Data in Culture and Society

DIGI65522 Data in Culture and Society 15 Semester 2 Postgraduate Taught Level 6 Luca Scholz Unit coordinator Giulia Grisot Unit coordinator School of Arts, Languages and Cultures 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 It is difficult to overestimate the hold of data on the contemporary imagination. Data pervades our academic, political, aesthetic, economic, and popular discourse. To critique claims and decisions made with data, we need to understand where data comes from and how it was analysed and presented. In this course, students will learn to think about data in its social, cultural, and technical dimensions. Students will discuss the ethical dilemmas, societal impacts, and cultural implications surrounding data. They will probe the social and cultural forces that determine how data is collected, organised, and made available, as well as new forms of data activism and advocacy. At the same time, students will experiment first-hand how processes of data creation, “cleaning”, processing, and visualisation affect the way data is understood. The course allows students to develop their skills in creating, analysing, and interpreting data while considering what their data reveals, what it obscures, and the societal and cultural implications of both.Note that this course does not require any previous technical knowledge. It is difficult to overestimate the hold of data on the contemporary imagination. Data pervades our academic, political, aesthetic, economic, and popular discourse. To critique claims and decisions made with data, we need to understand where data comes from and how it was analysed and presented. In this course, students will learn to think about data in its social, cultural, and technical dimensions. Students will discuss the ethical dilemmas, societal impacts, and cultural implications surrounding data. They will probe the social and cultural forces that determine how data is collected, organised, and made available, as well as new forms of data activism and advocacy. At the same time, students will experiment first-hand how processes of data creation, “cleaning”, processing, and visualisation affect the way data is understood. The course allows students to develop their skills in creating, analysing, and interpreting data while considering what their data reveals, what it obscures, and the societal and cultural implications of both.Note that this course does not require any previous technical knowledge. <ul> <li style="text-align: justify; margin-left: 8px;">Gain an understanding of the social and cultural forces that determine how data is collected, organised, and made accessible or unavailable</li> <li style="text-align: justify; margin-left: 8px;">Embrace work with data as an academic and citizenship practice, learning to become an active citizen of data, rather than its passive subject</li> <li style="text-align: justify; margin-left: 8px;">Gain familiarity with different ways of extracting, processing, visualising, and questioning data in the humanities</li> <li style="text-align: justify; margin-left: 8px;">Help you to become more vigilant and reflective users of data and data-informed arguments</li> <li style="text-align: justify; margin-left: 8px;">Enhance your employability by allowing you to develop technical, critical, and creative skills needed to thrive in roles involving work with data</li></ul> On successful completion of the unit, it is expected that you will be able to:<ul><li>Understand and explain the role that data plays in a range of societal and cultural contexts</li><li>Evaluate humanities and social science research undertaken with different types of data analysis, including visualisation, text mining, and computer vision</li><li>Learn how data can be used to reframe individual problems as structural patterns  </li></ul> On successful completion of the unit, it is expected that you will be able to:<ul><li>Read, critically evaluate, and apply literature on data-driven scholarship in the humanities, social sciences and data studies</li><li>Discuss and assess arguments made with data in public and academic discussions</li><li>Critically reflect on how choices made in the creation, processing, and visualisation of data influence its interpretation</li><li>Develop a critical perspective on data-intensive scholarship and learn to identify misinterpretation, bias and oversimplification, and act on your criticism by learning to work with data yourself</li></ul> On successful completion of the unit, it is expected that you will be able to:<ul><li>Use some of the most important tools currently employed in the humanities and social science, and develop a deeper proficiency in at least one technology of your choice</li><li>Use digital tools to collect, analyse, and explore different types of data</li><li>Gather, clean, and synthesise data from a diverse range of sources</li></ul> On successful completion of the unit, it is expected that you will be able to:<ul><li>Acquire practical skills using a range of different digital applications</li><li>Present information and arguments orally, verbally, and visually with due regard to the target audience</li><li>Think creatively about how to develop and communicate your work</li></ul> Analytical skills This course enables you to critically read and evaluate data-driven arguments. You will learn to recognise biased, misleading, or oversimplifying forms of data analysis. Group/team working The course allows you to learn to collaborate in a team with diverse skills and potentially conflicting visions. Innovation/creativity The course allows you to formulate your own research questions, condense them into a manageable agenda, and answer them using new tools that allow you to develop and present your argument through visualization and narrative. Other By the end of this course students will be able to extract and process different types of data, generate their own visualisations, and understand key principles of data work, an increasingly essential skillset in a range of occupations. You will be able to present information and arguments orally, verbally and visually with due regard to the target audience This syllabus is indicativeData, Datafication and CitizenshipCounterdataMaking DataRepresenting DataCultural DataThe Natures of DataData JournalismOpen Data The course is taught through a weekly seminar, which combines introductory lectures, full class discussions, practical labs, and small group work. The course will take place in the Digital Humanities Lab and includes hands-on work with data. Students have access to two scheduled weekly consultation hours to meet individually with the course unit director to discuss their ideas and progress. All course material will be made available on Blackboard. All feedback will incorporate advice on improving future performance. 6 Project output (not diss/n) 80% 7 Oral assessment/presentation 20% <table class="Table" style="width:646px; margin-left:7px; border-collapse:collapse; border:none" width="646"> <tbody> <tr> <td>Feedback method</td> <td>Formative and/or Summative</td> </tr> <tr> <td>Detailed written feedback on written assignments, designed to include advice on improving future performance</td> <td>Summative</td> </tr> <tr> <td>15 minutes of the weekly seminars will be dedicated to discussing project progress and address issues as soon as they arise. Week 9 is entirely dedicated to project troubleshooting ahead of the final submission. Moreover, students are encouraged to seek formative feedback during seminars and in consultation hours.</td> <td>Formative</td> </tr> </tbody></table> N Indicative reading list: Bowker, Geoffrey C., and Susan Leigh Star. Sorting Things Out: Classification and Its Consequences. MIT Press, 2000.D’Ignazio, Catherine. Counting Feminicide: Data Feminism in Action. MIT Press, 2022.  Goldstein, Jenny, and Eric Nost. The Nature of Data: Infrastructures, Environments, Politics. Lincoln: University of Nebraska Press, 2022.Garrido, Gonzalo José López. “Radical Geography and Advocacy Mapping: The Case of the Detroit Geographical Expedition and Institute (1968–1972).” Journal of Planning History 20/4 (2021): 291–307.Guldi, Jo. “What Kind of Information Does the Era of Climate Change Require?” Climatic Change 169/1 (2021): 3 (https://doi.org/10.1007/s10584-021-03243-5).  Iliadis, Andrew, and Federica Russo. “Critical Data Studies: An Introduction.” Big Data & Society 3, no. 2 (2016)Loukissas, Yanni Alexander. All Data Are Local: Thinking Critically in a Data-Driven Society. Cambridge, MA: The MIT Press, 2019.McGrath, Laura, Richard Jean So, Ted Underwood, and Chad Wellmon. “Culture, Theory, Data: An Introduction.” New Literary History 53/4 (2023): 519– 30  Thorp, Jer. Living in Data: A Citizen’s Guide to a Better Information Future. Farrar, Straus and Giroux, 2021.Williams, Sarah. Data Action: Using Data for Public Good (MIT Press, 2020). Lectures 5 Seminars 10 0 285