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All Outputs (7)

Translated transcription of a focus group with participative councillors, for information literacy research in Brazil (2023)
Dataset
Ryan, B., & Cruickshank, P. (2023). Translated transcription of a focus group with participative councillors, for information literacy research in Brazil. [Dataset]. https://doi.org/10.17869/enu.2023.3407211

This focus group took place on 24 January 2019, in São Paulo city hall. It was held in English and Portuguese with eight participative councillors, with in situ interpretation. The outcome of the research was a paper presenting an evaluation of the i... Read More about Translated transcription of a focus group with participative councillors, for information literacy research in Brazil.

'ChatGPT & Me' Student Padlet Data With Reactions (2023)
Dataset
Drumm, L., Illingworth, S., Graham, C., Calabrese, P., Taylor, S., Dencer-Brown, I., & van Knippenberg, I. (2023). 'ChatGPT & Me' Student Padlet Data With Reactions. [Dataset]. https://doi.org/10.17869/enu.2023.3200728

These data are from a project which aimed to collect and analyse student attitudes to artificial intelligence (AI) and their own learning and assessment while they are studying with Edinburgh Napier University. AI tools such as ChatGPT make it easier... Read More about 'ChatGPT & Me' Student Padlet Data With Reactions.

Rawls in the mangrove: perceptions of justice in nature-based solutions projects (Dataset) (2023)
Dataset
Huxham, M. (2023). Rawls in the mangrove: perceptions of justice in nature-based solutions projects (Dataset). [Dataset]. https://doi.org/10.17869/ENU.2023.3090209

1. Adapting to and mitigating against climate change requires the protection and expansion of natural carbon sinks, especially ecosystems with exceptional carbon density such as mangrove forests (an example of ‘blue carbon’). Projects that do this ar... Read More about Rawls in the mangrove: perceptions of justice in nature-based solutions projects (Dataset).

Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing (2023)
Dataset
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing. [Dataset]. https://doi.org/10.17869/enu.2023.3100035

This data contains the worker position datasets (including the event logs) and the source codes of the discrete event simulation that are used in the research article titled "Using Worker Position Data for Human-Driven Decision Support in Labour-inte... Read More about Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing.

Where to fish in the forest? Tree characteristics and contiguous seagrass features predict mangrove forest quality for fishes and crustaceans (Dataset) (2023)
Dataset
Wanjiru, C., Nagelkerken, I., Rueckert, S., Harcourt, W., & Huxham, M. (2023). Where to fish in the forest? Tree characteristics and contiguous seagrass features predict mangrove forest quality for fishes and crustaceans (Dataset). [Dataset]. https://doi.org/10.17869/enu.2023.3069847

1) Mangroves often support rich fish and crustacean communities, although faunal abundance and diversity show strong spatio-temporal variability. Consistent patterns in mangrove animal communities might be dictated by forest characteristics, by seasc... Read More about Where to fish in the forest? Tree characteristics and contiguous seagrass features predict mangrove forest quality for fishes and crustaceans (Dataset).

SAtour: Sentiment analysis of Saudi Arabia Tourism Tweets (2023)
Dataset
Basabain, S. (2023). SAtour: Sentiment analysis of Saudi Arabia Tourism Tweets. [Dataset]. https://doi.org/10.17869/enu.2023.3036363

SAtour is a new dataset of Arabic tweets in the tourism domain for Arabic SA. The total corpus size is 2293 tweets, after manual annotation these tweets were labeled as either positive, negative, or neutral. We present the detailed process of collect... Read More about SAtour: Sentiment analysis of Saudi Arabia Tourism Tweets.