Classifying Household Goods

This activity, which focuses on the practices of categorization and classification of data, is designed to help students think critically about how and why we describe, organize, and categorize information in certain ways.

Data Biography

In this assignment, students apply the concept of a “data biography” to analyze the history behind a particular dataset: the who, what, when, how, and why of the dataset and its creation. In doing so, they learn about the different interpretative filters that shape the historical trajectory of a dataset, from its initial collection to its availability and usability today.

Dear Data

This assignment, which is inspired by Giorgia Lupi and Stefanie Posavec’s project Dear Data, challenges students to collect data from their daily lives and to reflect critically on the data collection practice.

Classifying Household Goods

This activity, which focuses on the practices of categorization and classification of data, is designed to help students think critically about how and why we describe, organize, and categorize information in certain ways.

Data Cleaning

In this brief article, Alice Macfarlan describes the motivation behind careful data preparation and outlines a set of steps and questions to ask oneself when preparing data. Macfarlan also provides links to more information about the process of and motivation for cleaning data.

Strangers in the Dataset

This section from chapter five of D’Ignazio and Klein’s Data Feminism takes a critical look at the metaphor of “cleaning” data, assesses the implications of thinking about data in this way, and challenges readers to think more deeply about the assumptions that guide data the data gathering and preparation process.

Attending to the Cultures of Data Science Work

This essay argues that data science communities have a responsibility to attend not only to the cultures that orient the work of domain communities, but also to the cultures that orient their own work. The author also describes how ethnographic frameworks such as thick description can be enlisted to encourage more reflexive data science work.

Critically Analyzing the World Happiness Report

This activity invites students to think about the variables included in the World Happiness Report dataset, about the relations between variables, and about the advantages and disadvantages of the authors’ approach to measuring happiness. This exercise is designed to help cultivate habits of critical reflection and to provide practice in data analysis, including reflection on correlation.

Exploring Data

This assignment challenges students to examine, explore, and think critically about a dataset. In crtically analyzing a subset of the 2019 American Community Survey performed by the United States Census Bureau, students come to learn how counting the US population is inherently messy, and implicitly (and sometimes explicitly) caught up in questions of power.

Data Stewardship

Data stewardship can be understood as the “ ‘long-term care’ of valuable digital assets, with the goal that they should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data” (Wilkinson et al).

The CARE Principles for Indigenous Data Governance

This scholarly article describes the CARE principles for Indigenous data management and stewardship that have been built around the concept of data sovereignty and designed to complement the existing FAIR principles. Readers are challenged to think about what researchers owe to communities (particularly indigenous communities) who helped to create the data that researchers collect and publish.

The Fair Guiding Principles For Scientific Data Management And Stewardship

This article identifies practical guidelines for data management and data stewardship so that data can be published and preserved in ways that ensure transparency, reproducibility, and reusability.