<?xml version="1.0" encoding="UTF-8"?>
<CourseUnit xmlns="http://www.manchester.ac.uk/CUICourseUnitDetails" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.manchester.ac.uk/CUICourseUnitDetails.xsd">
  <UnitCode Applicant="Y" Label="Unit code" Student="Y">
    <Code>DIGI10082</Code>
  </UnitCode>
  <UnitTitle Applicant="Y" Label="Unit title" Student="Y">
    <Title>Histories of Data</Title>
  </UnitTitle>
  <MaxUnits Applicant="Y" Label="Credit rating" Student="Y">
    <Units>20</Units>
  </MaxUnits>
  <TeachingPeriods Applicant="Y" Label="Teaching period(s)" Student="Y">
    <Period>Semester 2</Period>
  </TeachingPeriods>
  <AcademicCareer Applicant="Y" Label="Academic career" Student="Y">
    <Value>Undergraduate</Value>
  </AcademicCareer>
  <UnitLevel Applicant="Y" Label="Unit level" Student="Y">
    <Level>Level 1</Level>
  </UnitLevel>
  <StaffList Applicant="Y" Label="Teaching staff" RoleLabel="Course Unit Role" Student="Y">
    <StaffMember>
      <Name>Luca Scholz</Name>
      <Role>Unit coordinator</Role>
    </StaffMember>
  </StaffList>
  <OfferedBy Applicant="Y" Label="Offered by" Student="Y">
    <OrganisationList>
      <Organisation>
        <OrgName></OrgName>
      </Organisation>
    </OrganisationList>
    <GroupList>
      <Group>
        <GroupName></GroupName>
      </Group>
    </GroupList>
    <FheqLevels>
      <FheqLevel>
        <LevelNumber>1</LevelNumber>
        <LevelName>FHEQ level (Framework for Higher Education Qualifications) ' First part HE study/Bachelors ' </LevelName>
      </FheqLevel>
    </FheqLevels>
    <Ects>
      <MaxUnits>European Credit Transfer &amp; Accumulation System Rating :   10.0</MaxUnits>
    </Ects>
  </OfferedBy>
  <MarketingOverview Applicant="Y" Label="Marketing Course unit overview" Student="">
    <Content>&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Contemporary debates around big data are often infused with a sense of novelty. While much is indeed new about how corporations, governments, and scholars use information today, the ways in which they create, order, visualise, and analyse data emerged over centuries. This course places contemporary enthusiasm and apprehension around big data and the technologies devised to make it legible – from diagrams to artificial intelligence – in a long historical perspective. Approaching data as a historical category and as a source, we will find that our present age is hardly the first in which human societies have agonized over the problem of information overload. By examining past and present forms and uses of data – including knots, punch cards, and language models – we will develop a deeper understanding of how data, and its associated practices, have been established and confronted in different historical and geographic settings.&lt;/span&gt;&lt;/p&gt;</Content>
  </MarketingOverview>
  <UnitOverview Applicant="" Label="Course unit overview" Student="Y">
    <Content>&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Contemporary debates around big data are often infused with a sense of novelty. While much is indeed new about how corporations, governments, and scholars use information today, the ways in which they create, order, visualise, and analyse data emerged over centuries. This course places contemporary enthusiasm and apprehension around big data and the technologies devised to make it legible – from diagrams to artificial intelligence – in a long historical perspective. Approaching data as a historical category and as a source, we will find that our present age is hardly the first in which human societies have agonized over the problem of information overload. By examining past and present forms and uses of data – including knots, punch cards, and language models – we will develop a deeper understanding of how data, and its associated practices, have been established and confronted in different historical and geographic settings.&lt;/span&gt;&lt;/p&gt;</Content>
  </UnitOverview>
  <Aims Applicant="Y" Label="Aims" Student="Y">
    <Content>&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;The unit aims to:&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Place contemporary enthusiasm and apprehension around big data and datafication in historical perspective&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Equip students with strong conceptual and methodological foundations for interacting with data and thinking about its history and biases&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Support students in examining the relationships between current and historic data practices in a variety of settings&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;</Content>
  </Aims>
  <LearningOutcomes Applicant="Y" Label="Learning outcomes" Student="Y">
    <Content>&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Upon completion of this course, students will be expected to have developed a range of transferable skills. Lectures, readings, and seminar discussions will allow students to develop their critical thinking skills and digital literacy and apply those to a wide range of examples. The first assignment will enable students to navigate a complex historical record, identify relevant material, and offer a succinct and precise discussion. The second assignment will enable students to conceive and pursue a small independent research project and enhance their work through peer feedback. Throughout, students will develop a situated, in-depth understanding of data, datafication, and their associated practices.&lt;/span&gt;&lt;/p&gt;</Content>
