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Data Literacies

Theory to Practice

Now that you’ve gained an understanding of some of the considerations around data and ethics, let’s think a bit further about how you may apply some of what we have discussed in your work.  Below you will find some additional readings that dives deeper into some of the topics that were covered in our lessons. If you would like, you can also consider exploring the “Projects or Challenges to Try” to see how you might apply what you’ve learnt.

Review your knowledge: 6 questions from the lessons

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Descriptive analysis help us summarize a data set.

(Select one of the following)

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Structured data can be:

(Select all that apply)

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Research data can be defined as:

(Select all that apply)

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Tiny data format only allows one value per cell.

(Select one of the following)

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Measurements are accurate when

(Select one of the following)

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The stages of data is a single iteration process, i.e. there is a fixed stage progression from data collection to visualization.

(Select one of the following)

Deepen your knowledge

Discussion Questions


  • How does increased data literacy add to your project planning?

  • How do you address your use of data and your ethics? For example,how might ethics play a part in the way you think about (a) data collection? (b) anonymity and confidentiality? (c) data and its relation to the communities it emerges from?

  • Consider your next project, what are some considerations from this workshop that you might bring into your project?

Tutorials

DHRI workshops provide a foundational comprehension of digital skills and tools often used in digital humanities and humanistic social science research. While having a strong foundation is important, there is usually more to learn if you want to get started on your own. Now that you have an awareness of the basics, here are some other tutorials to try that will extend your learning.


Computational social science with R

Computational social science with R is a 2-week summer institute program that follows the Bit By Bit: Social Research in Digital Age format. The current repository (updated: Jul, 2020) contains the institute’s workshops and materials.

The European Data Portal’s tutorial on Open Data

The European Data Portal’s tutorial on Open Data offers a guided insight to the importance of choosing the right format for open datasets.

The Data Visualisation Catalogue

The Data Visualisation Catalogue by Severino Ribecca provides a guide to data visualizations for different types of data and narratives.

From Data to Viz

From Data to Viz by From Data to Viz also provides a guide to data visualization for different types of data and narratives.

Further Readings

After they complete a workshop, participants often ask: what next? Here are some additional resources that can help you think about the projects that could be developed, the resources that you might need, the ways this skill could be used in the classroom, or debates in the field of digital humanities that provide context to what you have just learned.


Marieke Guy’s data management presentation

(Data management) Marieke Guy’s data management presentation discusses some ideas around planning for data management before, during, and after a project.

Queensland University of Technology’s Management of Research Data

(Data management) Queensland University of Technology’s Management of Research Data provides some ideas around ownership, roles and responsibilities of data-driven projects. While this is specific to Queensland University of Technology, it is useful for understanding some of the different roles in a research project.

The Graduate Center, CUNY’s Data Management

(Data management) The Graduate Center, CUNY’s Data Management research guide provides resources and specific steps for CUNY faculty, staff, and students.- (Ethics and (“big” data) research) The Council for Big Data, Ethics, and Society’s Perspectives on Big Data, Ethics, and Society is a white paper that consolidates the council’s discussions on big data, ethics, and society.

Catherine D’Ignazio & Lauren F. Klein’s Data Feminism

(Ethics and (“big” data) research) Catherine D’Ignazio & Lauren F. Klein’s Data Feminism (scroll down the page to access the book chapters for free). It looks at “big” data from a feminist perspective, and discuss the importance of understanding long histories and socio-political contexts in research, as well as providing an overview of the field.

Feminist Data’s Manifest-No

(Ethics and (“big” data) research) Feminist Data’s Manifest-No discusses the realities of “big” data and the fallacies of unequal harm and risk distribution, particularly towards marginalized communities.

Mimi Onuoha’s Missing Data Sets

(Ethics and (“big” data) research) Mimi Onuoha’s Missing Data Sets looks at “blank spots that exist in spaces that are otherwise data-saturated,” that usually affect those who are the most vulnerable.

Projects or Challenges to Try


2018 European Social Survey

Consider a project where you are interested in the trend of Euro-American political views. You’ve decided to look at the 2018 European Social Survey and the U.S.-based 2018 General Social Survey. How would you approach the data? If you’re interested in reporting on the trend of global political views, what do you have to consider when you join these data sets? What assumptions do you have to make? How would you collapse responses?