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Extract 1 - Crocodile Hole.mp4 (4.23 MB)
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Extract 2 - Gunpoint.mp4 (4.24 MB)
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Extract 3 - Erradram.mp4 (13.71 MB)
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Extract 4 - Lara.mp4 (21.53 MB)
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Using a geospatial approach to document and analyse locational points in face-to-face conversation / Supplementary video files

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posted on 2021-12-24, 01:30 authored by Francesco PossematoFrancesco Possemato, Joe BlytheJoe Blythe, Caroline de Dear, Josua DahmenJosua Dahmen, Rod Gardner, LESLEY STIRLINGLESLEY STIRLING

The four video clips in this dataset supplement the peer-reviewed paper Possemato et al. (2021). Using a geospatial approach to document and analyse locational points in face-to-face conversation, which is published in the open-access journal Language Documentation and Description. Please refer to the article for transcriptions and contextual information regarding the extracts.

The methodology paper presents a geospatial framework for the documentation and analysis of locational points in casual interaction. It demonstrates how GPS and GIS metadata can be used in conjunction with satellite imagery to accurately determine the direction of locational pointing gestures in video-recorded interactions. The illustrative extracts involve research participants from remote regions in Australia. Ethics approval has been obtained by Macquarie University and participants have given fully informed consent.

The supplementary files in this dataset have been downsized and converted to MP4 format. The larger corpora for the Aboriginal Australian languages will be archived at the Australian Institute of Aboriginal and Torres Strait Islander Studies (AIATSIS). The repository for the corpus of conversation in English spoken in remote and rural Australia is yet to be determined.

Funding

Australian Aboriginal conversational style

Australian Research Council

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History

Research Project ID

81341763

Q/A Log

  • Peer review completed
  • Institutional review completed
  • FAIR assessment completed

FAIR Self Assessment Summary

This text has been generated from a tool that has been adapted from the ARDC FAIR Assessment Tool Findable -------- Does the dataset have any identifiers assigned? Global Is the dataset identifier included in all metadata records/files describing the data? Yes How is the data described with metadata? Comprehensively (see suggestion) using a recognised formal machine-readable metadata schema What type of repository or registry is the metadata record in? Data is in one place but discoverable through several registries Accessible ---------- How accessible is the data? Publicly accessible Is the data available online without requiring specialised protocols or tools once access has been approved? Standard web service API (e.g. OGC) Will the metadata record be available even if the data is no longer available? Yes Interoperable ------------- What (file) format(s) is the data available in? In a structured, open standard, machine-readable format What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements? Standardised vocabularies/ontologies/schema without global identifiers How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)? The metadata record includes URI links to related metadata, data and definitions Reusable -------- Which of the following best describes the license/usage rights attached to the data? Standard machine-readable license (e.g. Creative Commons) How much provenance information has been captured to facilitate data reuse? Fully recorded in a machine-readable format

FAIR Self Assessment Rating

  • 5 Stars

Data Sensitivity

  • General

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