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This dataset contains a CSV file and an RDF Turtle file. Both files contain information on a few people mentioned in the Irish Exchequer Payments 1270-1326, a book written by Connolly, P and published by the Irish Manuscripts Commission in 1998. A historian transcribed those people in a CSV file, subsequently transformed into RDF using an R2RML mapping. This dataset contains the records and the output of a handful of people transcribed in this way. This dataset illustrates how the Beyond 2022 project avails of CIDOC-CRM to populate its knowledge graph. Beyond 2022 is funded by the Government of Ireland, through the Department of Culture, Heritage and the Gaeltacht, under the Project Ireland 2040 framework. The project is also partially supported by the ADAPT Centre for Digital Content Technology under the SFI Research Centres Programme (Grant 13/RC/2106).

People routinely hear and understand speech at rates of 120–200 words per minute [1, 2]. Thus, speech comprehension must involve rapid, online neural mechanisms that process words’ meanings in an approximately time-locked fashion. However, in the context of continuous speech, electrophysiological evidence for such time-locked processing has been lacking. Whilst valuable insights into the semantic processing of speech have been provided by the “N400 component” of the event-related potential [3-6], this literature has been dominated by paradigms using incongruous words within specially constructed sentences, and may not accurately reflect natural, narrative speech comprehension. Building on the discovery that cortical activity “tracks” the dynamics of running speech [7-9], and psycholinguistic work both demonstrating [10-12] and modeling [13-15] how context rapidly impacts on word processing, we describe a new approach for deriving an electrophysiological correlate of natural speech comprehension. We used a computational model [16] to quantify the meaning carried by each word based on how semantically dissimilar it was to its preceding context and then regressed this quantity against electroencephalographic (EEG) data recorded from subjects as they listened to narrative speech. This produced a prominent negativity at a time-lag of 200–600 ms on centro-parietal EEG channels, characteristics common to the N400. Applying this approach to EEG datasets involving time-reversed speech, cocktail party attention and audiovisual speech-in-noise demonstrated that this response was very sensitive to whether or not subjects understood the speech they heard. These findings demonstrate that, when successfully comprehending natural speech, the human brain responds to the contextual semantic content of each word in a relatively time-locked fashion. Cocktail Party DatasetCocktail Party.zipN400 DatasetN400.zipNatural Speech - Reverse DatasetNatural Speech - Reverse.zipNatural Speech DatasetNatural Speech.zipSpeech in Noise DatasetSpeech in Noise.zip