  </LearningOutcomes>
  <Knowledge Applicant="Y" Label="Knowledge and understanding" Student="Y">
    <Content>&lt;ul&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Place contemporary debates and practices of data and datafication in a historical perspective.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Identity the relationships between current and historic data practices in a variety of contexts.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Demonstrate a robust academic vocabulary for interacting with and questioning data.&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;</Content>
  </Knowledge>
  <IntellectualSkills Applicant="Y" Label="Intellectual skills" Student="Y">
    <Content>&lt;ul&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Situate and evaluate historical factors that have determined how data has been collected, organised, and made accessible or unavailable.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Examine and critique academic claims and arguments relating to the histories of data and datafication.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Identify, describe, and critically evaluate artifacts such as datasets, data visualisations, and related media.&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;</Content>
  </IntellectualSkills>
  <PracticalSkills Applicant="Y" Label="Practical skills" Student="Y">
    <Content>&lt;ul&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Identify, retrieve, and analyse different types of historical data and secondary readings.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Compare and contrast a range of methods and tools used to interrogate and analyse data in historical inquiry.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Analyse and recast a complex type of evidence in terms that peers and non-experts can understand.&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;</Content>
  </PracticalSkills>
  <TransferableSkills Applicant="Y" Label="Transferable skills and personal qualities" Student="Y">
    <Content>&lt;ul&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Think creatively about how to develop and communicate their work.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Reflect on and act upon peer and instructor feedback.&amp;nbsp;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Summarise and present information and arguments with due regard to the target audience.&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;</Content>
  </TransferableSkills>
  <EmployabilitySkillsList Applicant="Y" Label="Employability skills" Student="Y">
    <Skill>
      <SkillId></SkillId>
      <SkillDescription></SkillDescription>
    </Skill>
  </EmployabilitySkillsList>
  <Syllabus Applicant="Y" Label="Syllabus" Student="Y">
    <Content>&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;The course will open in our present historical moment of heightened awareness of big data and move backwards in time to trace the histories of data. Lectures and seminars are structured chronologically and thematically and will cover key questions and turning points in the history of data and digital technology. Themes covered in the course include such topics as shifting understandings and uses of personal data, the role of data for the study of climate change, the evolution of computing, the history of information visualisation, and constructions and erasures of race and sexuality.&lt;/span&gt;&lt;/p&gt;</Content>
  </Syllabus>
  <TeachingMethods Applicant="Y" Label="Teaching and learning methods" Student="Y">
    <Content>&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;The unit consists of 1h-long lectures and 2h-long seminars a week, delivered in person.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Lectures will combine oral presentations with presentation slides, audio and video material, as well as interactive elements.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Seminars will give students an opportunity to critically discuss the readings and lectures and involve them in a series of practical and group activities, including but not limited to debates, tutorials, and peer review.&lt;/span&gt;&lt;/p&gt;</Content>
  </TeachingMethods>
  <AssessmentMethods Applicant="Y" Label="Assessment methods" Student="Y">
    <IntroText> </IntroText>
    <Method>
      <MethodId>0</MethodId>
      <MethodName>Other</MethodName>
      <MethodWeight>100%</MethodWeight>
    </Method>
    <OtherDescription>&lt;figure class="table"&gt;&lt;table&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Formative Assessment Task&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Length (word count/time)&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Peer review of essay ideas&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;10 minutes&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/figure&gt;&lt;figure class="table"&gt;&lt;table&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Summative Assessment Task&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Length&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Weighting within unit (if relevant)&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Individual description of a historical instance of data&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;750 words&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;30%&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Individual essay about data in history or in contemporary life&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;2,000 words&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;70%&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/figure&gt;</OtherDescription>
