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GGES 6018, Data Collection & Research Methods for Sustainability and Environmental Science, is a module which aims to equip students on the MSc Sustainability and MSc Environmental Science programmes with the skills necessary to plan and undertake independent research as part of their studies and later in their chosen careers. Students are introduced to different research methods (quantitative, qualitative and mixed methods), with an initial focus on core quantitative research methods. They are then given the option to either continue learning quantitative research methods or to switch to receiving complementary training in qualitative methods.

In the first part of the module, students receive instruction on the fundamentals of quantitative data analysis. They are provided with relevant examples in Sustainability and Environmental Science and are given an opportunity to practice with these and write a quantitative report which contributes to the assessment of the module. They are also introduced to R programming language, which will be used throughout the module for all quantitative analyses.

The second part of the module focusing on further quantitative methods aims to introduce the students to statistical techniques relevant to data science applications. The alternative option focusing on qualitative methods aims to provide training on key concepts used in qualitative research. Students are also given an opportunity to apply the skills acquired in this part of the module to a project leading to a research report, which will also form part of the assessment of the module.

This module will cover the purposes and use of different methods for data collection in education research. It will address the design and use of questionnaires, different types of interviews and classroom observations. At the end of the module, students will have developed their skill in designing data collection instruments in connection to each of the three methods under focus and their critical understanding of the affordances and limitations of different methods.

This module will cover the purposes and use of different methods for data collection in education research. It will address the design and use of questionnaires, different types of interviews and classroom observations. At the end of the module, students will have developed their skill in designing data collection instruments in connection to each of the three methods under focus and their critical understanding of the affordances and limitations of different methods.

This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation.

For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved?

The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.

This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation.

For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved?

The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.

This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation.

For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved?

The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.

This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation.

For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved?

The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.

Data is material. It is produced by people, it is made possible by resource extraction, it needs power to survive, it inhabits and resculpts the landscape. The use of data, then, contributes to climate catastrophe, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector.

Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of data and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented data environmentalism.

Data is material. It is produced by people, it is made possible by resource extraction, it needs power to survive, it inhabits and resculpts the landscape. The use of data, then, contributes to climate catastrophe, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector.

Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of data and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented data environmentalism.

Data is material. It is produced by people, it is made possible by resource extraction, it needs power to survive, it inhabits and resculpts the landscape. The use of data, then, contributes to climate catastrophe, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector.

Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of data and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented data environmentalism.