the dynamics behind the establishment of peace.
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