  </AssessmentMethods>
  <FeedbackMethods Applicant="Y" Label="Feedback methods" Student="Y">
    <Content>&lt;figure class="table"&gt;&lt;table&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Formative Assessment Task&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;How and when feedback is provided&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Peer review of essay ideas&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Students will comment on each other’s essay ideas in small groups, supervised, and with additional feedback by the seminar leader(s)&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/figure&gt;&lt;figure class="table"&gt;&lt;table&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;Summative Assessment Task&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;&lt;strong&gt;How and when feedback is provided&lt;/strong&gt;&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Individual description of a historical instance of data&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Via Turnitin within 15 working days&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Individual essay about data in history or in contemporary life&lt;/span&gt;&lt;/td&gt;&lt;td&gt;&lt;span style="font-family:Arial, Helvetica, sans-serif;"&gt;Via Turnitin within 15 working days&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/figure&gt;</Content>
  </FeedbackMethods>
  <RequirementsList Applicant="Y" Label="Pre/co-requisites" Student="Y">
    <Requirement>
      <UnitCode></UnitCode>
      <UnitTitle></UnitTitle>
      <RequirementType></RequirementType>
      <Description></Description>
    </Requirement>
    <AdditionalRequirement></AdditionalRequirement>
  </RequirementsList>
  <AcademicPrograms Applicant="Y" Label="Academic programmes" Student="Y">
    <AcademicProgram>
      <Program></Program>
      <Plan></Plan>
      <Level></Level>
      <Requirement></Requirement>
    </AcademicProgram>
  </AcademicPrograms>
  <FreeChoice Applicant="Y" Label="Available as a free choice unit?" Student="Y">
    <Content>Y</Content>
  </FreeChoice>
  <Accreditation Applicant="Y" Label="Accreditation" Student="Y">
    <Content></Content>
  </Accreditation>
  <RecommendedReading Applicant="Y" Label="Recommended reading" Student="Y">
    <Content>&lt;p&gt;&lt;span class="text-small" style="font-family:Arial, Helvetica, sans-serif;"&gt;Blair, Ann, Paul Duguid, Anja-Silvia Goeing and Anthony Grafton (eds). Information: A Historical Companion, Princeton: Princeton University Press, 2021.&lt;/span&gt;&lt;span class="text-small"&gt;&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span class="text-small" style="font-family:Arial, Helvetica, sans-serif;"&gt;Bouk, Dan. “The History and Political Economy of Personal Data over the Last Two Centuries in Three Acts.” Osiris 32, no. 1 (2017): 85–106.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span class="text-small" style="font-family:Arial, Helvetica, sans-serif;"&gt;Edwards, Paul N. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press, 2010.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span class="text-small" style="font-family:Arial, Helvetica, sans-serif;"&gt;Halpern, Orit. Beautiful Data: A History of Vision and Reason since 1945. Durham, NC: Duke University Press, 2015.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span class="text-small" style="font-family:Arial, Helvetica, sans-serif;"&gt;Igo, Sarah E. “Me and My Data.” Historical Studies in the Natural Sciences 48, no. 5 (2018): 616–26.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span class="text-small" style="font-family:Arial, Helvetica, sans-serif;"&gt;Jones, Matthew L. “How We Became Instrumentalists (Again): Data Positivism since World War II.” Historical Studies in the Natural Sciences 48, no. 5 (2018): 673–84.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;</Content>
  </RecommendedReading>
  <StudyHours Applicant="Y" Label="Study hours" Student="Y">
    <IntroText> </IntroText>
    <ScheduledHours Applicant="Y" Label="Scheduled activity hours" Student="Y">
      <ActivityHours>
        <ActivityType>Lectures</ActivityType>
        <Hours>11</Hours>
      </ActivityHours>
      <ActivityHours>
        <ActivityType>Seminars</ActivityType>
        <Hours>22</Hours>
      </ActivityHours>
    </ScheduledHours>
    <PlacementHours Applicant="Y" Label="Placement hours" Student="Y">
      <ActivityHours>
        <ActivityType></ActivityType>
        <Hours>0</Hours>
      </ActivityHours>
    </PlacementHours>
    <TotalHours Applicant="Y" Label="Independent study hours" Student="Y">
      <Hours>167</Hours>
    </TotalHours>
  </StudyHours>
  <Notes Applicant="Y" Label="Additional notes" Student="Y">
    <Content></Content>
  </Notes>
</CourseUnit>
