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Dataset
OCR and crowdsourced annotations, Language of Mechanisation, JSON files
Datasets created through crowdsourcing tasks created on the Zooniverse crowdsourcing platform by the Living with Machines ‘language of mechanisation’ project team. Building on earlier work classifying machines by function, we asked volunteers on Zooniverse 'how did the word x change over time and place?' and presented them with options for... -
Dataset
Language of Mechanisation: annotated historical newspaper articles
Datasets created through crowdsourcing tasks created on the Zooniverse crowdsourcing platform by the Living with Machines ‘language of mechanisation’ project team. Building on earlier work classifying machines by function, we asked volunteers on Zooniverse 'how did the word x change over time and place?' and presented them with options for... -
Dataset
Diachronic word embeddings from 19th-century newspapers digitised by the British Library (1800-1919)
Word vectors related to the paper "Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers" by Nilo Pedrazzini and Barbara McGillivray (2022). The embeddings were trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and specific parameters. The embeddings are divided into...Pedrazzini, Nilo ; McGillivray, Barbara
historical semantics, word-vectors, late-modern-english, newspapers, diachronic-embeddings, and word2vec
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Dataset
Decade-level Word2Vec models from automatically transcribed 19th-century newspapers digitised by the British Library (1800-1919)
Word embeddings trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and specific parameters. The embeddings are divided into periods of ten years each. Unlike those in this repository, these were not aligned and OCR errors skimmed from the vocabulary. See related GitHub repository for the full documentation:...Pedrazzini, Nilo
historical semantics, British newspapers, word embeddings, word vectors, word2vec, and Late Modern English
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Dataset
Diachronic and diatopic word embeddings from newspapers digitised by the British Library (1830-1889): North and South England
Diachronic word embeddings (decade-level) trained with Word2Vec (via Gensim) on different geographic subcorpora of the Heritage Made Digital British and the Living with Machines historical newspaper collections: - North England (north.zip) - South England (south.zip) At the moment, for each subcorpus, Word2Vec models are available for each decade in the...Pedrazzini, Nilo ; McGillivray, Barbara
historical semantics, diachronic embeddings, late modern English, word embeddings, word vectors, word2vec, and diatopic embeddings
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Dataset
Living with Machines Zooniverse Participant Survey
Summary results from a survey of contributors to Living with Machines Zooniverse crowdsourcing projects. Responses were received between 24 May and 13 June 2022. We designed the survey so that we could align our reporting with two other audience / participant research groups. Firstly, we used the demographic categories that...British Library
online volunteering, digital participation, citizen science, citizen history, questionnaire, crowdsourcing, survey, and audience research
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Dataset
The Newspaper Press Directory (1846-1920) - enriched and structured version
Mitchell's Newspaper Press Directories contained an almost complete list of newspapers published in England, Wales, Scotland and Ireland. It was published regularly from 1846 onwards and provided a detailed description of the newspaper landscape over time. This version contains a structured, tabular representation of the directories (as CSV or Excel...C. Mitchell and Co. ; British Library
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Dataset
Stockton Herald, South Durham and Cleveland Advertiser
Stockton Herald, South Durham and Cleveland Advertiser. (1858 - 1918) was a weekly newspaper which has been digitised by the British Library for the Living with Machines projectBritish Library
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Dataset
Darlington & Richmond Herald
Darlington & Richmond Herald was a weekly newspaper which has been digitised by the British Library for the Living with Machines projectBritish Library
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